IEEE 2016 / 15 – MATLAB Project List

IEEE: 2016 Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images
IEEE Transaction on Medical Imaging
Abstract : Among brain tumors, gliomas are the most common and aggressive, leading to a very short life expectancy in their highest grade. Thus, treatment planning is a key stage to improve the quality of life of oncological patients. Magnetic resonance imaging (MRI) is a widely used imaging technique to assess these tumors, but the large amount of data produced by MRI prevents manual segmentation in a reasonable time, limiting theuse of precise quantitative measurements in the clinical practice. So, automatic and reliable segmentation methods are required; however, the large spatial and structural variability among brain tumors make automatic segmentation a challenging problem. In this paper, we propose an automatic segmentation method based on Convolutional Neural Networks (CNN), exploring small 3 3 kernels. The use of small kernels allows designing a deeper architecture, besides having a positive effect against overfitting, given the fewer number of weights in the network. We also investigated the use of intensity normalization as a pre-processing step, which though not common in CNN-based segmentation methods, proved together with data augmentation to be very effective for brain tumor segmentation in MRI images. Our proposal was validated in the Brain Tumor Segmentation Challenge 2013 database (BRATS 2013), obtaining simultaneously the first position for the complete, core, and enhancing regions in Dice Similarity Coefficient metric (0.88, 0.83, 0.77) for the Challenge data set. Also, it obtained the overall first position by the online evaluation platform. We alsoparticipated in the on-site BRATS 2015 Challenge using the same model, obtaining the second place, with Dice Similarity Coefficient metric of 0.78, 0.65, and 0.75 for the complete, core, and enhancing regions, respectively.


IEEE: 2016 Robust Blur Kernel Estimation for License Plate Images From Fast Moving Vehicles
IEEE Transaction on Vehicular Technology
Abstarct :- As the unique identification of a vehicle, license plate is a key clue to uncover over-speed vehicles or the ones involved in hit-and-run accidents. However, the snapshot of overspeed vehicle captured by surveillance camera is frequently blurred due to fast motion, which is even unrecognizable by human. Those observed plate images are usually in low resolution and suffer severe loss of edge information, which cast great challenge to existing blind deblurring methods. For license plate image blurring caused by fast motion, the blur kernel can be viewed as linear uniform convolution and parametrically modeled with angle and length. In this paper, we propose a novel scheme based on sparse representation to identify the blur kernel. By analyzing the sparse representation coefficients of the recovered image, we determine the angle of the kernel based on the observation that the recovered image has the most sparse representation when the kernel angle corresponds to the genuine motion angle. Then, we estimate the length of the motion kernel with Radon transform in Fourier domain. Our scheme can well handle large motion blur even when the license plate is unrecognizable by human. We evaluate our approach on real-world images and compare with several popular state-of-the-art blind image deblurring algorithms. Experimental results demonstrate the superiority of our proposed approach in terms of effectiveness and robustness.


IEEE: 2016 On the Shift Value Set of Cyclic Shifted Sequences for PAPR Reduction in OFDM Systems
IEEE Transaction on Broadcasting
Abstarct :- Orthogonal frequency division multiplexing (OFDM) signals have high peak-to-average power ratio (PAPR), which causes distortion when OFDM signal passes through a nonlinear high power amplifier. A partial transmit sequence (PTS) scheme is one of the typical PAPR reduction methods. A cyclic shifted sequences (CSSs) scheme is evolved from the PTS scheme to improve the PAPR reduction performance, where OFDM signal subsequences are cyclically shifted and combined to generate alternative OFDM signal sequences. The shift value (SV) sets in the CSS scheme should be carefully selected because those are closely related to the PAPR reduction performance of the CSS scheme. In this letter, we propose some criteria to select the good SV sets and verify its validness through simulations.


IEEE: 2016 Visual Place Recognition: A Survey
IEEE Transaction on Robotics
Abstarct :- Visual place recognition is a challenging problem due to the vast range of ways in which the appearance of real-world places can vary. In recent years, improvements in visual sensing capabilities, an ever-increasing focus on long-term mobile robot autonomy, and the ability to draw on state-of-the-art research in other disciplines—particularly recognition in computer vision and animal navigation in neuroscience—have all contributed to significant advances in visual place recognition systems. This paper presents a survey of the visual place recognition research landscape.We start by introducing the concepts behind place recognition—the role of place recognition in the animal kingdom, howa “place” is defined in a robotics context, and themajor components of a place recognition system. Long-term robot operations have revealed that changing appearance can be a significant factor in visual place recognition failure; therefore, we discuss how place recognition solutions can implicitly or explicitly account for appearance change within the environment. Finally, we close with a discussion on the future of visual place recognition, in particular with respect to the rapid advances being made in the related fields of deep learning, semantic scene understanding, and video description.


IEEE: 2016 Progressive Lossy-to-Lossless Compression of DNA Microarray Images
IEEE Transaction on Signal Processing Letters
Abstarct :- The analysis techniques applied to DNA microarray images are under active development. As new techniques become available, it will be useful to apply them to existing microarray images to obtain more accurate results. The compression of these images can be a useful tool to alleviate the costs associated to their storage and transmission. The recently proposed Relative Quantizer (RQ) coder provides the most competitive lossy compression ratios while introducing only acceptable changes in the images. However, images compressed with the RQ coder can only be reconstructed with a limited quality, determined before compression. In this work, a progressive lossy-to-lossless scheme is presented to solve this problem. Firstly, the regular structure of the RQ intervals is exploited to define a lossy-tolossless coding algorithm called the Progressive RQ (PRQ) coder. Secondly, an enhanced version that prioritizes a region of interest, called the PRQ-ROI coder, is described. Experiments indicate that the PRQ coder offers progressivity with lossless and lossy coding performance almost identical to the best techniques in the literature, none of which is progressive. In turn, the PRQROI exhibits very similar lossless coding results with better ratedistortion performance than both the RQ and PRQ coders.



IEEE: 2016 Improved Bag of Feature for Automatic Polyp Detection in Wireless Capsule Endoscopy Images
IEEE Transaction on Automation Science & Engineering
Abstarct :- Wireless capsule endoscopy (WCE) needs computerized method to reduce the review time for its large image data. In this paper, we propose an improved bag of feature (BoF) method to assist classification of polyps in WCE images. Instead of utilizing a single scale-invariant feature transform (SIFT) feature in the traditional BoF method, we extract different textural features from the neighborhoods of the key points and integrate them together as synthetic descriptors to carry out classification tasks. Specifically, we study influence of the number of visual words, the patch size and different classification methods in terms of classification performance. Comprehensive experimental results reveal that the best classification performance is obtained with the integrated feature strategy using the SIFT and the complete local binary pattern (CLBP) feature, the visual words with a length of 120, the patch size of 8*8, and the support vector machine (SVM). The achieved classification accuracy reaches 93.2%, confirming that the proposed scheme is promising for classification of polyps in WCE images.



IEEE: 2015 Text-Independent Phoneme Segmentation Combining EGG and Speech Data
IEEE Transaction on Audio, Speech, Language processing
Abstract:- new approach for text-independent phoneme segmentation at sampling point level is proposed in this paper. The algorithm consists of two phases: First, the voiced sections in speech data are detected using the information of vocal folds vibration contained in electroglottograph (EGG). A Hilbert envelopefeature is adopted to achieve sampling point level detection accuracy. Second, the voiced sections and other sections are treated separately. Each voiced section is divided into several candidate phonemes using the Viterbi algorithm. Then adjacent candidate phonemes are merged based on a Hotellings T-square test method. For other sections, the unvoiced consonants are detected from silence based on a singularity exponent feature. Comparison experiments show that the proposed method has better performance than the existing ones for a variety of tolerances, and is more robust to noise.



IEEE: 2015 Joint Beamforming, Power and Channel Allocation in Multi-User and Multi-Channel Underlay MISO Cognitive Radio Networks
IEEE Transaction on Vehicular Technology
Abstarct :-In this paper, we consider a joint beamforming, power, and channel allocation in a multi-user and multi-channel underlay multiple input single output (MISO) cognitive radio network (CRN). In this system, primary users’ (PUs’) spectrum can be reused by the secondary user transmitters (SUTXs) to maximize the spectrum utilization while the intra-user interference is minimized by implementing beamforming at each SU-TX. After formulating the joint optimization problem as a non-convex, mixed integer nonlinear programming (MINLP) problem, we propose a solution which consists of two stages. In the first stage, a feasible solution for power allocation and beamforming vectors is derived under a given channel allocation by converting the original problem into a convex form with an introduced optimal auxiliary variable and semidefinite relaxation (SDR) approach. After that, in the second stage, two explicit searching algorithms, i.e., genetic algorithm (GA) and simulated annealing (SA)-based algorithm, are proposed to determine suboptimal channel allocations. Simulation results show that beamforming, power and channel allocation with SA (BPCA-SA) algorithm can achieve close-to-optimal sum-rate while having a lower computational complexity compared with beamforming, power and channel allocation with GA (BPCA-GA) algorithm. Furthermore, our proposed allocation scheme has significant improvement in achievable sum-rate.



IEEE: 2015 Pilot Design Schemes for Sparse Channel Estimation in OFDM Systems
IEEE Transaction on Vehicular Technology
Abstarct :-In this paper, we consider the pilot design based on the mutual incoherence property (MIP) for sparse channel estimation in orthogonal frequency-division multiplexing (OFDM) systems. With respect to the length of channel impulse response (CIR), we first derive a sufficient condition for the optimal pilot pattern generated from the cyclic different set (CDS). Since the CDS does not exist for most practical OFDM systems, we propose three pilot design schemes to obtain a near-optimal pilot pattern. The first two schemes, including stochastic sequential search (SSS) and stochastic parallel search (SPS), are based on the stochastic search. The third scheme called iterative group shrinkage (IGS) employs a tree-based searching structure and removes rows in a group instead of removing a single row at each step. We later extend our work to multiple-input–multiple-output (MIMO) systems and propose two schemes, i.e., sequential design scheme and joint design scheme. We also combine them to design the multiple orthogonal pilot patterns, i.e., using the sequential scheme for the first several transmit antennas and using the joint scheme to design the pilot pattern for the remaining transmit antennas.Simulation results show that the proposed SSS, SPS, and IGS converge much faster than the cross-entropy optimization and the exhaustive search and are thus more efficient. Moreover, SSS and SPS outperform IGS in terms of channel estimation performance.


RJASET: 2014  Enhanced Color Filter Array Interpolation Using Fuzzy Genetic Algorithm
 Abstarct :- Covering sensor surface with a Color Filter Array (CFA) and enabling a sensor pixel sample only one of three primary color values, is how single sensor digital cameras capture imagery. An interpolation process, called CFA demosaicking estimates other two missing color values at every pixel to render a full color image. This study presents two contributions to CFA demosaicking: a new and improved CFA demosaicking method to ensure high quality color images and new image measures to quantify demosaicking performance. Though digital cameras are now more powerful and smaller, Charge-Coupled Device (CCD) sensors continue to associate only one color to a pixel. Called Bayer Pattern this color mosaic is processed to get a high resolution color image. Every interpolated image pixel includes a full surrounding pixels colors based color spectrum. This study uses an edge indicator function and edge directions are considered in the suggested interpolation method to avoid high frequency region artifacts and improve performance.


