With the increasing aging of the society,a series of consequences caused by falls of the elderly have become a serious medical problem and a real social *** at the fall behavior of the elderly,the research and develop...
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With the increasing aging of the society,a series of consequences caused by falls of the elderly have become a serious medical problem and a real social *** at the fall behavior of the elderly,the research and development of human fall recognition technology has practical application *** paper proposes a method to recognize human targets in indoor images using target detection technology,and then use the trained neural network based on Self-attention technique named Vision Transformer(ViT) to recognize the falling posture of the ***,the target detection model is optimized and trained for indoor scene,so that the model can accurately detect human targets in indoor scene;Next,the indoor scene human target image is preprocessed,and the indoor human pose image data set is constructed;Then,the data set is used to train the ViT depth network,and enable it to correctly classify five postures(standing,sitting,lying,bending and crawling).The experimental results show that ViT network achieves high accuracy and excellent generalization capacity for the above 5 human posture and can realize the recognition of elderly falls.
image quality assessment that aims at estimating the subject quality of images, builds models to evaluate the perceptual quality of the image in different applications. Based on the fact that the human visual system (...
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Tracking-by-detection is a popular tracking framework nowadays. The paradigm determines that detections will bring huge impact on the final tracking result. Based on the idea, to improve the tracking precision, we pro...
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Structural similarity (SSIM)-based distortion DSSIM is more consistent with human perception than the traditional mean squared error DMSE. To achieve better video quality, many studies on optimal bit allocation (OBA) ...
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Drawing on the idea that brain development is a Darwinian process of "evolution + selection" and the idea that the current state is a local equilibrium state of many bodies with self-organization and evoluti...
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The process of labeling samples costs time and resources but unlabeled samples are easier to obtain. Recently, graph-based deep semi-supervised learning (GDSSL) training a deep network using a small number of labeled ...
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ISBN:
(数字)9781728176871
ISBN:
(纸本)9781728176888
The process of labeling samples costs time and resources but unlabeled samples are easier to obtain. Recently, graph-based deep semi-supervised learning (GDSSL) training a deep network using a small number of labeled samples and the abundant unlabeled samples has been demonstrated to be promising on image classification tasks. These methods construct a graph to represent the structure of the input data (or hidden features). The successes of these GDSSL algorithms depend upon the structure of the similarity graph. However, existing GDSSL approaches construct the graph using predefined rules (such as knn graph) or fixed similarity measures (such as Gaussian kernel), which may limit the potential of GDSSL. In this paper, we move further in this direction to propose a novel end-to-end GDSSL approach which fully optimizes the whole graph without such limitations. To this end, we concatenate two neural networks (feature network and similarity network) together to learn the categorical label and semantic similarity, respectively, and train the networks with a new regularization term, the extended graph Laplacian, to minimize a unified objective function. Extensive experiments on several benchmark datasets demonstrate that our approach could outperform existing approaches on image classification. Furthermore, as a side-product, the similarity network could give faithful semantic similarity measure of samples, which is not possessed by other GDSSL approaches.
Addiction is a chronic and often relapsing brain disorder characterized by drug abuse and withdrawal symptoms and compulsive drug seeking(Koob and Volkow,2010)when access to the drug is *** leads to structural and fun...
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Addiction is a chronic and often relapsing brain disorder characterized by drug abuse and withdrawal symptoms and compulsive drug seeking(Koob and Volkow,2010)when access to the drug is *** leads to structural and functional brain changes implicated in reward,memory,motivation,and control(Volkow et al.,2019;Lüscher et al.,2020).
Deep convolution neural network (CNN) is one of the most popular Deep neural networks (DNN). It has won state-of-the-art performance in many computer vision tasks. The most used method to train DNN is Gradient descent...
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Recently, stereo matching from a pair of rectified images has been cast as a supervised learning task using the powerful representation of convolutional neural networks. However, existing methods only utilize last fea...
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Airway segmentation is critical for virtual bronchoscopy and computer-aided pulmonary disease analysis. In recent years, convolutional neural networks (CNNs) have been widely used to delineate the bronchial tree. Howe...
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