Overview
- Editors:
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Azriel Rosenfeld
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University of Maryland, College Park, USA
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David Doermann
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University of Maryland, College Park, USA
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Daniel DeMenthon
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University of Maryland, College Park, USA
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Table of contents (11 chapters)
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- Arnon Amir, Savitha Srinivasan, Dulce Ponceleon
Pages 1-30
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- Aya Aner-Wolf, John R. Kender
Pages 31-60
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- Nevenka Dimitrova, Lalitha Agnihotri, Radu Jasinschi
Pages 61-90
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- Ajay Divakaran, Kadir A. Peker, Regunathan Radhakrishnan, Ziyou Xiong, Romain Cabasson
Pages 91-121
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- Ying Li, Shrikanth Narayanan, C.-C. Jay Kuo
Pages 123-154
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- Zeeshan Rasheed, Mubarak Shah
Pages 185-217
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- Malcolm Slaney, Dulce Ponceleon, James Kaufman
Pages 219-252
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- John R. Smith, Ching-Yung Lin, Milind Naphade, Apostol Paul Natsev, Belle Tseng
Pages 253-277
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- Lexing Xie, Shih-Fu Chang, Ajay Divakaran, Huifang Sun
Pages 279-307
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- Rong Yan, Alexander G. Hauptmann, Rong Jin
Pages 309-338
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Back Matter
Pages 339-340
About this book
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 analyzing image sequences, or video Video understanding deals with understanding of video understanding. sequences, e.g., recognition of gestures, activities, facial 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 obvi ous overlap with computer vision. The main goal of computer graphics is to generate and animate realistic looking images, and videos. Re searchers in computer graphics are increasingly employing techniques from computer vision to generate the synthetic imagery. A good exam pIe of this is image-based rendering and modeling techniques, in which geometry, appearance, and lighting is derived from real images using computer vision techniques. Here the shift is from synthesis to analy sis followed by synthesis. Image processing has always overlapped with computer vision because they both inherently work directly with images.
