1 / 7

Advances in Dynamic Vision: Motion Segmentation, Tracking, and Generative Models

This CVPR 2007 presentation explores significant trends in dynamic vision, focusing on motion segmentation and tracking techniques. It covers oral sessions on detection, matching, and applications in surveillance and medical imaging. The discussion includes generative and discriminative models, including graphical models and conditional random fields. Important works, such as learning Gaussian conditional random fields and physics-based person tracking, are highlighted. The presentation also emphasizes experimental validations and future work in the domain, providing insights into image enhancements and transformations.

todd-moon
Télécharger la présentation

Advances in Dynamic Vision: Motion Segmentation, Tracking, and Generative Models

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related