SIGGRAPH '96 Proceedings of the 23rd annual conference on Computer graphics and interactive techniques
Video matting of complex scenes
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Machine Learning
A new point matching algorithm for non-rigid registration
Computer Vision and Image Understanding - Special issue on nonrigid image registration
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
ACM SIGGRAPH 2006 Papers
Natural video matting using camera arrays
ACM SIGGRAPH 2006 Papers
A Closed Form Solution to Natural Image Matting
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Cosegmentation of Image Pairs by Histogram Matching - Incorporating a Global Constraint into MRFs
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Image and video matting: a survey
Foundations and Trends® in Computer Graphics and Vision
Algorithm 887: CHOLMOD, Supernodal Sparse Cholesky Factorization and Update/Downdate
ACM Transactions on Mathematical Software (TOMS)
SIFT Flow: Dense Correspondence across Different Scenes
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
PatchMatch: a randomized correspondence algorithm for structural image editing
ACM SIGGRAPH 2009 papers
Unsupervised co-segmentation of a set of shapes via descriptor-space spectral clustering
Proceedings of the 2011 SIGGRAPH Asia Conference
Towards temporally-coherent video matting
MIRAGE'11 Proceedings of the 5th international conference on Computer vision/computer graphics collaboration techniques
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
A global sampling method for alpha matting
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Content-sensitive collection snapping
ICME '11 Proceedings of the 2011 IEEE International Conference on Multimedia and Expo
Co-Segmentation of 3D Shapes via Subspace Clustering
Computer Graphics Forum
On multiple foreground cosegmentation
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Random walks based multi-image segmentation: Quasiconvexity results and GPU-based solutions
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
An information-theoretic framework for image complexity
Computational Aesthetics'05 Proceedings of the First Eurographics conference on Computational Aesthetics in Graphics, Visualization and Imaging
Video matting using multi-frame nonlocal matting laplacian
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
Image Matting with Local and Nonlocal Smooth Priors
CVPR '13 Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition
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Single image matting, the task of estimating accurate foreground opacity from a given image, is a severely ill-posed and challenging problem. Inspired by recent advances in image co-segmentation, in this paper, we present a novel framework for a new task called co-matting, which aims to simultaneously extract alpha mattes in multiple images that contain slightly deformed instances of the same foreground object against different backgrounds. Our system first generates trimaps for input images using co-segmentation, and an initial alpha matte for each image using single image matting. Each alpha matte is then locally evaluated using a novel matting confidence metric learned from a training dataset. In the co-matting step, we first align the foreground object instances using appearance and geometric features, then apply a global optimization on all input images to jointly improve their alpha mattes, which allows high confidence local regions to guide their corresponding low confidence ones in other images to achieve more accurate mattes all together. Experimental results show that this co-matting framework can achieve noticeably higher quality results on an image stack than applying state-of-the-art single image matting techniques individually on each image.