Deep_Saliency: A Deep Learning based Saliency Approach to detect Covid-19 through x-ray images

Covid-19 a small virus has created a havoc in the world. The pandemic has already taken over 4 lakh lives. The tests to detect a Covid-19 positive takes time and is costly. Moreover, the ability of the virus to mutate surprises the doctors every day. Present paper proposes a saliency-based model called Deep_Saliency. The model works on chest x-rays of healthy, unhealthy, and covid-19 patients. An x-ray repository of Covid-19, available in public domain, is taken for the study. Deep_Saliency uses visual, disparity, and motion saliency to create a feature dataset of the x-rays. The collected features are tested and trained using Long Short-Term Memory (LSTM) network. A predictive analysis is performed using the x-ray of a new patient to confirm a Covid-19 positive case. The first objective of the paper is to detect Covid-19 positive cases from x-rays. The other objective is to provide a benchmark dataset of biomarkers. The proposed work achieved an accuracy of 96.66%.

Deep_Saliency: A Deep Learning based Saliency Approach to detect Covid-19 through x-ray images

Covid-19 a small virus has created a havoc in the world. The pandemic has already taken over 4 lakh lives. The tests to detect a Covid-19 positive takes time and is costly. Moreover, the ability of the virus to mutate surprises the doctors every day. Present paper proposes a saliency-based model called Deep_Saliency. The model works on chest x-rays of healthy, unhealthy, and covid-19 patients. An x-ray repository of Covid-19, available in public domain, is taken for the study. Deep_Saliency uses visual, disparity, and motion saliency to create a feature dataset of the x-rays. The collected features are tested and trained using Long Short-Term Memory (LSTM) network. A predictive analysis is performed using the x-ray of a new patient to confirm a Covid-19 positive case. The first objective of the paper is to detect Covid-19 positive cases from x-rays. The other objective is to provide a benchmark dataset of biomarkers. The proposed work achieved an accuracy of 96.66%.

Motion Saliency Detection for Surveillance Systems Using Streaming Dynamic Mode Decomposition

Intelligent surveillance systems enable secured visibility features in the smart city era. One of the major models for pre-processing in intelligent surveillance systems is known as saliency detection, which provides facilities for multiple tasks such as object detection, object segmentation, video coding, image re-targeting, image-quality assessment, and image compression. Traditional models focus on improving detection accuracy at the cost of high complexity. However, these models are computationally expensive for real-world systems. To cope with this issue, we propose a fast-motion saliency method for surveillance systems under various background conditions. Our method is derived from streaming dynamic mode decomposition (s-DMD), which is a powerful tool in data science. First, DMD computes a set of modes in a streaming manner to derive spatial–temporal features, and a raw saliency map is generated from the sparse reconstruction process. Second, the final saliency map is refined using a difference-of-Gaussians filter in the frequency domain. The effectiveness of the proposed method is validated on a standard benchmark dataset. The experimental results show that the proposed method achieves competitive accuracy with lower complexity than state-of-the-art methods, which satisfies requirements in real-time applications.

Moving Object Detection under a Moving Camera via Background Orientation Reconstruction

Moving object detection under a moving camera is a challenging question, especially in a complex background. This paper proposes a background orientation field reconstruction method based on Poisson fusion for detecting moving objects under a moving camera. As enlightening by the optical flow orientation of a background is not dependent on the scene depth, this paper reconstructs the background orientation through Poisson fusion based on the modified gradient. Then, the motion saliency map is calculated by the difference between the original and the reconstructed orientation field. Based on the similarity in appearance and motion, the paper also proposes a weighted accumulation enhancement method. It can highlight the motion saliency of the moving objects and improve the consistency within the object and background region simultaneously. Furthermore, the proposed method incorporates the motion continuity to reject the false positives. The experimental results obtained by employing publicly available datasets indicate that the proposed method can achieve excellent performance compared with current state-of-the-art methods.