A Rain Pixel Recovery Algorithm for Videos With Highly Dynamic Scenes 
IEEE 2014:Transactions on Image Processing

Abstract—Rain removal is a very useful and important technique in applications such as security surveillance and movie editing. Several rain removal algorithms have been proposed these years, where photo metric, chromatic, and probabilistic properties of the rain have been exploited to detect and remove the rainy effect. Current methods generally work well with light rain and relatively static scenes, when dealing with heavier rainfall in dynamic scenes, these methods give very poor visual results. The proposed algorithm is based on motion segmentation of dynamic scene. After applying photo metric and chromatic constraints for rain detection, rain removal filters are applied on pixels such that their dynamic property as well as motion occlusion clue are considered; both spatial and temporal information’s are then adaptively exploited during rain pixel recovery. Results show that the proposed algorithm has a much better performance for rainy scenes with large motion than existing algorithms.


Automatic Estimation of Multiple Motion Fields From Video Sequences Using a Region Matching Based Approach
IEEE 2014: Transactions on Multimedia
Abstract—Estimation of velocity fields from a video sequence is an important step towards activity classification in a surveillance system. It has been recently shown that multiple motion fields estimated from trajectories are an efficient tool to describe the movement of objects, allowing an automatic classification of activities in the scene. However, the trajectory detection in noisy environments is difficult, usually requiring some sort manual editing to complete or correct them. This paper proposes two novel contributions. First, an automatic method for building pedestrian trajectories in far-field surveillance scenarios is presented not requiring user intervention. This basically comprises the detection of multiple moving objects in a video sequence through the   detection of the active regions, followed by the estimation of the velocity fields that is accomplished by performing region matching of the above regions at consecutive time instants. This leads to a sequence of centroids and corresponding velocity vectors, describing the local motions presented in the image. A motion correspondence algorithm is then applied to group the centroids in a  contiguous sequence of frames into trajectories corresponding to each moving object. The second contribution is a method for automatically finding the trajectories from a library of previously computed ones. Experiments on extensive video sequences from university campuses show that motion fields can be reliably estimated from these automatically detected trajectories, leading to a fully automatic procedure for the estimation of multiple motion fields.



Radial Basis Function Based Neural Network for Motion Detection in Dynamic Scenes
IEEE 2014 :Transactions on Cybernetics
Abstract—Motion detection, the process which segments moving objects in video streams, is the first critical process and plays an important role in video surveillance systems. Dynamic scenes are commonly encountered in both indoor and outdoor situations and contain objects such as swaying trees, spouting fountains, rippling water, moving curtains, and so on. However, complete and accurate motion detection in dynamic scenes is often a challenging task. This paper presents a novel motion detection approach based on radial basis function artificial neural networks to accurately detect moving objects not only in dynamic scenes but also in static scenes. The proposed method involves two important modules: a multibackground generation module and a moving object detection module. The multibackground generation module effectively generates a flexible probabilistic model through an unsupervised learning process to fulfill the property of either dynamic background or static background. Next, the moving object detection module achieves complete and accurate detection of moving objects by only processing blocks that are highly likely to contain moving objects. This is accomplished by two procedures: the block alarm procedure and the object extraction procedure. The detection results of our method were evaluated by qualitative and quantitative comparisons with other state-of-the-art methods based on a wide range of natural video sequences. The overall results show that the proposed method substantially outperforms existing methods with Similarity and F1 accuracy rates of 69.37% and 65.50%, respectively



A Relative-Discriminating-Histogram-of-Oriented-Gradients-Based Particle Filter Approach to Vehicle Occlusion Handling and Tracking
IEEE 2014: Transactions on Industrial Electronics
Abstract—This paper presents a relative discriminative histogram of oriented gradients (HOG) (RDHOG)-based particle filter (RDHOGPF) approach to traffic surveillance with occlusion handling. Based on the conventional HOG, an  xtension known as RDHOG is proposed, which enhances the descriptive ability of the central block and the surrounding blocks. RDHOGPF can be used to predict and update the positions of vehicles in continuous video sequences. RDHOG was integrated with the particle filter framework in order to improve the tracking robustness and accuracy. To resolve multi object tracking problems, a partial occlusion handling approach is addressed, based on the reduction of the particle weights within the occluded region. Using the proposed procedure, the predicted trajectory is closer to that of the real rigid body. The proposed RDHOGPF can determine the target by using the feature descriptor correctly, and it overcomes the drift problem by updating in low-contrast and very bright situations. An empirical evaluation is performed inside a tunnel and on a real road. The test videos include low viewing angles in the tunnel, low-contrast and bright  situations, and partial and full occlusions. The experimental results demonstrate that the detection ratio and precision of RDHOGPF both exceed 90%.



Automatic Moving Object Extraction Through a Real-World Variable-Bandwidth Network for Traffic Monitoring Systems
IEEE 2014 : Transactions on Industrial Electronics
Abstract—Automated motion detection has become an increasingly important subject in traffic surveillance systems. Video communication in traffic surveillance systems may experience network congestion or unstable bandwidth over real-world networks with limited bandwidth, which is harmful in regard to motion detection in video streams of variable bit rate. In this paper, we propose a unique Fisher’s linear discriminant-based radial basis function network motion detection approach for accurate and complete detection of moving objects in video streams of both high and low bit rates. The proposed approach is accomplished through a combination of two stages: adaptive pattern generation (APG) and moving object extraction (MOE). For the APG stage, the variable bit-rate video stream properties are accommodated by the proposed approach, which subsequently distinguishes the moving objects within the regions belonging to the moving object class by using two devised procedures during the MOE stage. Qualitative and quantitative detection accuracy evaluations show that the proposed approach exhibits superior efficacy when compared to previous methods. For  example, accuracy rates produced by F1 and Similarity metrics for the proposed approach were, respectively, up to 92.23% and 88.24% higher than those produced for other previous methods.



A Distributed Canny Edge Detector: Algorithm and FPGA Implementation
IEEE 2014 :Transactions on Digital Signal Processing
Abstract—The Canny edge detector is one of the most widely used edge detection algorithms due to its superior performance. Unfortunately, not only is it computationally more intensive as compared with other edge detection algorithms, but it also has a higher latency because it is based on frame-level statistics. In this paper, we propose a mechanism to implement the Canny algorithm at the block level without any loss in edge detection performance compared with the original frame-level Canny algorithm. Directly applying the original Canny algorithm at the block-level leads to excessive edges in smooth regions and to loss of significant edges in high-detailed regions since the original Canny computes the high and low thresholds based on the frame-level statistics. To solve this problem, we present a distributed Canny edge detection algorithm that adaptively computes the edge detection thresholds based on the block type and the local distribution of the gradients in the image block. In addition, the new algorithm uses a nonuniform gradient magnitude histogram to compute block-based hysteresis thresholds. The resulting block-based algorithm has a significantly reduced latency and can be easily integrated with other block-based image codecs. It is capable of supporting fast edge detection of images and videos with high resolutions, including full-HD since the latency is now a function of the block size instead of the frame size. In addition, quantitative conformance evaluations and subjective tests show that the edge detection performance of the proposed algorithm is better than the original frame-based algorithm, especially when noise is present in the images. Finally, this algorithm is implemented using a 32 computing engine architecture and is synthesized on the Xilinx Virtex-5 FPGA. The synthesized architecture takes only 0.721 ms (including the SRAM READ/WRITE time and the computation time) to detect edges of 512 × 512 images in the USC SIPI database when clocked at 100 MHz and is faster than existing FPGA and GPU implementations.


A Novel Local Pattern Descriptor—Local Vector Pattern in High-Order Derivative Space for Face Recognition
IEEE 2014: Transaction on Image Processing
Abstract—In this paper, a novel local pattern descriptor generated by the proposed local vector pattern (LVP) in high-order derivative space is presented for use in face recognition. Based on the vector of each pixel constructed by computing the values between the referenced pixel and the adjacent pixels with diverse distances from different directions, the vector representation of the referenced pixel is generated to provide the 1D structure of micro patterns. With the devise of pairwise direction of vector for each pixel, the LVP reduces the feature length via comparative space transform to encode various spatial surrounding relationships between the referenced pixel and its neighborhood pixels. Besides, the concatenation of LVPs is compacted to produce more distinctive features. To effectively extract more detailed discriminative information in a given sub region, the vector of LVP is refined by varying local derivative directions from the nth-order LVP in (n − 1)th-order derivative space, which is a much more resilient structure of micro patterns than standard local pattern descriptors. The proposed LVP is compared with the existing local pattern descriptors including local binary pattern (LBP), local derivative pattern (LDP), and local tetra pattern (LTrP) to evaluate the performances from input grayscale face images. In addition, extensive experiments conducting on benchmark face image databases, FERET, CAS-PEAL, CMU-PIE, Extended Yale B, and LFW, demonstrate that the proposed LVP in high-order derivative space indeed performs much better than LBP, LDP, and LTrP in face recognition.



Accelerated Learning-Based Interactive Image Segmentation Using Pairwise Constraints
IEEE 2014: Transactions on Image processing
Abstract—Algorithms for fully automatic segmentation of images are often not sufficiently generic with suitable accuracy, and fully manual segmentation is not practical in many settings. There is a need for semiautomatic algorithms, which are capable of interacting with the user and taking into account the collected feedback. Typically, such methods have simply incorporated user feedback directly. Here, we employ active learning of optimal queries to guide user interaction. Our work in this paper is based on constrained spectral clustering that iteratively incorporates user feedback by propagating it through the calculated affinities. The original framework does not scale well to large data sets, and hence is not straightforward to apply to interactive image segmentation. In order to address this issue, we adopt advanced numerical methods for eigen-decomposition implemented over a subsampling scheme. Our key innovation, however, is an active learning strategy that chooses pairwise queries to present to the user in order to increase the rate of learning from the feedback. Performance evaluation is carried out on the Berkeley segmentation and Graz-02 image data sets, confirming that convergence to high accuracy levels is realizable in relatively few iterations.



An Analysis and Method for Contrast Enhancement Turbulence Mitigation
IEEE 2014 : Transactions on Image Processing

Abstract—A common problem for imaging in the atmosphere is fog and atmospheric turbulence. Over the years, many researchers have provided insight into the physics of either the fog or turbulence but not both. Most recently, researchers have proposed methods to remove fog in images fast enough for real-time processing. Additionally, methods have been proposed by other researchers that address the atmospheric turbulence problem. In this paper, we provide an analysis that incorporates both physics models: 1) fog and 2) turbulence. We observe how contrast enhancements (fog removal) can affect image alignment and image averaging. We present in this paper, a new joint contrast enhancement and turbulence mitigation (CETM) method that utilizes estimations from the contrast enhancement algorithm to improve the turbulence removal algorithm. We provide a new turbulent mitigation object metric that measures temporal consistency. Finally, we design the CETM to be efficient such that it can operate in fractions of a second for near real-time applications. 



