'Zisserman' presentation slideshows

O BJ C UT & Pose Cut CVPR 05 ECCV 06

UNIVERSITY OF OXFORD. O BJ C UT & Pose Cut CVPR 05 ECCV 06. Philip Torr M. Pawan Kumar, Pushmeet Kohli and Andrew Zisserman. Conclusion. Combining pose inference and segmentation worth investigating. (tommorrow) Tracking = Detection Detection = Segmentation

1.06k views • 88 slides

Structure from motion Class 9

831 views • 47 slides

776 Computer Vision

776 Computer Vision. Jan-Michael Frahm Spring 2012. Image alignment. Image from http://graphics.cs.cmu.edu/courses/15-463/2010_fall/. A look into the past. http://blog.flickr.net/en/2010/01/27/a-look-into-the-past/. A look into the past. http://komen-dant.livejournal.com/345684.html.

580 views • 45 slides

Single-view metrology

Single-view metrology. Magritte, Personal Values , 1952. Many slides from S. Seitz, D. Hoiem. Vertical vanishing point (at infinity). Vanishing line. Vanishing point. Vanishing point. Camera calibration revisited. What if world coordinates of reference 3D points are not known?

807 views • 28 slides

BING: Binarized Normed Gradients for Objectness Estimation at 300fps

BING: Binarized Normed Gradients for Objectness Estimation at 300fps. Ming-Ming Cheng 1 Ziming Zhang 2 Wen-Yan Li 1 Philip H. S. Torr 1 1 Torr Vision Group, Oxford University 2 Boston University. Motivation: Generic object detection. Motivation: What is an object?.

713 views • 23 slides

Support Vector Machines

307 views • 18 slides