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ACIVS
2006
Springer

Context-Based Scene Recognition Using Bayesian Networks with Scale-Invariant Feature Transform

9 years 2 months ago
Context-Based Scene Recognition Using Bayesian Networks with Scale-Invariant Feature Transform
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.
Seung-Bin Im, Sung-Bae Cho
Added 20 Aug 2010
Updated 20 Aug 2010
Type Conference
Year 2006
Where ACIVS
Authors Seung-Bin Im, Sung-Bae Cho
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