BRINT: Binary Rotation Invariant and Noise Tolerant Texture Classification
IEEE 2014 :Transactions on Image Processing 
Abstract—In this paper, we propose a simple, efficient, yet robust multi resolution approach to texture classification—binary rotation invariant and noise tolerant (BRINT). The proposed approach is very fast to build, very compact while remaining robust to illumination variations, rotation changes, and noise. We develop a novel and simple strategy to compute a local binary descriptor based on the conventional local binary pattern (LBP) approach, preserving the advantageous characteristics of uniform LBP. Points are sampled in a circular neighborhood, but keeping the number of bins in a single-scale LBP histogram constant and small, such that arbitrarily large circular neighborhoods can be sampled and compactly encoded over a number of scales. There is no necessity to learn a text on dictionary, as in methods based on clustering, and no tuning of parameters is required to deal with different data sets. Extensive experimental results on representative texture databases show that the proposed BRINT not only demonstrates superior performance to a number of recent state-of-the-art LBP variants under normal conditions, but also performs significantly and consistently better in presence of noise due to its high distinctiveness and robustness. This noise robustness characteristic of the proposed BRINT is evaluated quantitatively with different artificially generated types and levels of noise (including Gaussian, salt and pepper, and speckle noise) in natural texture images.


Complex Background Subtraction by Pursuing Dynamic Spatio-Temporal Models
IEEE 2014 : TTransactions on Image processing
Abstract—Although it has been widely discussed in video surveillance, background subtraction is still an open problem in the context of complex scenarios, e.g., dynamic backgrounds, illumination variations, and indistinct foreground objects. To address these challenges, we propose an effective background subtraction method by learning and maintaining an array of dynamic texture models within the spatio-temporal representations. At any location of the scene, we extract a sequence of regular video bricks, i.e., video volumes spanning over both spatial and temporal domain. The background modeling is thus posed as pursuing subspaces within the video bricks while adapting the scene variations. For each sequence of video bricks, we pursue the subspace by employing the auto regressive moving average model that jointly characterizes the appearance consistency and temporal coherence of the observations. During online processing, we incrementally update the subspaces to cope with disturbances from foreground objects and scene changes. In the experiments, we validate the proposed method in several complex scenarios, and show superior performances over other state-of-the-art approaches of background subtraction. The empirical studies of parameter setting and component analysis are presented as well.


CSMMI: Class-Specific Maximization of Mutual Information for Action and Gesture  Recognition
Abstract—In this paper, we propose a novel approach called class-specific maximization of mutual information (CSMMI) using a submodular method, which aims at learning a compact and discriminative dictionary for each class. Unlike traditional dictionary-based algorithms, which typically learn a shared dictionary for all of the classes, we unify the intraclass and interclass mutual information (MI) into an single objective function to optimize class-specific dictionary. The objective function has two aims: 1) maximizing the MI between dictionary items within a specific class (intrinsic structure) and 2) minimizing the MI between the dictionary items in a given class and those of the other classes (extrinsic structure). We significantly reduce the computational complexity of CSMMI by introducing an novel submodular method, which is one of the important contributions of this paper. This paper also contributes a state-of-the-art endto-end system for action and gesture recognition incorporating CSMMI, with feature extraction, learning initial dictionary per each class by sparse coding, CSMMI via submodularity, and classification based on reconstruction errors. We performed extensive experiments on synthetic data and eight benchmark data sets. Our experimental results show that CSMMI outperforms shared dictionary methods and that our end-to-end system is competitive with other state-of-the-art approaches.

IEEE 2013– MATLAB Project List

Reversible Watermarking Based on Invariant Image Classification and Dynamic Histogram Shifting

AbstractIn this paper, we propose a new reversible watermarking scheme. One first contribution is a histogram shifting modulation which adaptively takes care of the local specificities of the image content. By applying it to the image prediction-errors and by considering their immediate neighborhood, the scheme we propose inserts data in textured areas where other methods fail to do so. Furthermore, our scheme makes use of a classification process for identifying parts of the image that can be watermarked with the most suited reversible modulation. This classification is based on a reference image derived from the image itself, a prediction of it, which has the property of being invariant to the watermark insertion. In that way, the watermark embedder and extractor remain synchronized for message extraction and image reconstruction.


Automatic Detection and Reconstruction of Building Radar Footprints From Single VHR SAR Images
Abstract—The spaceborne synthetic aperture radar (SAR) systems Cosmo-SkyMed, TerraSAR-X, and TanDEM-X acquire imagery with very high spatial resolution (VHR), supporting various important application scenarios, such as damage assessment in urban areas after natural disasters. To ensure a reliable, consistent, and fast extraction of the information from the complex SAR scenes, automatic information extraction methods are essential. Focusing on the analysis of urban areas, which is of prime interest of VHR SAR, in this paper, we present a novel method for the automatic detection and 2-D reconstruction of building radar footprints from VHR SAR scenes. Unlike most of the literature methods, the proposed approach can be applied to single images. The method is based on the extraction of a set of low-level features from the images and on their composition to more structured primitives using a production system. Then, the concept of semantic meaning of the primitives is introduced and used for both the generation of building candidates and the radar footprint reconstruction. The semantic meaning represents the probability that a primitive belongs to a certain scattering class (e.g., double bounce, roof, facade) and has been defined in order to compensate for the lack of detectable features in single images. Indeed, it allows the selection of the most reliable primitives and footprint hypotheses on the basis of fuzzy membership grades.

Linear Distance Coding for Image Classification
Abstract—The feature coding-pooling framework is shown to perform well in image classification tasks, because it can generate discriminative and robust image representations. The unavoidable information loss incurred by feature quantization in the coding process and the undesired dependence of pooling on the image spatial layout, however, may severely limit the classification. In this paper, we propose a linear distance coding (LDC) method to capture the discriminative information lost in traditional coding methods while simultaneously alleviating the dependence of pooling on the image spatial layout. The core of the LDC lies in transforming local features of an image into more discriminative distance vectors, where the robust imageto-class distance is employed. These distance vectors are further encoded into sparse codes to capture the salient features of the image.

Interactive Segmentation for Change Detection in Multispectral Remote-Sensing Images

AbstractIn this letter, we propose to solve the change detection (CD) problem in multitemporal remote-sensing images using interactive segmentation methods. The user needs to input markers related to change and no-change classes in the difference image. Then, the pixels under these markers are used by the support vector machine classifier to generate a spectral-change map. To enhance further the result, we include the spatial contextual information in the decision process using two different solutions based on Markov random field and level-set methods.



Estimating Information from Image Colors: An Application to Digital Cameras and Natural Scenes

AbstractThe colors present in an image of a scene provide information about its constituent elements. But the amount of information depends on the imaging conditions and on how information is calculated. This work had two aims. The first was to derive explicitly estimators of the information available and the information retrieved from the color values at each point in images of a scene under different illuminations.




Casual Stereoscopic Photo Authoring
AbstractStereoscopic 3D displays become more andmore popular these years. However, authoring high-quality stereoscopic 3D content remains challenging. In this paper, we present a method for easy stereoscopic photo authoring with a regular (monocular) camera. Our method takes two images or video frames using a monocular camera as input and transforms them into a stereoscopic image pair that provides a pleasant viewing experience. The key technique of our method is a perceptual-plausible image rectification algorithm that warps the input image pairs to meet the stereoscopic geometric constraint while avoiding noticeable visual distortion. Our method uses spatially-varying mesh-based image warps. Our warping method encodes a variety of constraints to best meet the stereoscopic geometric constraint and minimize visual distortion. Since each energy term is quadratic, our method eventually formulates the warping problem as a quadratic energy minimization which is solved efficiently using a sparse linear solver. Our method also allows both local and global adjustments of the disparities, an important property for adapting resulting stereoscopic images to different viewing conditions.


                                                     Airborne Vehicle Detection in Dense Urban Areas Using HoG Features
 AbstractVehicle detection has been an important research field for years as there are a lot of valuable applications, ranging from support of traffic planners to real-time traffic management. Especially detection of cars in dense urban areas is of interest due to the high traffic volume and the limited space. In city areas many car-like objects (e.g., dormers) appear which might lead to confusion. Additionally, the inaccuracy of road databases supporting the extraction process has to be handled in a proper way. This paper describes an integrated real-time processing chain which utilizes multiple occurrence of objects in images.


Rich Intrinsic Image Decomposition of Outdoor Scenes from Multiple Views
Abstract—Intrinsic images aim at separating an image into its reflectance and illumination components to facilitate further analysis or manipulation. This separation is severely ill-posed and the most successful methods rely on user indications or precise geometry to resolve the ambiguities inherent to this problem. In this paper we propose a method to estimate intrinsic images from multiple views of an outdoor scene without the need for precise geometry and with a few manual steps to calibrate the input. We use multiview stereo to automatically reconstruct a 3D point cloud of the scene.

                                                    A Hybrid Multiview Stereo Algorithm for Modeling Urban Scenes
Abstract-We present an original multiview stereo reconstruction algorithm which allows the 3D-modeling of urban scenes as a combination of meshes and geometric primitives. The method provides a compact model while preserving details: Irregular elements such as statues and ornaments are described by meshes, whereas regular structures such as columns and walls are described by primitives (planes, spheres, cylinders, cones, and tori). We adopt a two-step strategy consisting first in segmenting the initial meshbased surface using a multilabel Markov Random Field-based model and second in sampling primitive and mesh components simultaneously on the obtained partition by a Jump-Diffusion process. The quality of a reconstruction is measured by a multi-object energy model which takes into account both photo-consistency and semantic considerations (i.e., geometry and shape layout). The segmentation and sampling steps are embedded into an iterative refinement procedure which provides an increasingly accurate hybrid representation

                                                     Histology Image Retrieval in Optimized Multifeature Spaces
AbstractContent-based histology image retrieval systems have shown great potential in supporting decision making in clinical activities, teaching, and biological research. In content-based image retrieval, feature combination plays a key role. It aims at enhancing the descriptive power of visual features corresponding to semantically meaningful queries. It is particularly valuable in histology image analysis where intelligent mechanisms are needed for interpreting varying tissue composition and architecture into histological concepts. This paper presents an approach to automatically combine heterogeneous visual features for histology image retrieval. The aim is to obtain the most representative fusion model for a particular keyword that is associated with multiple query images. The core of this approach is a multiobjective learning method, which aims to understand an optimal visual-semantic matching function by jointly considering the different preferences of the group of query images. The task is posed as an optimization problem, and a multiobjective optimization strategy is employed in order to handle potential contradictions in the query images associated with the same keyword.

Automatic License Plate Recognition (ALPR)
AbstractAutomatic license plate recognition (ALPR) is the extraction of vehicle license plate information from an image or a sequence of images. The extracted information can be used with or without a database in many applications, such as electronic payment systems (toll payment, parking fee payment), and freeway and arterial monitoring systems for traffic surveillance. The ALPR uses either a color, black and white, or infrared camera to take images. The quality of the acquired images is a major factor in the success of the ALPR. ALPR as a reallife application has to quickly and successfully process license plates under different environmental conditions, such as indoors, outdoors, day or night time. It should also be generalized to process license plates from different nations, provinces, or states. These plates usually contain different colors, are written in different languages, and use different fonts; some plates may have a single color background and others have background images. The license plates can be partially occluded by dirt, lighting, and towing accessories on the car.

