@inproceedings{fe32adb654234ef799a49ee4b7d27623,
title = "Context-based scene recognition using bayesian networks with scale-invariant feature transform",
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.",
author = "Im, {Seung Bin} and Cho, {Sung Bae}",
year = "2006",
doi = "10.1007/11864349_98",
language = "English",
isbn = "3540446303",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "1080--1087",
booktitle = "Advanced Concepts for Intelligent Vision Systems - 8th International Conference, ACIVS 2006, Proceedings",
address = "Germany",
note = "8th International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2006 ; Conference date: 18-09-2006 Through 21-09-2006",
}