gustation 发表于 2025-3-28 15:03:23
http://reply.papertrans.cn/24/2342/234104/234104_41.pngIntuitive 发表于 2025-3-28 21:45:34
http://reply.papertrans.cn/24/2342/234104/234104_42.pngOTTER 发表于 2025-3-28 23:55:33
Generic Object Class Detection Using Boosted Configurations of Oriented Edgesble configurations of oriented edges. An ensemble of such configurations is learnt in a boosting framework. Each edge configuration can capture some local . shape property of the target class and the representation is thus . to representing and detecting visual classes that have distinctive local stFactorable 发表于 2025-3-29 05:39:45
http://reply.papertrans.cn/24/2342/234104/234104_44.pngORE 发表于 2025-3-29 07:45:40
http://reply.papertrans.cn/24/2342/234104/234104_45.pngellagic-acid 发表于 2025-3-29 13:36:45
http://reply.papertrans.cn/24/2342/234104/234104_46.png思想上升 发表于 2025-3-29 18:05:41
http://reply.papertrans.cn/24/2342/234104/234104_47.png缓解 发表于 2025-3-29 20:30:08
Multi-Target Tracking by Learning Class-Specific and Instance-Specific Cuesled Data-Driven Particle Filtering (DDPF). The learned . include an online learned geometrical model for excluding detection outliers that violate geometrical constraints, global pose estimators shared by all targets for particle refinement, and online Boosting based appearance models which select d把手 发表于 2025-3-30 01:42:02
Modeling Complex Scenes for Accurate Moving Objects Segmentationround subtraction. We propose an online and unsupervised technique to find optimal segmentation in a Markov Random Field (MRF) framework. To improve the accuracy, color, locality, temporal coherence and spatial consistency are fused together in the framework. The models of color, locality and temporCOLIC 发表于 2025-3-30 04:22:43
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