IEEE Transactions on Pattern Analysis and Machine Intelligence
Region-based parametric motion segmentation using color information
Graphical Models and Image Processing
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Cooperative Algorithm for Stereo Matching and Occlusion Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
Accurate Motion Layer Segmentation and Matting
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Motion Layer Extraction in the Presence of Occlusion Using Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
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We propose to tackle the optical flow problem by a combination of two recent advances in the computation of dense correspondences, namely the incorporation of image segmentation and robust global optimization via graph-cuts. In the first step, each segment (extracted by colour segmentation) is assigned to an affine motion model from a set of sparse correspondences. Using a layered model, we then identify those motion models that represent the dominant image motion. This layer extraction task is accomplished by optimizing a simple energy function that operates in the domain of segments via graph-cuts. We then estimate the spatial extent that is covered by each layer and identify occlusions. Since treatment of occlusions is hardly possible when using entire segments as matching primitives, we propose to use the pixel level in addition. We therefore define an energy function that measures the quality of an assignment of segments and pixels to layers. This energy function is then extended to work on multiple input frames and minimized via graph-cuts. In the experimental results, we show that our method produces good-quality results, especially in regions of low texture and close to motion boundaries, which are challenging tasks in optical flow computation.