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Titlebook: Energy Minimization Methods in Computer Vision and Pattern Recognition; 7th International Co Daniel Cremers,Yuri Boykov,Frank R. Schmidt Co

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樓主: emanate
21#
發(fā)表于 2025-3-25 05:52:35 | 只看該作者
Hierarchical Pairwise Segmentation Using Dominant Sets and Anisotropic Diffusion Kernelsiffusion kernel allows for a scale variation for the extraction of the segments, thus enforcing separations on strong boundaries at a high level of the hierarchy. Experimental results on the standard Berkeley database [9] show the effectiveness of the approach.
22#
發(fā)表于 2025-3-25 10:30:08 | 只看該作者
23#
發(fā)表于 2025-3-25 15:30:17 | 只看該作者
Pose-Invariant Face Matching Using MRF Energy Minimization Frameworkber of innovations, such as a dynamic block size and block shape adaptation, as well as label pruning and error prewhitening measures have been introduced to increase the effectiveness of the approach. The experimental evaluation of the method is performed on the rotation shots of the XM2VTS database and promising results are obtained.
24#
發(fā)表于 2025-3-25 16:48:42 | 只看該作者
A Local Normal-Based Region Term for Active Contours to the contour. Mathematical derivations are performed on an explicit curve, leading to a form allowing efficient implementation on a parametric snake. However, we provide implementations on both explicit and implicit contours.
25#
發(fā)表于 2025-3-25 21:05:18 | 只看該作者
Complementary Optic Flowng a strong filling-in effect orthogonal to it. This allows optimal complementarity between both terms and avoids undesirable interference. The high quality of our complementary optic flow (COF) approach is demonstrated by the current top ranking result at the Middlebury benchmark.
26#
發(fā)表于 2025-3-26 00:43:38 | 只看該作者
27#
發(fā)表于 2025-3-26 07:08:04 | 只看該作者
28#
發(fā)表于 2025-3-26 11:39:41 | 只看該作者
29#
發(fā)表于 2025-3-26 15:58:52 | 只看該作者
30#
發(fā)表于 2025-3-26 18:47:11 | 只看該作者
Three Dimensional Monocular Human Motion Analysis in End-Effector Spacesolver allows us to perform the filtering in end-effector space. This effectively reduces the dimensionality of the state space while still allowing for the estimation of a large set of motions. Preliminary experiments with the strategy show good results compared to a full-pose tracker.
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