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CVPR 2007 Sharing

CVPR 2007 Sharing. Xin (Frank) Fan 2007.6.29. What are popular?. Dynamic (video-based) Vision Two of twelve oral sessions motion segmentation and tracking Detection, Matching, Tracking Five poster sessions Optical Flow and Tracking (1,2,3) Body Tracking, Gait, and Gesture (1,2)

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CVPR 2007 Sharing

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  1. CVPR 2007 Sharing Xin (Frank) Fan 2007.6.29

  2. What are popular? • Dynamic (video-based) Vision • Two of twelve oral sessions • motion segmentation and tracking • Detection, Matching, Tracking • Five poster sessions • Optical Flow and Tracking (1,2,3) • Body Tracking, Gait, and Gesture (1,2) • Applications • Human body • Surveillance, change detection • Medical image

  3. What are popular? • Learning generative/discriminative models • joint distribution VS. marginalized distribution • Graphical model VS. conditional random fields • Inference VS. Classifier (boosting, SVM) • Combining generative model and discriminative model • Some related papers • Li-Jia Li, G. Wang, and L. Fei-Fei, OPTIMOL: automatic Object Picture collecTion via Incremental MOdel Learning (Generative) • M. Tappen, C. Liu, W. Freeman, and E. Adelson, Learning Gaussian Conditional Random Fields for Low-Level Vision (Disc.) • S. Zheng, Zhuowen Tu, and Alan Yuille, Detecting Object Boundaries Using Low-, Mid-, and High-level Information (Comb) • Zhuowen Tu, Learning Generative Models via Discriminative Approaches (Comb)

  4. What are popular? • Blur and enhancement • One oral session • Solve enhancement problem from a CV view • Shengyang Dai, Mei Han, Wei Xu, Ying Wu, and Yihong Gong, Soft Edge Smoothness Prior for Alpha Channel Super Resolution • Jiaya Jia, Single Image Motion Deblurring Using Transparency • Amit Agrawal and Ramesh Raskar, Resolving Objects at Higher Resolution from a Single Motion-blurred Image

  5. Interesting papers • Marcus A. Brubaker et al. Physics-Based Person Tracking Using Simplified Lower-Body Dynamics • Share common idea with our work • Employ physics principle to define dynamics • Strength • A topic with broad interests in CVPR • Experiments and fancy demos

  6. Interesting papers • R. Memisevic and. G. Hinton, Unsupervised Learning of Image Transformations • Learn transformations of data • Modulated regression • Applications • Matching with invariant similarity • Image denoising • Image super-resolution

  7. What did I learn • Their commons • Theoretical formulation • Extensive experimental validations • Future work • Idea • Problem formulation • Experiments

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