Context-Based Hierarchical Unequal Merging for SAR Image Segmentation
AbstractThis paper presents an image segmentation method named Context-based Hierarchical Unequal Merging for Synthetic aperture radar (SAR) Image Segmentation (CHUMSIS), which uses superpixels as the operation units instead of pixels. Based on the Gestalt laws, three rules that realize a new and natural way to manage different kinds of features extracted from SAR images are proposed to represent superpixel context. The rules are prior knowledge from cognitive science and serve as top-down constraints to globally guide the superpixel merging. The features, including brightness, texture, edges, and spatial information, locally describe the superpixels of SAR images and are bottom-up forces. While merging superpixels, a hierarchical unequalmerging algorithm is designed, which includes two stages: 1) coarse merging stage and 2) fine merging stage. The merging algorithm unequally allocates computation resources so as to spend less running time in the superpixels without ambiguity and more running time in the superpixels with ambiguity.

Robust Hashing for Image Authentication Using  Zernike Moments and Local Features
AbstractA robust hashing method is developed for detecting image forgery including removal, insertion, and replacement of objects, and abnormal color modification, and for locating the forged area. Both global and local features are used in forming the hash sequence. The global features are based on Zernike moments representing luminance and chrominance characteristics of the image as a whole. The local features include position and texture information of salient regions in the image. Secret keys are introduced in feature extraction and hash construction. While being robust against content-preserving image processing, the hash is sensitive to malicious tampering and, therefore, applicable to image authentication. The hash of a test image is compared with that of a reference image. When the hash distance is greater than a threshold and less than, the received image is judged as a fake. By decomposing the hashes, the type of image forgery and location of forged areas can be determined. Probability of collision between hashes of different images approaches zero.

Context-Dependent Logo Matching and Recognition
                                                                                         
Abstract—We contribute, through this paper, to the design of a novel variational framework able to match and recognize multiple instances of multiple reference logos in image archives. Reference logos and test images are seen as constellations of local features (interest points, regions, etc.) and matched by minimizing an energy function mixing: 1) a fidelity term that measures the quality of feature matching, 2) a neighborhood criterion that captures feature co-occurrence/geometry, and 3) a regularization term that controls the smoothness of the matching solution.

Active Visual Segmentation
                                           
 Abstract—Attention is an integral part of the human visual system and has been widely studied in the visual attention literature. The human eyes fixate at important locations in the scene, and every fixation point lies inside a particular region of arbitrary shape and size, which can either be an entire object or a part of it. Using that fixation point as an identification marker on the object, we propose a method to segment the object of interest by finding the “optimal” closed contour around the fixation point in the polar space, avoiding the perennial problem of scale in the Cartesian space. The proposed segmentation process is carried out in two separate steps: First, all visual cues are combined to generate the probabilistic boundary edge map of the scene; second, in this edge map, the “optimal” closed contour around a given fixation point is found. Having two separate steps also makes it possible to establish a simple feedback between the mid-level cue (regions) and the low-level visual cues  (edges).

Design of Low-Complexity High-Performance Wavelet Filters for Image Analysis
                                                                                                                          Abstract—This paper addresses the construction of a family of wavelets based on halfband polynomials. An algorithm is proposed that ensures maximum zeros at ω π for a desired length of analysis and synthesis filters. We start with the coefficients of the polynomial (x + 1)n and then use a generalized matrix formulation method to construct the filter halfband polynomial. The designed wavelets are efficient and give acceptable levels of peak signal-to-noise ratio when used for image compression. Furthermore, these wavelets give satisfactory recognition rates when used for feature extraction. Simulation results show that the designed wavelets are effective and more efficient than the existing standard wavelets.

Uncompressed Video Quality Metric Based on Watermarking Technique
                                                                                                     
Abstract – This paper presents a no-reference video quality metric that blindly estimates the quality of a video. The proposed system is based on video watermarking using 8x8 blocks DCT coefficients of YCBCR domain, and for watermark generation; the Geffe generator has been used to generate binary stream sequence watermark in embedding and extracting processor. Data hiding is achieved by simple “even-odd” signaling of the DCT coefficients. The comparison process between the extracted watermark and the generated watermark from Geffe generator was calculated to conclude the video quality assessment by measuring the watermark degradation. An identical watermark within each frame has been used in this system. With these mechanisms, the proposed method is robust against the attacks of frame dropping, averaging, swapping, and statistical analysis. The results indicate that the proposed video quality metric outperforms standard Peak Signal to Noise Ratio (PSNR) and structural similarity and Image Quality (SSIM) metric in estimating the perceived quality of a video.

Noise reduction in magnetic resonance images using adaptive non-local means filtering    
                                                                                                                                   
Abstract: Proposed is a noise reduction method for magnetic resonance (MR) images. This method can be considered a new adaptive non-local means filtering technique since different weights based on the edgeness of an image are applied. Unlike conventional noise reduction methods, which typically fail in preserving detailed information, the proposed method preserves fine structures while significantly reducing noise in MR images. For comparing the proposed method with other noise reduction methods, both a simulated ground truth data set and real MR images were used. The experiment shows that the proposed method outperforms conventional methods in terms of both restoration accuracy and quality.


Bio-Medical Based Image Processing

Wavelet statistical texture features-based segmentation and classification of brain computed tomography images
                  
Abstract: A computer software system is designed for segmentation and classification of benign and malignant tumour slices in brain computed tomography images. In this study, the authors present a method to select both dominant run length and co occurrence texture features of wavelet approximation tumour region of each slice to be segmented by a support vector machine (SVM). Two-dimensional discrete wavelet decomposition is performed on the tumour image to remove the noise. The images considered for this study belong to 208 tumour slices. Seventeen features are extracted and six features are selected using Student's t-test. This study constructed the SVM and probabilistic neural network (PNN) classifiers with the selected features. The classification accuracy of both classifiers are evaluated using the k fold cross validation method. The segmentation results are also compared with the experienced radiologist ground truth. Quantitative analysis between ground truth and the segmented tumour is presented in terms of segmentation accuracy and segmentation error. The proposed system provides some newly found texture features have an important contribution in classifying tumour slices efficiently and accurately. The experimental results show that the proposed SVM classifier is able to achieve high segmentation and classification accuracy effectiveness as measured by sensitivity and specificity.

Computerized Detection of Lung Nodules by Means of “Virtual Dual-Energy” Radiography

AbstractMajor challenges in current computer-aided detection (CADe) schemes for nodule detection in chest radiographs (CXRs) are to detect nodules that overlap with ribs and/or clavicles and to reduce the frequent false positives (FPs) caused by ribs. Detection of such nodules by a CADe scheme is very important, because radiologists are likely to miss such subtle nodules. Our purpose in this study was to develop a CADe scheme with improved sensitivity and specificity by use of “virtual dualenergy” (VDE) CXRs where ribs and clavicles are suppressed with massive-training artificial neural networks (MTANNs). To reduce rib-induced FPs and detect nodules overlapping with ribs, we incorporated the VDE technology in our CADe scheme. The VDE technology suppressed rib and clavicle opacities in CXRs while maintaining soft-tissue opacity by use of the MTANN technique that had been trained with real dual-energy imaging. Our scheme detected nodule candidates on VDE images by use of a morphologic filtering technique.

An Optimized Wavelength Band Selection for Heavily Pigmented Iris Recognition

AbstractCommercial iris recognition systems usually acquire images of the eye in 850-nm band of the electromagnetic spectrum. In this work, the heavily pigmented iris images are captured at 12 wavelengths, from 420 to 940 nm. The purpose is to find the most suitable wavelength band for the heavily pigmented iris recognition. A multispectral acquisition system is first designed for imaging the iris at narrow spectral bands in the range of 420–940 nm. Next, a set of 200 human black irises which correspond to the right and left eyes of 100 different subjects are acquired for an analysis. Finally, the most suitable wavelength for heavily pigmented iris recognition is found based on two approaches: 1) the quality assurance of texture; 2) matching performance—equal error rate (EER) and false rejection rate (FRR).

Adaptive Fingerprint Image Enhancement with Emphasis on Pre-processing of Data

AbstractThis article proposes several improvements to an adaptive fingerprint enhancement method that is based on contextual filtering. The term adaptive implies that parameters of the method are automatically adjusted based on the input fingerprint image. Five processing blocks comprise the adaptive fingerprint enhancement method, where four of these blocks are updated in our proposed system. Hence, the proposed overall system is novel. The four updated processing blocks are; pre-processing, global analysis, local analysis and matched filtering. In the pre-processing and local analysis blocks, a nonlinear dynamic range adjustment method is used. In the global analysis and matched filtering blocks, different forms of order statistical filters are applied.

Robust Face Recognition for Uncontrolled Pose and Illumination Changes

AbstractFace recognition has made significant advances in the last decade, but robust commercial applications are still lacking. Current authentication/identification applications are limited to controlled settings, e.g., limited pose and illumination changes, with the user usually aware of being screened and collaborating in the process. Among others, pose and illumination changes are limited. To address challenges from looser restrictions, this paper proposes a novel framework for real-world face recognition in uncontrolled settings named Face Analysis for Commercial Entities (FACE). Its robustness comes from normalization (“correction”) strategies to address pose and illumination variations. In addition, two separate image quality indices quantitatively assess pose and illumination changes for each biometric query, before submitting it to the classifier. Samples with poor quality are possibly discarded or undergo a manual classification or, when possible, trigger a new capture. After such filter, template similarity for matching purposes is measured using a localized version of the image correlation index. Finally, FACE adopts reliability indices, which estimate the “acceptability” of the final identification decision made by the classifier. 



                                                IEEE 2012– MATLAB Project List


Monotonic Regression: A New Way for Correlating Subjective and Objective Ratings in Image Quality Research

Abstract—To assess the performance of image quality metrics (IQMs), some regressions, such as logistic regression and polynomial regression, are used to correlate objective ratings with subjective scores. However, some defects in optimality are shown in these regressions. In this correspondence, monotonic regression (MR) is found to be an effective correlation method in the performance assessment of IQMs. Both theoretical analysis and experimental results have proven that MR performs better than any other regression. We believe that MR could be an effective tool for performance assessment in the IQM research.

Accelerated Hypothesis Generation for Multistructure Data via Preference Analysis

Abstract—Random hypothesis generation is integral to many robust geometric model fitting techniques. Unfortunately, it is also computationally expensive, especially for higher order geometric models and heavily contaminated data. We propose a fundamentally new approach to accelerate hypothesis sampling by guiding it with information derived from residual sorting. We show that residual sorting innately encodes the probability of two points having arisen from the same model, and is obtained without recourse to domain knowledge (e.g., keypoint matching scores) typically used in previous sampling enhancement methods. More crucially, our approach encourages sampling within coherent structures and thus can very rapidly generate all-inlier minimal subsets that maximize the robust criterion. Sampling within coherent structures also affords a natural ability to handle multistructure data, a condition that is usually detrimental to other methods. The result is a sampling scheme that offers substantial speed-ups on common computer vision tasks such as homography and fundamental matrix estimation. We show on many computer vision data, especially those with multiple structures, that ours is the only method capable of retrieving satisfactory results within realistic time budgets.

