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Conference Paper

Acquiring Robust Representations for Recognition from Image Sequences

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84298

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

http://pubman.mpdl.mpg.de/cone/persons/resource/persons83839

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

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Wallraven, C., & Bülthoff, H. (2001). Acquiring Robust Representations for Recognition from Image Sequences. DAGM-Symposium München 2001, 216-222.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-E20E-C
Abstract
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.