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  Automatic acquisition of exemplar-based representations for recognition from image sequences

Wallraven, C., & Bülthoff, H. (2001). Automatic acquisition of exemplar-based representations for recognition from image sequences. In T. Kanade, & T. Sim (Eds.), CVPR 2001 Workshop on Models versus Exemplars in Computer Vision (pp. 1-9).

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-E160-5 Version Permalink: http://hdl.handle.net/21.11116/0000-0005-A84D-8
Genre: Conference Paper

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 Creators:
Wallraven, C1, 2, Author              
Bülthoff, HH1, 2, Author              
Affiliations:
1Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497797              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: We present an exemplar-based object recognition system which is capable of on-line learning of representations of scenes and objects from image sequences. Local appearance features are used in a tracking framework to find `key-frames' of the input sequence during learning. The representation of the stored sequences which are used for recognition of novel images consists only of the appearance features in these key-frames and contains no further a-priori assumptions about the underlying sequences. The system is able to create sparse and extendable representations and shows good recognition performance in a variety of viewing conditions for databases of natural and synthetic image sequences.

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 Dates: 2001-12
 Publication Status: Published online
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 Rev. Type: -
 Identifiers: BibTex Citekey: 952
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Title: CVPR 2001 Workshop on Models versus Exemplars in Computer Vision
Place of Event: Kauai, HI, USA
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Title: CVPR 2001 Workshop on Models versus Exemplars in Computer Vision
Source Genre: Proceedings
 Creator(s):
Kanade, T, Editor
Sim, T, Editor
Affiliations:
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Publ. Info: -
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 1 - 9 Identifier: -