In this paper, we propose an automatic image based smoke detection using source separation. In particular, we assume that the region of interest(smoke region) is a linear combination of smoke and background pixels, an...In this paper, we propose an automatic image based smoke detection using source separation. In particular, we assume that the region of interest(smoke region) is a linear combination of smoke and background pixels, and we estimate the smoke component. More specifically, we extend the linear hyperspectral unmixing techniques to the context of image based smoke detection in order to separate the smoke component from the background. The proposed approach yields promising results especially with smoke images captured outdoor.展开更多
The world has been suffering from the Coronavirus(COVID-19)pandemic since its appearance in late 2019.COVID-19 spread has led to a drastic increase of the number of infected people and deaths worldwide.Imminent and ac...The world has been suffering from the Coronavirus(COVID-19)pandemic since its appearance in late 2019.COVID-19 spread has led to a drastic increase of the number of infected people and deaths worldwide.Imminent and accurate diagnosis of positive cases emerged as a natural alternative to reduce the number of serious infections and limit the spread of the disease.In this paper,we proposed an X-ray based COVID-19 classification system that aims at diagnosing positive COVID-19 cases.Specifically,we adapted lightweight versions of EfficientNet as backbone of the proposed recognition system.Particularly,lightweight EfficientNet networks were used to build classification models able to discriminate between positive and negative COVID-19 cases using chest X-ray images.The proposed models ensure a trade-off between scaling down the architecture of the deep network to reduce the computational cost and optimizing the classification performance.In the experiments,a public dataset containing 7,345 chest X-ray images was used to train,validate and test the proposed models for binary and multiclass classification problems,respectively.The obtained results showed the EfficientNet-elite-B9-V2,which is the lightest proposed model yielded an accuracy of 96%.On the other hand,EfficientNet-lite-B0 overtook the other models,and achieved an accuracy of 99%.展开更多
t We propose a framework for automatic fall detection based on video visual feature extraction. The proposed approach relies on a membership histogram descriptor that encodes the visual properties of the video frames....t We propose a framework for automatic fall detection based on video visual feature extraction. The proposed approach relies on a membership histogram descriptor that encodes the visual properties of the video frames. This descriptor is obtained by mapping the original low-level visual features to a more discriminative descriptor using possibilistic memberships. This mapping can be summarized in two main phases. The first one consists in categorizing the low-level visual features of the video frames arid generating homogeneous clusters in an unsupervised way. The second phase uses the obtained membership degrees generated by the clustering process to compute the membership based histogram descriptor （MHD）. For the testing stage, the proposed fall detection approach categorizes unlabeled videos as ＂Fall＂ or ＂Non-Fall＂ scene using a possibilistic K-nearest neighbors classifier. The proposed approach is assessed using standard videos dataset that simulates patient fall. Also, we compare its performance with that of state-of-the-art fall detection techniques.展开更多
文摘In this paper, we propose an automatic image based smoke detection using source separation. In particular, we assume that the region of interest(smoke region) is a linear combination of smoke and background pixels, and we estimate the smoke component. More specifically, we extend the linear hyperspectral unmixing techniques to the context of image based smoke detection in order to separate the smoke component from the background. The proposed approach yields promising results especially with smoke images captured outdoor.
基金This work was supported by the Research Center of the College of Computer and Information Sciences at King Saud University.The authors are grateful for this support.
文摘The world has been suffering from the Coronavirus(COVID-19)pandemic since its appearance in late 2019.COVID-19 spread has led to a drastic increase of the number of infected people and deaths worldwide.Imminent and accurate diagnosis of positive cases emerged as a natural alternative to reduce the number of serious infections and limit the spread of the disease.In this paper,we proposed an X-ray based COVID-19 classification system that aims at diagnosing positive COVID-19 cases.Specifically,we adapted lightweight versions of EfficientNet as backbone of the proposed recognition system.Particularly,lightweight EfficientNet networks were used to build classification models able to discriminate between positive and negative COVID-19 cases using chest X-ray images.The proposed models ensure a trade-off between scaling down the architecture of the deep network to reduce the computational cost and optimizing the classification performance.In the experiments,a public dataset containing 7,345 chest X-ray images was used to train,validate and test the proposed models for binary and multiclass classification problems,respectively.The obtained results showed the EfficientNet-elite-B9-V2,which is the lightest proposed model yielded an accuracy of 96%.On the other hand,EfficientNet-lite-B0 overtook the other models,and achieved an accuracy of 99%.
文摘t We propose a framework for automatic fall detection based on video visual feature extraction. The proposed approach relies on a membership histogram descriptor that encodes the visual properties of the video frames. This descriptor is obtained by mapping the original low-level visual features to a more discriminative descriptor using possibilistic memberships. This mapping can be summarized in two main phases. The first one consists in categorizing the low-level visual features of the video frames arid generating homogeneous clusters in an unsupervised way. The second phase uses the obtained membership degrees generated by the clustering process to compute the membership based histogram descriptor （MHD）. For the testing stage, the proposed fall detection approach categorizes unlabeled videos as ＂Fall＂ or ＂Non-Fall＂ scene using a possibilistic K-nearest neighbors classifier. The proposed approach is assessed using standard videos dataset that simulates patient fall. Also, we compare its performance with that of state-of-the-art fall detection techniques.
