Titlebook: Computer Vision - ACCV 2014 Workshops; Singapore, Singapore C.V. Jawahar,Shiguang Shan Conference proceedings 2015 Springer International P

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Object Recognition in 3D Point Cloud of Urban Street SceneiDAR laser scanner. An important problem in object recognition is the need for sufficient labeled training data to learn robust classifiers. In this paper we show how to significantly reduce the need for manually labeled training data by reduction of scene complexity using non-supervised ground and

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Depth-Based Real-Time Hand Tracking with Occlusion Handling Using Kalman Filter and DAM-Shiftift) algorithm for occlusion handling. DAM-Shift is a useful algorithm for hand tracking, but difficult to track when occlusion occurs. To detect the hand region, we use a classifier that combines a boosting and a cascade structure. To verify occlusion, we predict in real time the center position of

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Computer Vision - ACCV 2014 Workshops978-3-319-16628-5Series ISSN 0302-9743 Series E-ISSN 1611-3349

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Linnaeus and the Four Corners of the Worlding body shapes as silhouettes averaged over gait cycles. Our method, however, captures geometric properties of the silhouettes boundaries. Namely, we evaluate contour curvatures locally using Gauss maps. This results in an improved shape representation, as contrasted to average silhouettes. In addi

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https://doi.org/10.1057/9781137338211 color, upper body position and flow information. We apply our hand detection results to perform fine-grained human action recognition. We demonstrate that motion features extracted from hand areas can help classify actions even when they look familiar and they are associated with visually similar o