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Titlebook: Computer Vision - ACCV 2012 Workshops; ACCV 2012 Internatio Jong-Il Park,Junmo Kim Conference proceedings 2013 Springer-Verlag Berlin Heide

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11#
發(fā)表于 2025-3-23 12:55:35 | 只看該作者
12#
發(fā)表于 2025-3-23 14:46:28 | 只看該作者
Paul Corrigan,Mike Hayes,Paul Joyce projecting” 2D contours extracted in the depth map. As our experimental results demonstrate, the proposed approach significantly outperforms the state-of-the-art 2D approaches, in particular, latent SVM object detector, as well as recently proposed approaches for object detection in RGB-D data.
13#
發(fā)表于 2025-3-23 18:24:51 | 只看該作者
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發(fā)表于 2025-3-23 23:01:13 | 只看該作者
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發(fā)表于 2025-3-24 04:34:50 | 只看該作者
16#
發(fā)表于 2025-3-24 08:04:50 | 只看該作者
View-Invariant Object Detection by Matching 3D Contours projecting” 2D contours extracted in the depth map. As our experimental results demonstrate, the proposed approach significantly outperforms the state-of-the-art 2D approaches, in particular, latent SVM object detector, as well as recently proposed approaches for object detection in RGB-D data.
17#
發(fā)表于 2025-3-24 13:37:16 | 只看該作者
Human Detection with Occlusion Handling by Over-Segmentation and Clustering on Foreground Regions3D data of the foreground regions using a “split-merge” approach. Over-segmentation and clustering are preformed on foreground regions followed by the height validation. Experimental results demonstrate that the proposed method outperforms two state-of-art human detection methods.
18#
發(fā)表于 2025-3-24 17:22:52 | 只看該作者
Conference proceedings 2013op on Background Models Challenge. LNCS 7729 contains the papers selected for the Workshop on e-Heritage, the Workshop on Color Depth Fusion in Computer Vision, the Workshop on Face Analysis, the Workshop on Detection and Tracking in Challenging Environments, and the International Workshop on Intelligent Mobile Vision.
19#
發(fā)表于 2025-3-24 20:16:33 | 只看該作者
https://doi.org/10.1057/9780230236660s in the training set. For evaluation, a new dataset representing 60 coin classes of the Roman Republican period is used. The proposed system achieves a classification rate of 83.3 % and a runtime improvement of 93 % through the coarse-to-fine classification.
20#
發(fā)表于 2025-3-25 02:17:22 | 只看該作者
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