Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting Moving Shadows: Algorithms and Evaluation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Moving Shadow Detection using a Combined Geometric and Color Classification Approach
WACV-MOTION '05 Proceedings of the IEEE Workshop on Motion and Video Computing (WACV/MOTION'05) - Volume 2 - Volume 02
Shadow Flow: A Recursive Method to Learn Moving Cast Shadows
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Proceedings of the 2008 ACM symposium on Virtual reality software and technology
Shadow Detection in Dynamic Scenes Using Dense Stereo Information and an Outdoor Illumination Model
Dyn3D '09 Proceedings of the DAGM 2009 Workshop on Dynamic 3D Imaging
Gait Recognition Using Shadow Analysis
BLISS '09 Proceedings of the 2009 Symposium on Bio-inspired Learning and Intelligent Systems for Security
Non-parametric background and shadow modeling for object detection
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
Gait identification based on multi-view observations using omnidirectional camera
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
Adaptive shadow estimator for removing shadow of moving object
Computer Vision and Image Understanding
Detecting moving objects, ghosts, and shadows in video streams
IEEE Transactions on Pattern Analysis and Machine Intelligence
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A static binocular camera system is widely used in many computer vision applications; and being able to segment foreground, shadow, and background is an important problem for them. In this paper, we propose a homography-correspondence pair-based segmentation framework. Existing segmentation approaches, based on homography constraints, often suffer from occlusion problems. In our approach, we treat a homography-correspondence pair symmetrically, to explicitly take the occlusion relationship into account, and we regard the segmentation problem as a multi-labeling problem for the homography-correspondence pair. We then formulate an energy function for this problem and get the pair-wise segmentation results by minimizing them via an α-β swap algorithm. Experimental results show that accurate segmentation is obtained in the presence of the occlusion region in each side image.