Context-Based Vision: Recognizing Objects Using Information from Both 2D and 3D Imagery
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part I
A Parametric Texture Model Based on Joint Statistics of Complex Wavelet Coefficients
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
Decision Making and Uncertainty Management in a 3D Reconstruction System
IEEE Transactions on Pattern Analysis and Machine Intelligence
Context-based vision system for place and object recognition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Bayesian approach to sensor-based context awareness
Personal and Ubiquitous Computing
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Layered representations for learning and inferring office activity from multiple sensory channels
Computer Vision and Image Understanding - Special issue on event detection in video
Learning Bayesian Networks
A Bayesian network-based framework for semantic image understanding
Pattern Recognition
Handheld AR indoor guidance system using vision technique
Proceedings of the 2007 ACM symposium on Virtual reality software and technology
ConVeS: a context verification framework for object recognition system
Proceedings of the 2009 conference on Information Science, Technology and Applications
Environment adapted active multi-focal vision system for object detection
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
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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 objects-existence 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.