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 language | English |
|---|---|
| Title of host publication | Advanced Concepts for Intelligent Vision Systems - 8th International Conference, ACIVS 2006, Proceedings |
| Publisher | Springer Verlag |
| Pages | 1080-1087 |
| Number of pages | 8 |
| Volume | 4179 LNCS |
| ISBN (Print) | 3540446303, 9783540446309 |
| Publication status | Published - 2006 Jan 1 |
| Event | 8th International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2006 - Antwerp, Belgium Duration: 2006 Sep 18 → 2006 Sep 21 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 4179 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Other
| Other | 8th International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2006 |
|---|---|
| Country | Belgium |
| City | Antwerp |
| Period | 06/9/18 → 06/9/21 |
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All Science Journal Classification (ASJC) codes
- Computer Science(all)
- Theoretical Computer Science
Cite this
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Context-based scene recognition using bayesian networks with scale-invariant feature transform. / Im, Seung Bin; Cho, Sung Bae.
Advanced Concepts for Intelligent Vision Systems - 8th International Conference, ACIVS 2006, Proceedings. Vol. 4179 LNCS Springer Verlag, 2006. p. 1080-1087 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4179 LNCS).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Context-based scene recognition using bayesian networks with scale-invariant feature transform
AU - Im, Seung Bin
AU - Cho, Sung Bae
PY - 2006/1/1
Y1 - 2006/1/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=33750266379&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33750266379&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:33750266379
SN - 3540446303
SN - 9783540446309
VL - 4179 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 1080
EP - 1087
BT - Advanced Concepts for Intelligent Vision Systems - 8th International Conference, ACIVS 2006, Proceedings
PB - Springer Verlag
ER -