Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
Level Set Evolution without Re-Initialization: A New Variational Formulation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Graph Cuts and Efficient N-D Image Segmentation
International Journal of Computer Vision
Bilayer Segmentation of Live Video
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
A Framework for Evaluating Video Object Segmentation Algorithms
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
A Closed-Form Solution to Natural Image Matting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust foreground extraction technique using Gaussian family model and multiple thresholds
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
Motion-based background subtraction using adaptive kernel density estimation
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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We propose a region-based method to extract semantic foreground regions from color video sequences with static backgrounds. First, we introduce a new distance measure for background subtraction which is robust against shadows. Then the foreground region is extracted with a graph-based region segmentation method considering background difference and spatial homogeneity. For efficient computation, the graph structure is optimized by the minimum spanning tree before segmentation. The main contribution is that the proposed algorithm improves on conventional approaches especially in strong shadow regions and does not require manual initialization. We have verified through experiments and comparison to state of the art methods that the proposed algorithm works well with various cameras and environment.