Acquiring robust representations for recognition from image sequences

Christian Wallraven, Heinrich Bulthoff

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

Keywords

  • Appearance-based learning
  • Model acquisition
  • Object recognition

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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