Semi supervised Biased Maximum Margin Analysis for Interactive Image Retrieval

Abstract—With many potential practical applications, content-based image retrieval (CBIR) has attracted substantial attention during the past few years. A variety of relevance feedback (RF) schemes have been developed as a powerful tool to bridge the semantic gap between low-level visual features and high-level semantic concepts, and thus to improve the performance of CBIR systems. Among various RF approaches, support-vector-machine (SVM)-based RF is one of the most popular techniques in CBIR. Despite the success, directly using SVM as an RF scheme has two main drawbacks. First, it treats the positive and negative feedbacks equally, which is not appropriate since the two groups of training feedbacks have distinct properties. Second, most of the SVM-based RF techniques do not take into account the unlabeled samples, although they are very helpful in constructing a good classier. To explore solutions to overcome these two drawbacks, in this paper, we propose a biased maximum margin analysis (BMMA) and a semi supervised BMMA (SemiBMMA) for in- tegrating the distinct properties of feedbacks and utilizing the information of unlabeled samples for SVM-based RF schemes. The BMMA differentiates positive feedbacks from negative ones based on local analysis, whereas the SemiBMMA can effectively integrate information of unlabeled samples by introducing a Laplacian regularizer to the BMMA. We formally formulate this problem into a general subspace learning task and then propose an automatic approach of determining the dimensionality of the embedded subspace for RF. Extensive experiments on a large real-world image database demonstrate that the proposed scheme combined with the SVM RF can significantly improve the performance of CBIR systems.

Polyview Fusion: A Strategy to Enhance Video-Denoising Algorithms

Abstract—We propose a simple but effective strategy that aims to enhance the performance of existing video denoising algorithms, i.e., polyview fusion (PVF). The idea is to denoise the noisy video as a 3-D volume using a given base 2-D denoising algorithm but applied from multiple views (front, top, and side views). A fusion algorithm is then designed to merge the resulting multiple denoised videos into one, so that the visual quality of the fused video is improved. Extensive tests using a variety of base video-denoising algorithms show that the proposed PVF method leads to surprisingly significant and consistent gain in terms of both peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) performance, particularly at high noise levels, where the improvement over state-of-the-art denoising algorithms is often more than 2 dB in PSNR.

Patch-Based Near-Optimal Image Denoising

Abstract—In this paper, we propose a denoising method motivated by our previous analysis of the performance bounds for image denoising. Insights from that study are used here to derive a high-performance practical denoising algorithm. We propose a patch-based Wiener filter that exploits patch redundancy for image denoising. Our framework uses both geometrically and photometrically similar patches to estimate the different filter parameters. We describe how these parameters can be accurately estimated directly from the input noisy image. Our denoising approach, designed for near-optimal performance (in the mean-squared error sense), has a sound statistical foundation that is analyzed in detail. The performance of our approach is
experimentally verified on a variety of images and noise levels. The results presented here demonstrate that our proposed method is on par or exceeding the current state of the art, both visually and quantitatively.

Color Constancy for Multiple Light Sources

Abstract—Color constancy algorithms are generally based on the simplifying assumption that the spectral distribution of a light source is uniform across scenes. However, in reality, this assumption is often violated due to the presence of multiple light sources. In this paper, we will address more realistic scenarios where the uniform light-source assumption is too restrictive. First, a methodology is proposed to extend existing algorithms by applying color constancy locally to image patches, rather than globally to the entire image. After local (patch-based) illuminant estimation, these estimates are combined into more robust estimations, and a local correction is applied based on a modified diagonal model. Quantitative and qualitative experiments on spectral and real images show that the proposed methodology reduces the influence of two light sources simultaneously present in one scene. If the chromatic
difference between these two illuminants is more than 1 , the proposed framework outperforms algorithms based on the uniform light-source assumption (with error-reduction up to approximately 30%).Otherwise,when the chromatic difference is less than 1 and the scene can be considered to contain one (approximately) uniform light source, the performance of the proposed method framework is similar to global color constancy methods.

A Novel Data Embedding Method Using Adaptive Pixel Pair Matching

Abstract—This paper proposes a new data-hiding method based on pixel pair matching (PPM). The basic idea of PPM is to use the values of pixel pair as a reference coordinate, and search a coordinate in the neighborhood set of this pixel pair according to a givenmessage digit. The pixel pair is then replaced by the searched coordinate to conceal the digit. Exploiting  modification direction (EMD) and diamond encoding (DE) are two data-hiding methods proposed recently based on PPM. The maximum capacity of EMD is 1.161 bpp and DE extends the payload of EMD by embedding digits in a larger notational system. The proposed method offers lower distortion than DE by providing more compact neighborhood sets and allowing embedded digits in any notational system. Compared with the optimal pixel adjustment process (OPAP) method, the proposed method always has lower distortion for various payloads. Experimental results reveal that the proposed method not only provides better performance than those of OPAP and DE, but also is secure under the detection of some well-known steganalysis techniques.

Performance Analysis of a Block-Neighborhood-Based Self-Recovery Fragile Watermarking Scheme

Abstract—In this paper,we present the performance analysis of a self-recovery fragile watermarking scheme using block-neighborhood tamper characterization. This method uses a pseudorandom sequence to generate the nonlinear block-mapping and employs an optimized neighborhood characterization method to detect the tampering. Performance of the proposed method and its resistance to malicious attacks are analyzed. We also investigate three optimization strategies that will further improve the quality of tamper localization and recovery. Simulation results demonstrate that the proposed method allows image recovery with an acceptable visual quality peak signal-to-noise ratio (PSNR) dB up to 60% tampering.


Nonparametric Bayesian Dictionary Learning for Analysis  of   Noisy  and  Incomplete Images.

Abstract—Nonparametric Bayesian methods are considered for recovery of imagery based upon compressive, incomplete, and/or noisy measurements. A truncated beta-Bernoulli process is employed to infer an appropriate dictionary for the data under test and also for image recovery. In the context of compressive sensing, significant improvements in image recovery are manifested using learned dictionaries, relative to using standard or thonormal image expansions. The compressive-measurement projections are also optimized for the learned dictionary. Additionally, we consider simpler (incomplete) measurements, defined by measuring a subset of image pixels, uniformly selected at random. Spatial interrelationships within imagery are exploited through use of the Dirichlet and probit stick-breaking processes. Several example results are presented, with comparisons to other methods in the literature.

A Primal–Dual Method for Total-Variation-Based Wavelet  Domain    Inpainting.

Abstract—Loss of information in a wavelet domain can occur during storage or transmission when the images are formatted and stored in terms of wavelet coefficients. This calls for image inpainting in wavelet domains. In this paper, a variational ap- proach is used to formulate the reconstruction problem. We propose a simple but very efficient iterative scheme to calculate an optimal solution and prove its convergence. Numerical results are presented to show the performance of the proposed algorithm.

Blind Separation of  Superimposed  Moving Images Using  Image   Statistics.

Abstract— Nowadays, a lot of captured signals represent a mixture of two or more original signals and the necessity of automatically separating them into original sources arises. The separation of a set of signals from a set of mixed signals without the aid of additional information is called blind source separation. I dedicated my experimentation project to study this problem for the case of image mixtures formed when taking a picture of a reective surface (i.e, painting protected by glass). A lot of researchers addressed this problem and proposed dierent methods. Some of them are variants of some popular methods in blind source separation, like Independent Component Analysis, others are based on simple image properties or statistics. This paper presents an overview of the existing methods for image separation and focuses on the analysis and experimentation of an algorithm developed by Gai et al, Blind Separation of Superimposed Moving Images using Image Statistics. The algorithm assumes that the mixtures are linear, with unknown linear mixing coeffcients and unknown motions of sources in each image and it is based on the statistics of natural images. Besides the separation of the original sources, the method can automatically identify the number of original images and it has good results even in under-determined cases, where mixtures are fewer than layers. By experimenting the method, I identi ed some small drawbacks and gave their possible explanation. Even if this method has impressive results, it doesn't work in real time;thus, there is still a lot of room for improvement in the eld.

Image Signature: Highlighting Sparse Salient Regions.

Abstract—We introduce a simple image descriptor referred to as the image signature. We show, within the theoretical framework of sparse signal mixing, that this quantity spatially approximates the foreground of an image. We experimentally investigate whether this approximate foreground overlaps with visually conspicuous image locations by developing a saliency algorithm based on the image signature. This saliency algorithm predicts human fixation points best among competitors on the Bruce and Tsotsos [1] benchmark data set and does so in much shorter running time. In a related experiment, we demonstrate with a change blindness data set that the distance between images induced by the image signature is closer to human perceptual distance than can be achieved using other saliency algorithms, pixel-wise, or GIST [2] descriptor methods.

Image Segmentation by Probabilistic Bottom-Up Aggregation  and  Cue Integration.

Abstract— We present a parameter free approach that utilizes multi-ple cues for image segmentation. Beginning with an image,we execute a sequence of bottom-up aggregation steps in which pixels are gradually merged to produce larger and larger regions. In each step we consider pairs of adjacent regions and provide a probability measure to assess whether or not they should be included in the same segment. Our probabilistic formulation takes into account intensity and texture distributions in a local area around each region. It further incorporates priors based on the geometry of the regions. Finally, posteriors based on intensity and texture cues are combined using a mixture of experts formulation. This probabilistic approach is integrated into a graph coarsening scheme providing a complete hierarchical segmentation of the image. The algorithm complexity is linear in the number of the image pixels and it requires almost no user-tuned parameters. We test our method ona variety of gray scale images and compare our results toseveral existing segmentation algorithms.

Image Restoration by Matching Gradient Distributions

Abstract—The restoration of a blurry or noisy image is commonly performed with a MAP estimator, which maximizes a posterior probability to reconstruct a clean image from a degraded image. A MAP estimator, when used with a sparse gradient image prior, reconstructs piecewise smooth images and typically removes textures that are important for visual realism. We present an alternative deconvolution method called iterative distribution reweighting (IDR) which imposes a global constraint on gradients so that a reconstructed image should have a gradient distribution similar to a reference distribution. In natural images, a reference distribution not only varies from one image to another, but also within an image depending on texture. We estimate a reference distribution directly from an input image for each texture segment. Our algorithm is able to restore rich mid-frequency textures. A large-scale user study supports the conclusion that our algorithm improves the visual realism of reconstructed images compared to those of MAP estimators.

Active Curve Recovery of Region Boundary Patterns.

Abstract—This study investigates the recovery of region boundary patterns in an image by a variational level set method which drivesc an active curve to coincide with boundaries on which a feature distribution matches a reference distribution. We formulate the scheme for both the Kullback-Leibler and the Bhattacharyya similarities, and apply it in two conditions: the simultaneous recovery of all region boundaries consistent with a given outline pattern, and segmentation in the presence of faded boundary segments. The first task uses an image-based geometric feature, and the second a photometric feature. In each case, the corresponding curve evolution equation can be viewed as a geodesic active contour (GAC) flow having a variable stopping function which depends on the feature distribution on the active curve. This affords a potent global representation of the target boundaries, which can effectively drive active curve segmentation in a variety of otherwise adverse conditions. Detailed experimentation shows that the scheme can significantly improve on current region and edge-based formulations.

