IEEE 2019 / 20 - PYTHON IMAGE PROCESSING PROJECTS

IEEE 2020: Deep Learning for the Detection of COVID-19 Using Deep Learning

ABSTRACT:Covid-19 disease is the one off the disorders. Though thesymptoms are benign initially, they become moresevere over time. Although for most people COVID-19 causes only mild illness, it can make some people very ill. More rarely, the disease can be fatal. Older people, and those with pre-existing medical conditions (such as high blood pressure, heart problems or diabetes) appear to be more vulnerable. In this project we are going to use chest X-Ray images for classify the covid-19. We are using Deep Learning algorithm name called Convolutional neural network for classify diagnose the disease and we able toachieve the best accuracy.

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IEEE 2020:Deep Learning based Face Mask Detection in Public Areas

ABSTRACT: According to data obtained by the World Health Organization, the global pandemic of COVID-19 has severely impacted the world and has now infected more than eight million people worldwide. Wearing face masks and following safe social distancing are two of the enhanced safety protocols need to be followed in public places in order to prevent the spread of the virus. To create safe environment that contributes to public safety, we propose an efficient computer vision-based approach focused on the real-time automated monitoring of people to were face masks in public places by implementing the model on Deep Learning to monitor activity and detect violations through camera. For this proposed model we are using Convolutional Neural Network(CNN) for train the model. 

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   IEEE 2020: FB-CNN: Feature Fusion-Based Bilinear         CNN for Classification of Fruit Fly Image 

Abstract: The high-resolution devices for image capturing and the high professional requirement for users, are very complex to extract features of the fruit fly image for classification. Therefore, a bilinear CNN model based on the mid-level and high-level feature fusion (FB-CNN) is proposed for classifying the fruit fly image. At the first step, the images of fruit fly are blurred by the Gaussian algorithm, and then the features of the fruit fly images are extracted automatically by using CNN. Afterward, the mid- and high-level features are selected to represent the local and global features, respectively. Then, they are jointly represented. When finished, the FB-CNN model was constructed to complete the task of image classification of the fruit fly. Finally, experiments data show that the FB-CNN model can effectively classify four kinds of fruit fly images. The accuracy, precision, recall, and F1 score in testing dataset are 95.00%, respectively.

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       IEEE 2020: Gender Prediction based on Voting of                CNN  models

Abstract: Gender prediction accuracy increases as CNN          architecture evolves. This paper proposes voting schemes to utilize  the already developed CNN models to further improve gender        prediction accuracy. Majority voting usually requires odd numbered models while proposed softmax based  voting can utilize any number of models to improve accuracy. With experiments, it is shown that the voting of CNN models leads to further improvement of gender prediction accuracy and that softmax-based voters always show better gender prediction accuracy than majority voters though they consist                                                          of the same CNN models.

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IEEE 2020: Color Image Reversible Data Hiding with Double-Layer Embedding 

Abstract: Reversible data hiding (RDH) in color image is an important topic of data hiding. This paper presents an efficient RDH algorithm for color image via double-layer embedding. The key contribution is the proposed double-layer embedding technique based on histogram shifting (HS). This technique exploits image interpolation to generate prediction error matrices for HS in the first-layer embedding and uses local pixel similarity to calculate difference matrices for HS in the second-layer embedding. It inherits reversibility from HS and makes high embedding capacity due to the use of double layers in data embedding. In addition, interchannel correlation is incorporated into the first-layer embedding and the second-layer embedding for generating histograms with high peaks, so as to improve embedding capacity. Experiments with open standard datasets are done to validate performance of the proposed RDH algorithm. Comparison results show that the proposed RDH algorithm outperforms some state-of-the-art RDH algorithms in terms of embedding capacity and visual quality.

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                                IEEE 2018 : Latent Fingerprint Value Prediction: Crowd-based                                                 Learning

 Abstract : Latent fingerprints are one of the most crucial sources of evidence in forensic investigations. As such, development of automatic latent fingerprint recognition systems to quickly and accurately identify the suspects is one of the most pressing problems facing fingerprint researchers. One of the first steps in manual latent processing is for a fingerprint examiner to perform a triage by assigning one of the following three values to a query latent: Value for Individualization (VID), Value for Exclusion Only (VEO) or No Value (NV). However, latent value determination by examiners is known to be subjective, resulting in large intra-examiner and inter-examiner variations. Furthermore, in spite of the guidelines available, the underlying bases that examiners implicitly use for value determination are unknown. In this paper, we propose a crowdsourcing based framework for understanding the underlying bases of value assignment by fingerprint examiners, and use it to learn a predictor for quantitative latent value assignment. Experimental results are reported using four latent fingerprint databases, two from forensic casework (NIST SD27 and MSP) and two collected in laboratory settings (WVU and IIITD), and a stateof- the-art latent AFIS. The main conclusions of our study are as follows: (i) crowdsourced latent value is more robust than prevailing value determination (VID, VEO and NV) and LFIQ for predicting AFIS performance, (ii) two bases can explain expert value assignments which can be interpreted in terms of latent features, and (iii) our value predictor can rank a collection of latents from most informative to least informative.

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