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|a 10.1007/978-1-4615-0459-7
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|a Video Registration
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|c edited by Mubarak Shah, Rakesh Kumar.
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|a 1st ed. 2003.
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|a New York, NY :
|b Springer US :
|b Imprint: Springer,
|c 2003.
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|a XIV, 257 p.
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|a The International Series in Video Computing,
|x 1571-5205 ;
|v 5
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|a Springer eBook Collection
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|a 1 Video Registration: A Perspective -- 2 Automatic Camera Tracking -- 3 Motion Information in the Phase Domain -- 4 Parallel-Perspective Stereo Mosaics -- 5 Model-Based Landmark Extraction and Correspondence Finding for Aerial Image Registration -- 6 Airborne Video Registration for Activity Monitoring -- 7 Geodetic Alignment of Aerial Video Frames -- 8 Robust Video Georegistration -- 9 Video Registration Panel: Key Challenges and the potential impact of their solution to the field of computer vision -- 10 Index.
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| 520 |
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|a 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.
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|a Electronic access restricted to members of the Holy Cross Community.
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|a Application software.
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|a Optical data processing.
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|a Signal processing.
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|a Image processing.
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|a Speech processing systems.
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