BM3D Frames and Variational Image Deblurring.

Abstract—A family of the block matching 3-D (BM3D) algorithms for various imaging problems has been recently proposed within the framework of nonlocal patchwise image modeling [1], [2]. In this paper, we construct analysis and synthesis frames, formalizing BM3D image modeling, and use these frames to develop novel iterative deblurring algorithms. We consider two different formulations of the deblurring problem, i.e., one given by the minimization of the single-objective function and another based on the generalized Nash equilibrium (GNE) balance of two objective functions. The latter results in the algorithm where deblurring and denoising operations are decoupled.  The convergence of the developed algorithms is proved. Simulation experiments show that the decoupled algorithm derived from theGNE formulation demonstrates the best numerical and visual results and shows superiority with respect to the state of the art inthe field, confirming a valuable potential of BM3D-frames as an advanced image modeling tool.

Interactive Image Segmentation Using Dirichle   Process Multiple-View Learning

Abstract—Segmenting semantically meaningful whole objects from images is a challenging problem, and it becomes especially so without higher level common sense reasoning. In this paper, we present an interactive segmentation framework that integrates image appearance and boundary constraints in a principled way to address this problem. In particular, we assume that small sets of pixels, which are referred to as seed pixels, are labeled as the object and background. The seed pixels are used to estimate the labels of the unlabeled pixels using Dirichlet process multiple-view learning, which leverages 1) multiple-view learning that integrates appearance and boundary constraints and 2) Dirichlet process mixture-based nonlinear classification that simultaneously models image features and discriminates between the object and background classes. With the proposed learning and inference algorithms, our segmentation framework is experimentally shown to produce both quantitatively and qualitatively promising results on a standard dataset of images. In particular, our proposed framework is able to segment whole objects from images given insufficient seeds.

PDE-Based Enhancement of Color Images in RGB Space

Abstract—A novel method for color image enhancement is pro- posed as an extension of the scalar-diffusion–shock-filter coupling model, where noisy and blurred images are denoised and sharpened. The proposed model is based on using the single vectors of the gradient magnitude and the second derivatives as a manner to relate different color components of the image. This model canbe viewed as a generalization of the Bettahar–Stambouli filter to multivalued images. The proposed algorithm is more efficient than the mentioned filter and some previous works at color images de- noising and deblurring without creating false colors.

Robust Watermarking of Compressed and Encrypted JPEG2000  Images

Abstract—Digital asset management systems (DAMS) generally handle media data in a compressed and encrypted form. It is some-times necessary to watermark these compressed encrypted media items in the compressed-encrypted domain itself for tamper detection or ownership declaration or copyright management purposes. It is a challenge to watermark these compressed encrypted streams as the compression process would have packed the infor-mation of raw media into a low number of bits and encryption would have randomized the compressed bit stream. Attempting to watermark such a randomized bit stream can cause a dramatic degradation of the media quality. Thus it is necessary to choose an encryption scheme that is both secure and will allow water- marking in a predictable manner in the compressed encrypteddo main.Inthispaper,wepropose a robust watermarking algorithm to watermark JPEG2000 compressed and encrypted images. The encryption algorithm we propose to use is a stream cipher. While the proposed technique embeds watermark in the compressed-en- crypted domain, the extraction ofwatermark can be done in the de- crypted domain. We investigate in detail the embedding capacity, robustness, perceptual quality and security of the proposed algo- rithm, using these watermarking schemes: Spread Spectrum (SS), Scalar Costa Scheme Quantization IndexModulation (SCS-QIM), and Rational Dither Modulation (RDM).

Robust White Matter Lesion Segmentation  in FLAIR MRI

Abstract—This paper discusses a white matter lesion (WML) segmentation scheme for fluid attenuation inversion recoverynm (FLAIR) MRI. The method computes the volume of lesions with sub voxel precision by accounting for the partial volume averaging (PVA) artifact. As WMLs are related to stroke and carotid disease, accurate volume measurements are most important.Manual volume computation is laborious, subjective, time consuming, and error prone. Automated methods are a nice alternative since they quantify WML volumes in an objective, efficient, and reliable manner. PVA is initially modeled with a localized edge strength measure since PVA resides in the boundaries between tissues. This map is computed in 3-D and is transformed to a global representa- tion to increase robustness to noise. Significant edges correspond to PVA voxels, which are used to find the PVA fraction α (amount of each tissue present in mixture voxels). Results on simulated and real FLAIR images show high WML segmentation performance compared to ground truth (98.9%and 83%overlap, respectively), which outperforms othermethods. Lesion load studies are included that automatically analyze WML volumes for each brain hemi- sphere separately. This technique does not require any distribu- tional assumptions/parameters or training samples and is applied on a singleMR modality, which is a major advantage compared to the traditional methods.

Segmentation of Stochastic Images With a  Stochastic Random   Walker Method

Abstract—We present an extension of the random walker segmentation to images with uncertain gray values. Such gray-value uncertainty may result from noise or other imaging artifacts or more general from measurement errors in the image acquisition process. The purpose is to quantify the influence of the gray-value uncertainty onto the result when using random walker segmentation. In random walker segmentation, a weighted graph is built from the image, where the edge weights depend on the image gradient between the pixels. For given seed regions, the probability is evaluated for a random walk on this graph starting at a pixel to end in one of the seed regions. Here, we extend this method to images with uncertain gray values. To this end, we consider the pixel values to be random variables (RVs), thus introducing the notion of stochastic images. We end up with stochastic weights for the graph in random walker segmentation and a stochastic partial differential equation (PDE) that has to be solved. We discretize the RVs and the stochastic PDE by the method of generalized polynomial chaos, combining the recent developments in numerical methods for the discretization of stochastic PDEs and an interactive segmen- tation algorithm. The resulting algorithm allows for the detection of regions where the segmentation result is highly influenced by the uncertain pixel values. Thus, it gives a reliability estimate for the resulting segmentation, and it furthermore allows determining the probability density function of the segmented object volume.

Tumor-Cut: Segmentation of Brain Tumors on Contrast Enhanced  MR Images for Radiosurgery Applications

Abstract—In this paper, we present a fast and robust practical tool for segmentation of solid tumors with minimal user interaction to assist clinicians and researchers in radio surgery planning and assessment of the response to the therapy. Particularly, a cellular automata (CA) based seeded tumor segmentation method on contrast enhanced T1 weighted magnetic resonance (MR) images, which standardizes the volume of interest (VOI) and seed selection, is proposed. First, we establish the connection of the CA-based segmentation to the graph-theoretic methods to show that the iterative CA framework solves the shortest path problem. In that regard, we modify the state transition function of the CA to calculate the exact shortest path solution. Furthermore, a sensitivity parameter is introduced to adapt to the heterogeneous tumor segmentation problem, and an implicit level set surface is evolved on a tumor probability map constructed from CA states to impose spatial smoothness. Sufficient information to initialize the algorithmis gathered from the user simply by a line drawn on the maximumdi- ameter of the tumor, in line with the clinical practice. Furthermore, an algorithm based on CA is presented to differentiate necrotic and enhancing tumor tissue content, which gains importance for a de- tailed assessment of radiation therapy response. Validation studies on both clinical and synthetic brain tumor datasets demonstrate 80%–90%overlap performance of the proposed algorithm with an emphasis on less sensitivity to seed initialization, robustness with respect to different and heterogeneous tumor types, and its efficiency in terms of computation time.

Vehicle Detection in Aerial Surveillance Using  Dynamic Bayesian                                                      Networks

Abstract—We present an automatic vehicle detection system for aerial surveillance in this paper. In this system, we escape from the stereotype and existing frameworks of vehicle detection in aerial surveillance, which are either region based or sliding window based. We design a pixelwise classification method for vehicle detection. The novelty lies in the fact that, in spite of performing pixelwise classification, relations among neighboring pixels in a region are preserved in the feature extraction process. We consider features including vehicle colors and local features. For vehicle color extraction, we utilize a color transform to separate vehicle colors and nonvehicle colors effectively. For edge detection, we apply moment preserving to adjust the thresholds of theCanny edge detector automatically, which increases the adaptability and the accuracy for detection in various aerial images. Afterward, a dynamic Bayesian network (DBN) is constructed for the classification purpose. We convert regional local features into quantitative observations that can be referenced when applying pixelwise classification via DBN. Experiments were conducted on a wide variety of aerial videos. The results demonstrate flexibility and good generalization abilities of the proposed method on a challenging data set with aerial surveillance images taken at different heights and under different camera angles.

Multiple Object Overlap  Resolution in Histological Imagery

Abstract—Active contours and active shape models (ASM) have been widely employed in image aries of intersecting objects and to 2) handle occlusion. Multiple overlapping objects are typically segmented out as a single object. On the other hand, ASMs are limited by point correspondence issues since object landmarks need to be identified across multiple objects for initial object alignment. ASMs are also are constrained in that they can usually only segment a single object in an image. In this paper, we present a novel synergistic boundary and region-based active contour model that incorporates shape priors in a level set formulation with automated initialization based on watershed. We demonstrate an application of these synergistic active contour models using multiple level sets to segment nuclear and glandular structures on digitized histopathology images of breast and prostate biopsy specimens. Unlike previous related approaches, our model is able to resolve object overlap and separate occluded boundaries of multiple objects simultaneously. The energy functional of the active contour is comprised of three terms. The first term is the prior shape term, modeled on the object of interest, thereby constraining the deformation achieable by the active contour. The second term, a boundary-based term detects object boundaries from image gradients. The third term drives the shape prior and the contour towards the object boundary based on region statistics. The results of qualitative and quantitative evaluation on 100 prostate and 14 breast cancer histology images for the task of detecting and segmenting nuclei and lymphocytes reveals that the model easily outperforms two state of the art segmentation schemes (geodesic active contour and Rousson shape-based model) and on average is able to resolve upto 91% of overlapping/occluded structures in the images.

Automatic Assessment of Macular Edema  From Color Retinal  Images

 Abstract—Diabetic macular edema (DME) is an advanced symptom of diabetic retinopathy and can lead to irreversible vi- sion loss. In this paper, a two-stage methodology for the detection and classification of DME severity from color fundus images is proposed. DME detection is carried out via a supervised learning approach using the normal fundus images. A feature extraction technique is introduced to capture the global characteristics of the fundus images and discriminate the normal from DME images. Disease severity is assessed using a rotational asymmetry metric by examining the symmetry of macular region. The performance of the proposed methodology and features are evaluated against several publicly available datasets. The detection performance has a sensitivity of 100% with specificity between 74% and 90%. Cases needing immediate referral are detected with a sensitivity of 100% and specificity of 97%. The severity classification accuracy is 81% for the moderate case and 100% for severe cases. These results establish the effectiveness of the proposed solution.

