Comparison of view-based object recognition algorithms using realistic 3D models

V. Blanz, B. Schölkopf, H. Bülthoff, C. Burges, V. Vapnik, T. Vetter

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

122 Citations (Scopus)

Abstract

Two view-based object recognition algorithms are compared: (1) a heuristic algorithm based on oriented filters, and (2) a support vector learning machine trained on low-resolution images of the objects. Classification performance is assessed using a high mlmber of images generated by a computer graphics system under precisely controlled conditions. Training- and test-images show a set of 25 realistic three-dimensional models of chairs from viewing directions spread over the upper half of the viewing sphere. The percentage of correct identification of all 25 objects is measured.

Original languageEnglish
Title of host publicationArtificial Neural Networks, ICANN 1996 - 1996 International Conference, Proceedings
PublisherSpringer Verlag
Pages251-256
Number of pages6
ISBN (Print)3540615105, 9783540615101
DOIs
Publication statusPublished - 1996
Event1996 International Conference on Artificial Neural Networks, ICANN 1996 - Bochum, Germany
Duration: 1996 Jul 161996 Jul 19

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1112 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference1996 International Conference on Artificial Neural Networks, ICANN 1996
CountryGermany
CityBochum
Period96/7/1696/7/19

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Blanz, V., Schölkopf, B., Bülthoff, H., Burges, C., Vapnik, V., & Vetter, T. (1996). Comparison of view-based object recognition algorithms using realistic 3D models. In Artificial Neural Networks, ICANN 1996 - 1996 International Conference, Proceedings (pp. 251-256). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1112 LNCS). Springer Verlag. https://doi.org/10.1007/3-540-61510-5_45