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Acquiring Robust Representations for Recognition from Image Sequences

MPG-Autoren
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Wallraven,  C
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Bülthoff,  HH
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

Externe Ressourcen

https://link.springer.com/content/pdf/10.1007%2F3-540-45404-7_29.pdf
(Verlagsversion)

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Zitation

Wallraven, C., & Bülthoff, H. (2001). Acquiring Robust Representations for Recognition from Image Sequences. In B. Radig, & S. Florczyk (Eds.), Pattern Recognition: 23rd DAGM Symposium Munich, Germany, September 12–14, 2001 (pp. 216-222). Berlin, Germany: Springer.


Zitierlink: https://hdl.handle.net/11858/00-001M-0000-0013-E20E-C
Zusammenfassung
We present an object recognition system which is capable of on-line learning of representations of scenes and objects from natural image sequences. Local appearance features are used in a tracking framework to find ‘key-frames’ of the input sequence during learning. In addition, the same basic framework is used for both learning and recognition. The system creates sparse representations and shows good recognition performance in a variety of viewing conditions for a database of natural image sequences.