Automatic Image Equalization and Contrast  Enhancement Using  Gaussian Mixture Modeling
  
Abstract—In this paper, we propose an adaptive image equalization algorithmthat automatically enhances the contrast in an input image. The algorithm uses the Gaussian mixture model to model the image gray-level distribution, and the intersection points of the Gaussian components in the model are used to partition the dynamic range of the image into input gray-level intervals. The con- trast equalized image is generated by transforming the pixels’ gray levels in each input interval to the appropriate output gray-level interval according to the dominant Gaussian component and the cumulative distribution function of the input interval. To take account of the hypothesis that homogeneous regions in the image rep- resent homogeneous silences (or set ofGaussian components) in the image histogram, the Gaussian components with small variances are weighted with smaller values than the Gaussian components with larger variances, and the gray-level distribution is also used to weight the components in the mapping of the input interval to the output interval. Experimental results show that the proposed algorithm produces better or comparable enhanced images than several state-of-the-art algorithms. Unlike the other algorithms, the proposed algorithmis free of parameter setting for a given dynamic range of the enhanced image and can be applied to a wide range of image types.

Automatic Single-Image-Based Rain Streaks  Removal via Image  Decomposition

Abstract—Rain removal from a video is a challenging problem and has been recently investigated extensively. Nevertheless, the problem of rain removal from a single image was rarely studied in the literature, where no temporal information among successive images can be exploited, making the problem very challenging. In this paper, we propose a single-image-based rain removal framework via properly formulating rain removal as an image de- composition problem based on morphological component analysis. Instead of directly applying a conventional image decomposition technique, the proposed method first decomposes an image into  he low- and high-frequency (HF) parts using a bilateral filter. The HF part is then decomposed into a “rain component” and a “nonrain component” by performing dictionary learning and sparse coding. As a result, the rain component can be successfully removed from the image while preserving most original image details. Experimental results demonstrate the efficacy of the proposed algorithm
.
Cognition and Removal of Impulse Noise With Uncertainty

Abstract—Uncertainties are the major inherent feature of impulse noise. This fact makes image denoising a difficult task. Understanding the uncertainties can improve the performance of image denoising. This paper presents a novel adaptive detail-pre- serving filter based on the cloud model (CM) to remove impulse noise. It is called the CMfilter. First, an uncertainty-based detector identifies the pixels corrupted by impulse noise. Then, a weighted fuzzy mean filter is applied to remove the noise candidates. The experimental results show that, compared with the traditional switching filters, the CM filter makes a great improvement in image denoising. Even at a noise level as high as 95%, the CM filter still can restore the image with good detail preservation.

Color Local Texture Features for Color Face Recognition

Abstract—This paper proposes new color local texture features, i.e., color local Gabor wavelets (CLGWs) and color local binary pattern (CLBP), for the purpose of face recognition (FR). The proposed color local texture features are able to exploit the dis- criminative information derived from spatio chromatic texture patterns of different spectral channels within a certain local face region. Furthermore, in order to maximize a complementary effect taken by using both color and texture information, the opponent color texture features that capture the texture patterns of spatial interactions between spectral channels are also incorporated into the generation of CLGW and CLBP. In addition, to perform the final classification, multiple color local texture features (each corresponding to the associated color band) are combined within a feature-level fusion framework. Extensive and comparative experiments have been conducted to evaluate our color local texture features for FR on five public face databases, i.e., CMU-PIE, Color FERET, XM2VTSDB, SCface, and FRGC 2.0. Experimental results show that FR approaches using color local texture features impressively yield better recognition rates than FR approaches using only color or texture information. Particularly, compared with grayscale texture features, the proposed color local texture features are able to provide excellent recognition rates for face images taken under severe variation in illumination, as well as for small- (low-) resolution face images. In addition, the feasibility of our color local texture features has been successfully demon- strated by making comparisons with other state-of-the-art color FR methods.

Co-Transduction for Shape Retrieval

Abstract—In this paper, we propose a new shape/object retrieval algorithm, namely, co-transduction. The performance of a retrieval system is critically decided by the accuracy of adopted similarity measures (distances or metrics). In shape/object retrieval, ideally, intra lass objects should have smaller distances than interclass objects. However, it is a difficult task to design an ideal metric to account for the large intra class variation. Different types of measures may focus on different aspects of the objects: for ex- ample, measures computed based on contours and skeletons are often complementary to each other. Our goal is to develop an  algorithm to fuse different similarity measures for robust shape retrieval through a semisupervised learning framework.We name our method co-transduction, which is inspired by the co-training algorithm. Given two similarity measures and a query shape, the algorithm iteratively retrieves the most similar shapes using one measure and assigns them to a pool for the other measure to do a re-ranking, and vice versa. Using co-transduction, we achieved an improved result of 97.72%(bull’s-eye measure) on the MPEG-7 data set over the state-of-the-art performance. We also present an algorithm called tri-transduction to fuse multiple-input similarities, and it achieved 99.06%on the MPEG-7 data set. Our algorithm is general, and it can be directly applied on input simi- larity measures/metrics; it is not limited to object shape retrieval and can be applied to other tasks for ranking/retrieval.

A Channel Splitting Strategy for Reducing Handoff   Delay in   Internet-Based Wireless Mesh Networks

Abstract—Seamless intergateway handoff support is an essen- tial issue to ensure continuous communications in Internet-based wireless mesh networks (WMNs). Due to the existence of multihop wireless links, traditional handoff schemes designed for single-hop wireless access networks can hardly guarantee the low handoff latency requirement in multihop WMNs. Existing solutions on reducing the handoff delay in WMNs ignore one important factor for the long handoff delay: the channel access delay of handoff sig- naling packets over themultihop wirelessmesh backbone network. In this paper, we address the seamless intergateway handoff issue in Internet-basedWMNs from a different perspective and propose a channel splitting strategy to reduce the channel access delay of handoff signaling packets over multihop wireless links. The handoff procedures of two scenarios when a mobile node has one or two transceivers are designed, and two transmission strategies for scheduling the delivery of handoff signaling packets are pro- posed. Simulation results show that by using the proposed channel splitting strategy, the handoff delay requirement in WMNs can be guaranteed, regardless of the background data traffic, and the average channel utilization can also be improved.

A Robust Distributive Approach to Adaptive Power and Adaptive  Rate Link Scheduling in Wireless Mesh Networks

Abstract—In this paper, we present a distributive heuristic algorithm for maximizing the network throughput in adaptive power and adaptive rate spatial-TDMA wireless mesh networks. At each step of our algorithm, the link with highest receive Signal-to-Interference and Noise Ratio in its neighborhood is included in the schedule that is set for the underlying time slot, configuring its Modulation/Coding Scheme so that it transmits at the highest feasible power and rate levels. The transmitting node of the winning link announces the current receive power margin of its link’s receiver to its neighbors. The transmitters of unscheduled links subsequently calculate the maximum potential Signal-to-Interference and Noise Ratio level at which their links could operate if scheduled next. The process repeats until the announced power margins of scheduled links do not allow more additions to the schedule at the underlying time slot. We show the performance of this distributive algorithm to be within 5-10% of that exhibited by our recently developed centralized algorithm, while inducing a much lower computational complexity. We also demonstrate the robustness and energy efficiency of our distributive algorithm by applying it to schedule a new set of links on top of an existing schedule. For the examined scenarios, we show that the throughput rate achieved by using such an incremental scheduling scheme is within 15% of the throughput rate attained when a complete scheduling of all the links is carried out, while reducing the control traffic overhead rate and achieving a more energy efficient operation.

Information Theoretic  Capacity of Cellula Multiple Access Channel with Shadow Fading

Abstract—In this paper, we extend the well-known model for the Gaussian Cellular Multiple Access Channel originally presented by Wyner. The first extension to the model incorporates the distance-dependent path loss (maintaining a close relevance to path loss values in real world cellular systems) experienced by the users distributed in a planar cellular array. The density of base stations and hence the cell sizes are variable. In the context of a Hyper-receiver joint decoder, an expression for the information theoretic capacity is obtained assuming a large number of users in each cell. The model is further extended to incorporate the log-normal shadow fading variations, ensuring that the shadowing models are fairly comparable to the free space model. Using these fair models the effect of the shadow fading standard deviation on the information theoretic capacity of the cellular system is quantified. It is observed that a higher standard deviation results in lower capacity if the mean path loss is appropriately adjusted in order to model the mean loss due to the physical obstacles causing the shadow fading. The results validate that larger cell sizes and a higher standard deviation of shadowing (with appropriately adjusted mean path loss) results in lower spectral efficiency.

Multi-Hop Connectivity Probability in Infrastructure-Based  Vehicular Networks

Abstract—Infrastructure-based vehicular networks (consisting of a group of Base Stations (BSs) along the road) will be widely deployed to support Wireless Access in Vehicular Environment (WAVE) and a series of safety and non-safety related applications and services for vehicles on the road. As an important measure of user satisfaction level, uplink connectivity probability is defined as the probability that messages from vehicles can be received by the infrastructure (i.e., BSs) through multi-hop paths. While on the system side, downlink connectivity probability is defined as the probability that messages can be broadcasted from BSs to all vehicles through multi-hop paths, which indicates service coverage performance of a vehicular network. This paper proposes an analytical model to predict both uplink and down- link connectivity probabilities. Our analytical results, validated by simulations and experiments, reveal the trade-off between these two key performance metrics and the important system parameters, such as BS and vehicle densities, radio coverage (or transmission power), and maximum number of hops. This insightful knowledge enables vehicular network engineers and operators to effectively achieve high user satisfaction and good service coverage, with necessary deployment of BSs along the road according to traffic density, user requirements and service types.

On the Capacity of Intensity-Modulated Direct-Detection Systems and the Information Rate of ACO-OFDM for Indoor Optical Wireless  Applications

Abstract—In this paper we derive information theoretic results for asymmetrically clipped optical orthogonal frequency division multiplexing (ACO-OFDM) in an intensity modulated direct detection (IM/DD) optical communication system subject to a range of constraints. ACO-OFDM is a form of OFDM designed for IM/DD systems. It is an effective solution to intersymbol interference (ISI) caused by a dispersive channel and also requires less optical power than conventional optical modulation formats. Although the classical Shannon capacity formula cannot be applied directly to an IM/DD system, we show that when ACO-OFDM is used in an IM/DD system, it can be adapted to calculate the information rate of the data-carrying odd frequency subcarriers. As a result conventional water filling techniques can be used for a frequency selective channel. These results are applied to indoor wireless systems using realistic parameters  for the transmitter, receiver and channel. The maximum rate at which data can be transmitted depends on the channel, the electrical bandwidth and the transmitted optical power. Even when there is no line of sight (LOS) path, when the electrical bandwidth is limited to 50 MHz and the average optical power is limited to 0.4 W, data rates of approximately 80 Mbit/scan theoretically be achieved.

Optimum Transmission Policies for Battery Limited Energy  Harvesting Nodes

Abstract—Wireless networks with energy harvesting battery equipped nodes are quickly emerging as a viable option for future wireless networks with extended lifetime. Equally important to their counterpart in the design of energy harvesting radios are the design principles that this new networking paradigm calls for. In particular, unlike wireless networks considered to date, the energy replenishment process and the storage constraints of the rechargeable batteries need to be taken into account in designing efficient transmission strategies. In this work, such transmission policies for rechargeable nodes are considered, and optimum solutions for two related problems are identified. Specifically, the transmission policy that maximizes the short term throughput, i.e., the amount of data transmitted in a finite time horizon is found. In addition, the relation of this optimization problem to another, namely, the minimization of the transmission completion time for a given amount of data is demonstrated, which leads to the solution of the latter as well. The optimum transmission policies are identified under the constraints on energy causality, i.e., energy replenishment process, as well as the energy storage, i.e., battery capacity. For battery replenishment, a model with discrete packets of energy arrivals is considered. The necessary conditions that the throughput-optimal allocation satisfies are derived, and then the algorithm that finds the optimal transmission policy with respect to the short-term throughput and the minimum transmission completion time is given. Numerical results are presented to confirm the analytical findings.

