Acquiring robust representations for recognition from image sequences

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

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 andre cognition. The system creates sparse representations and shows good recognition performance in a variety of viewing conditions for a database of natural image sequences.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages216-222
Number of pages7
Volume2191
ISBN (Print)3540425969
Publication statusPublished - 2001
Externally publishedYes
Event23rd German Association for Pattern Recognition Symposium, DAGM 2001 - Munich, Germany
Duration: 2001 Sep 122001 Sep 14

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2191
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other23rd German Association for Pattern Recognition Symposium, DAGM 2001
CountryGermany
CityMunich
Period01/9/1201/9/14

Fingerprint

Object recognition
Image Sequence
Sparse Representation
Object Recognition
Cognition
Learning
Framework

Keywords

  • Appearance-based learning
  • Model acquisition
  • Object recognition

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Wallraven, C., & Bulthoff, H. (2001). Acquiring robust representations for recognition from image sequences. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2191, pp. 216-222). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2191). Springer Verlag.

Acquiring robust representations for recognition from image sequences. / Wallraven, Christian; Bulthoff, Heinrich.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 2191 Springer Verlag, 2001. p. 216-222 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2191).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Wallraven, C & Bulthoff, H 2001, Acquiring robust representations for recognition from image sequences. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 2191, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 2191, Springer Verlag, pp. 216-222, 23rd German Association for Pattern Recognition Symposium, DAGM 2001, Munich, Germany, 01/9/12.
Wallraven C, Bulthoff H. Acquiring robust representations for recognition from image sequences. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 2191. Springer Verlag. 2001. p. 216-222. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
Wallraven, Christian ; Bulthoff, Heinrich. / Acquiring robust representations for recognition from image sequences. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 2191 Springer Verlag, 2001. pp. 216-222 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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