Context-based scene recognition using bayesian networks with scale-invariant feature transform

Seung Bin Im, Sung Bae Cho

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

10 Citations (Scopus)

Abstract

Scene understanding is an important problem in intelligent robotics. Since visual information is uncertain due to several reasons, we need a novel method that has robustness to the uncertainty. Bayesian probabilistic approach is robust to manage the uncertainty, and powerful to model high-level contexts like the relationship between places and objects. In this paper, we propose a context-based Bayesian method with SIFT for scene understanding. At first, image pre-processing extracts features from vision information and objectsexistence information is extracted by SIFT that is rotation and scale invariant. This information is provided to Bayesian networks for robust inference in scene understanding. Experiments in complex real environments show that the proposed method is useful.

Original languageEnglish
Title of host publicationAdvanced Concepts for Intelligent Vision Systems - 8th International Conference, ACIVS 2006, Proceedings
PublisherSpringer Verlag
Pages1080-1087
Number of pages8
Volume4179 LNCS
ISBN (Print)3540446303, 9783540446309
Publication statusPublished - 2006 Jan 1
Event8th International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2006 - Antwerp, Belgium
Duration: 2006 Sep 182006 Sep 21

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4179 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other8th International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2006
CountryBelgium
CityAntwerp
Period06/9/1806/9/21

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All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Im, S. B., & Cho, S. B. (2006). Context-based scene recognition using bayesian networks with scale-invariant feature transform. In Advanced Concepts for Intelligent Vision Systems - 8th International Conference, ACIVS 2006, Proceedings (Vol. 4179 LNCS, pp. 1080-1087). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4179 LNCS). Springer Verlag.