Pilot Contamination for Active Eavesdropping

Abstract—Existing studies on physical layer security often assume the availability of perfect channel state information (CSI) and overlook the importance of channel training needed for obtaining the CSI. In this letter, we discuss how an active eaves- dropper can attack the training phase in wireless communication to improve its eavesdropping performance. We derive a new security attack from the pilot contamination phenomenon, which targets at systems using reverse training to obtain the CSI at the transmitter for precoder design. This attack changes the precoder used by the legitimate transmitter in a controlled manner to strengthen the signal reception at the eavesdropper during data transmission. Furthermore, we discuss an efficient use of the transmission energy of an advanced full-duplex eavesdropper to simultaneously achieve a satisfactory eavesdropping performance whilst degrading the detection performance of thelegitimate receiver.

IEEE 2012: Reducing DRAM Image Data Access  Energy  Consumption in Video Processing

IEEE 2012 Transactions on Neural Networks and Learning Systems



Abstract—This paper presents domain-specific techniques to reduce DRAM energy consumption for image data access in video processing. In mobile devices, video processing is one of the most energy-hungry tasks, and DRAM image data access energy consumption becomes increasingly dominant in overall video processing  system energy consumption. Hence, it is highly desirable to develop domain-specific techniques that can exploit unique image data access characteristics to improve DRAM energy efficiency. Nevertheless, prior efforts on reducing DRAM energy consumption in video processing pale in comparison with that on reducing video processing logic energy consumption. In this work, we first apply three simple yet  effective data manipulation techniques that exploit image data spatial/temporal correlation to reduce DRAM image data access energy consumption, then propose a heterogeneous DRAM architecture that can better adapt to unbalanced image access in most video processing to further improve DRAM energy efficiency. DRAM modeling and power estimation have been carried out to evaluate these domain-specific design techniques, and the results show that they can reduce DRAM energy consumption by up to 92%.


IEEE 2012: A Generalized Logarithmic Image Processing Model Based on the Gigavision Sensor Model

IEEE TRANSACTIONS ON IMAGE PROCESSING

Abstract—The logarithmic image processing (LIP) model is a mathematical theory providing generalized linear operations for image processing. The gigavision sensor (GVS) is a new imaging device that can be described by a statistical model. In this paper, by studying these two seemingly unrelated models, we develop a generalized LIP (GLIP) model. With the LIP model being its special case, the GLIP model not only provides new insights into the LIP model but also defines new image representations and operations for solving general image processing problems that are not necessarily related to the GVS. A new parametric LIP model is also developed. To illustrate the application of the new scalar multiplication operation, we propose an energy-preserving algorithm for tone mapping, which is a necessary step in image dehazing. By comparing with results using two state-of-the-art algorithms, we show that the new scalar multiplication operation is an effective tool for tone mapping.

IEEE 2012: Classification of Dielectric Barrier Discharges Using Digital Image Processing Technology

IEEE TRANSACTIONS ON PLASMA SCIENCE

AbstractA digital image processing technology—gray level histogram obtained by image processing of the discharge—is proposed to classify the two kinds of dielectric barrier discharge (DBD) modes. With an increase of the applied voltage, frequency, or exposure time, the kurtosis and the skewness of the gray levels decrease, the standard deviation of the gray levels increases significantly in the case of filamentary mode, in contradistinction to the case of homogeneous mode, where its kurtosis, skewness, and standard deviation remain almost constant. The majority of the pixels correspond to near-zero gray levels in the case of the filamentary mode and have a larger gray level for the homogeneous  mode. With a decrease of the pressure, the mean gray level in the homogeneous mode increases significantly when the voltage is kept fixed. This suggests that the onset voltage in higher pressure is larger than that in lower pressure. The mean gray level in Ar is larger than that in He when the voltage is kept fixed. It may mean that the onset voltage in He is larger than that in Ar. These results indicate that the method is not only effective but also simple to classify the DBD modes

IEEE 2012: ISP: An Optimal Out-Of-Core Image-Set Processing Streaming Architecture for Parallel Heterogeneous Systems

IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS

AbstractImage population analysis is the class of statistical methods that plays a central role in understanding the development, evolution and disease of a population. However, these techniques often require excessive computational power and memory that are compounded with a large number of volumetric inputs. Restricted access to supercomputing power limits its influence in general research and practical applications. In this paper we introduce ISP, an Image-Set Processing streaming framework that harnesses the processing power of commodity heterogeneous CPU/GPU systems and attempts to solve this computational problem. In ISP we introduce specially-designed streaming algorithms and data structures that provide an optimal solution for out-of-core multi-image processing problems both in terms of memory usage and computational efficiency. ISP makes use of the asynchronous execution mechanism supported by parallel heterogeneous systems to efficiently hide the inherent latency of the processing pipeline of outof-core approaches. Consequently, with computationally intensive problems, the ISP out-of-core solution can achieve the same performance as the in-core solution. We demonstrate the efficiency of the ISP framework on synthetic and real datasets.

IEEE 2012: Toward a Unified Color Space for Perception-Based Image Processing

IEEE TRANSACTIONS ON IMAGE PROCESSING

Abstract—Image processing methods that utilize characteristics of the human visual system require color spaces with certain properties to operate effectively. After analyzing different types of perception-based image processing problems, we present a list of properties that a unified color space should have. Due to contradictory perceptual phenomena and geometric issues, a color space cannot incorporate all these properties. We therefore identify the most important properties and focus on creating opponent color spaces without cross contamination between color attributes (i.e., lightness, chroma, and hue) and with maximum perceptual uniformity induced by color-difference formulas. Color lookup tables define simple transformations from an initial color space to the new spaces. We calculate such tables using multigrid optimization considering the Hung and Berns data of constant perceived hue and the CMC, CIE94, and CIEDE2000 color-difference formulas. The resulting color spaces exhibit low cross contamination between color attributes and are only slightly less perceptually uniform than spaces optimized exclusively for perceptual uniformity. We compare the CIEDE2000-based space with commonly used color spaces in two examples of perception-based image processing. In both cases, standard methods show improved results if the new space is used. All color-space transformations and examples are provided as MATLAB codes on our website.

IEEE 2012: A Complete Processing Chain for Shadow Detection and Reconstruction in VHR Images

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING

AbstractThe presence of shadows in very high resolution (VHR) images can represent a serious obstacle for their full exploitation. This paper proposes to face this problem as a whole through the proposal of a complete processing chain, which relies on various advanced image processing and pattern recognition tools. The first key point of the chain is that shadow areas are not only detected but also classified to allow their customized compensation. The detection and classification tasks are implemented by means of the state-of-the-art support vector machine approach. A quality check mechanism is integrated in order to reduce subsequent misreconstruction problems. The reconstruction is based on a linear regression method to compensate shadow regions by adjusting the intensities of the shaded pixels according to the statistical characteristics of the corresponding nonshadow regions. Moreover, borders are explicitly handled by making use  of adaptive morphological filters and linear interpolation for the prevention of possible border artifacts in the reconstructed image. Experimental results obtained on three VHR images representing different shadow conditions are reported, discussed, and compared with two other reconstruction techniques.

IEEE 2012: Automated Multiscale Morphometry of  Muscle Disease From Second Harmonic Generation Microscopy Using Tensor-Based Image Processing

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING

AbstractPractically, all chronic diseases are characterized by tissue remodeling that alters organ and cellular function through changes to normal organ architecture. Some morphometric alterations become irreversible and account for disease progression even on cellular levels. Early diagnostics to categorize tissue alterations, as well asmonitoring progression or remission of disturbed cytoarchitecture upon treatment in the same individual, are a new emerging field. They strongly challenge spatial resolution and require advanced imaging techniques and strategies for  detecting morphological changes. We use a combined second harmonic generation (SHG) microscopy and automated image processing approach to quantify morphology in an animal model of inherited Duchenne muscular dystrophy (mdx mouse) with age. Multiphoton XYZ image stacks from tissue slices reveal vast morphological deviation in muscles from old mdx mice at different scales of cytoskeleton architecture: cell calibers are irregular, myofibrils within cells are twisted, and sarcomere lattice disruptions (detected as “verniers”) are larger in number compared to samples from healthy mice. In young mdx mice, such alterations are only minor. The boundary tensor approach, adapted and optimized for SHGdata, is a suitable approach to allow quick quantitative morphometry in whole tissue slices. The overall detection performance of the automated algorithm compares very well with manual “by eye” detection, the latter being time consuming and prone to subjective errors. Our algorithm outperfoms manual detection by time with similar reliability. This approach will be an important prerequisite for the implementation of a clinical image databases to diagnose and monitor specific morphological alterations in chronic (muscle) diseases

IEEE 2012: Hyper connections and Hierarchical Representations for Grayscale and Multiband Image  Processing

IEEE TRANSACTIONS ON IMAGE PROCESSING

Abstract—Connections in image processing are an important notion that describes how pixels can be grouped together according  to their spatial relationships and/or their gray-level values. In recent years, several works were devoted to the development of new theories of connections among which hyperconnection (h-connection) is a very promising notion. This paper addresses two major issues of this theory. First, we propose a new axiomatic that ensures that every h-connection generates decompositions that are consistent for image processing and, more precisely, for the design of h-connected filters. Second, we develop a general framework to represent the decomposition of an image into h-connections as a tree that corresponds to the generalization of the connected component tree. Such trees are indeed an efficient and intuitive way to design attribute filters or to perform detection tasks based on qualitative or quantitative attributes. These theoretical developments are applied to a particular fuzzy h-connection, and we test this new framework on several classical applications in image processing, i.e., segmentation, connected filtering, and document image binarization. The experiments confirm the suitability of the proposed approach: It is robust to noise, and it provides an efficient framework to design selective filters.

IEEE 2012: Low-Complexity Image Processing for Real-Time Detection of Neonatal Clonic Seizures


IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE


Abstract—In this paper, we consider a novel low-complexity real-time image processing-based approach to the detection of neonatal clonic seizures. Our approach is based on the extraction, from a video of a newborn, of an average luminance signal representative of the body movements. Since clonic seizures are characterized by periodic movements of parts of the body (e.g., the limbs), by evaluating the periodicity of the extracted average luminance signal it is possible to detect the presence of a clonic seizure. The periodicity is investigated, through a hybrid autocorrelation-Yin estimation technique, on a perwindow basis, where a time window is defined as a sequence of consecutive video frames. While processing is first carried out on a single window basis, we extend our approach to interlaced windows. The performance of the proposed detection algorithm is investigated, in terms of sensitivity and specificity, through Receiver Operating Characteristic ROC) curves, considering video recordings of newborns affected by neonatal seizures.

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