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ROMAN
2007
IEEE
179views Robotics» more  ROMAN 2007»
13 years 11 months ago
A Bayesian Network Framework for Vision Based Semantic Scene Understanding
— For a robot to understand a scene, we have to infer and extract meaningful information from vision sensor data. Since scene understanding consists in recognizing several visual...
Seung-Bin Im, Keum-Sung Hwang, Sung-Bae Clio
ACIVS
2006
Springer
13 years 8 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...
Seung-Bin Im, Sung-Bae Cho
ICCV
2011
IEEE
12 years 4 months ago
Manhattan Scene Understanding Using Monocular, Stereo, and 3D Features
This paper addresses scene understanding in the context of a moving camera, integrating semantic reasoning ideas from monocular vision with 3D information available through struct...
Alex Flint, David Murray, Ian Reid
CVPR
2004
IEEE
13 years 8 months ago
A Probabilistic Approach to Image Orientation Detection via Confidence-Based Integration of Low-Level and Semantic Cues
Automatic image orientation detection for natural images is a useful, yet challenging research area. Humans use scene context and semantic object recognition to identify the corre...
Jiebo Luo, Matthew R. Boutell
CVPR
2004
IEEE
14 years 6 months ago
Bayesian Fusion of Camera Metadata Cues in Semantic Scene Classification
Semantic scene classification based only on low-level vision cues has had limited success on unconstrained image sets. On the other hand, camera metadata related to capture condit...
Matthew R. Boutell, Jiebo Luo