'Optical flow estimation' presentation slideshows

Analysis of Contour Motions

Neural Information Processing Systems 2006. Analysis of Contour Motions. Ce Liu William T. Freeman Edward H. Adelson Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Visual Motion Analysis in Computer Vision. Motion analysis is essential in

814 views • 72 slides

Ming Ye Electrical Engineering, University of Washington mingye@u.washington

Robust Visual Motion Analysis: Piecewise-Smooth Optical Flow and Motion-Based Detection and Tracking. Ming Ye Electrical Engineering, University of Washington mingye@u.washington.edu. What Is Visual Motion. 2D image velocity 3D motion projection Temporal correspondence Image deformation

698 views • 54 slides

Video Matting

Ehsan Shahrian , Brian Price. Video Matting. Video Matting. - Difference in input. Video Matting. - Difference in input. Video Matting. - Coherence. Video Matting. - Introduction. Most video methods are extensions of image methods Common extensions Propagate/generate trimap

576 views • 26 slides

Optical flow and Tracking

399 views • 26 slides

Learning color and locality cues for moving object detection and segmentation

Learning color and locality cues for moving object detection and segmentation. Feng Liu and Michael Gleicher University of Wisconsin-Madison CVPR 2009. Yuan-Hao Lai. [Background subtraction] Detect/segment a moving object From monocular video Object motion is sparse and insufficient.

371 views • 20 slides

Optical Flow

Optical Flow. 10-24-2005. Problem. Problems in motion estimation Noise, color (intensity) smoothness, lighting (shadowing effects), occlusion, abrupt movements, etc Approaches: Block matching, Generalized block matching, Optical flow (block-based, Horn-Schunck etc) Bayesian, etc.

253 views • 11 slides

Optical Flow

Optical Flow. 10-24-2005. Problem. Problems in motion estimation Noise, color (intensity) smoothness, lighting (shadowing effects), occlusion, abrupt movements, etc Approaches: Block matching, Generalized block matching, Optical flow (block-based, Horn-Schunck etc) Bayesian, etc.

218 views • 11 slides

Analysis of Contour Motions Ce Liu William T. Freeman Edward H. Adelson

Two-step inference Contour grouping: set switch variables to optimize Pr( S ; B , O ) (hard) Global motion given grouping (easy, least squares) Importance sampling to estimate the marginal of the switch variables Bidirectional proposal density Use marginals to obtain a best grouping.

256 views • 1 slides