Video Registration

1. Front Matter

   Pages i-xiv

   PDF

2. Video Registration: A Perspective

   • Mubarak Shah, Rakesh Kumar

   Pages 1-17

3. Automatic Camera Tracking

   • Andrew W. Fitzgibbon, Andrew Zisserman

   Pages 18-35

4. Motion Information in the Phase Domain

   • Hassan Foroosh, W. Scott Hoge

   Pages 36-71

5. Generation and Error Characterization of Pararell-Perspective Stereo Mosaics from Real Video

   • Zhigang Zhu, Allen R. Hanson, Howard Schultz, Edward M. Riseman

   Pages 72-105

6. Model-Based Landmark Extraction and Correspondence Finding for Aerial Image Registration

   • Karl Rohr, Christian Drewniok

   Pages 106-129

7. Airborne Video Registration for Activity Monitoring

   • Chandra Shekhar, Rama Chellappa

   Pages 130-143

8. Geodetic Alignment of Aerial Video Frames

   • Y. Sheikh, S. Khan, M. Shah, Richard Cannata

   Pages 144-179

9. Robust Video Georegistration in the Presence of Significant Appearance Changes

   • B. Matei, R. P. Wildes, S. Hsu, R. Kumar, S. Samarasekera, H. S. Sawhney et al.

   Pages 180-221

10. Panel Introduction Video Registration: Key Challenges and the Potential Impact of their Solutions to the Field of Computer Vision

    • Mubarak Shah, Lisa Brown, Harpreet S. Sawhney, Richard Szeliski

    Pages 222-255

11. Back Matter

    Pages 256-257

    PDF

Traditionally, scientific fields have defined boundaries, and scientists work on research problems within those boundaries. However, from time to time those boundaries get shifted or blurred to evolve new fields. For instance, the original goal of computer vision was to understand a single image of a scene, by identifying objects, their structure, and spatial arrangements. This has been referred to as image understanding. Recently, computer vision has gradually been making the transition away from understanding single images to analyz­ ing image sequences, or video understanding. Video understanding deals with understanding of video sequences, e. g. , recognition of gestures, activities, fa­ cial expressions, etc. The main shift in the classic paradigm has been from the recognition of static objects in the scene to motion-based recognition of actions and events. Video understanding has overlapping research problems with other fields, therefore blurring the fixed boundaries. Computer graphics, image processing, and video databases have obvious overlap with computer vision. The main goal of computer graphics is to gener­ ate and animate realistic looking images, and videos. Researchers in computer graphics are increasingly employing techniques from computer vision to gener­ ate the synthetic imagery. A good example of this is image-based rendering and modeling techniques, in which geometry, appearance, and lighting is de­ rived from real images using computer vision techniques. Here the shift is from synthesis to analysis followed by synthesis.

• DOM
• Stereo
• communication
• image processing
• information
• multimedia
• robotics
• video

• Department of Computer Science, University of Central Florida, Orlando, USA

  Mubarak Shah

• Sarnoff Corporation CN5300, Princeton, USA

  Rakesh Kumar

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