A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Integrated Bayesian Approach to Layer Extraction from Image Sequences
IEEE Transactions on Pattern Analysis and Machine Intelligence
Video matting of complex scenes
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Cooperative Robust Estimation Using Layers of Support
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Layered Motion Representation with Occlusion and Compact Spatial Support
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Motion Segmentation and Tracking Using Normalized Cuts
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Pop-up light field: An interactive image-based modeling and rendering system
ACM Transactions on Graphics (TOG)
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
ACM SIGGRAPH 2004 Papers
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Dynamic Conditional Random Field Model for Object Segmentation in Image Sequences
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Bi-Layer Segmentation of Binocular Stereo Video
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
ACM SIGGRAPH 2005 Papers
An Iterative Optimization Approach for Unified Image Segmentation and Matting
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Motion layer extraction in the presence of occlusion using graph cut
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Moving object segmentation using motor signals
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Online learning for fast segmentation of moving objects
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
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Segmentation of video foreground objects from background has many important applications, such as human computer interaction, video compression, multimedia content editing and manipulation. Most existing methods work on image pixels or color segments which are computationally expensive. Some methods require extensive manual inputs, static cameras, and/or rigid scenes. In this paper we propose a fully automatic foreground segmentation method based on sequential clustering of sparse image features. The sparseness makes the method computationally efficient. We use both edge and corner points extracted from each video frame. A joint spatio-temporal linear regression method is developed to compute sparse motion layers of M consecutive frames jointly under the temporal consistency constraint. Once the sparse motion layers have been identified for each frame, the corresponding dense motion layers are created using the Markov Random Field (MRF) model. The MRF model assigns the rest of the image pixels to the motion layers by considering both the color attributes and the spatial relations between each pixel and its surrounding edge/corner points. Experimental evaluations on videos taken by webcams show the effectiveness of the proposed method.