Principal Warps: Thin-Plate Splines and the Decomposition of Deformations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Experimental Comparison of Min-cut/Max-flow Algorithms for Energy Minimization in Vision
EMMCVPR '01 Proceedings of the Third International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition
Markov Random Fields with Efficient Approximations
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Visual Correspondence Using Energy Minimization and Mutual Information
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Motion Layer Extraction in the Presence of Occlusion Using Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Piecewise Image Registration in the Presence of Multiple Large Motions
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
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
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This paper presents an approach based on graph cuts initially used for motion segmentation that is being applied to the nonrigid registration problem. The main contribution of our method is the formulation of landmarks in the graph cut minimization framework. In the graph cut method, we add a penalty cost based on landmarks to the data energy. In the presence of a landmark, we adjust the T-link weights to cut strategic links. Our formulation also allows the spread of a landmark influence to its neighborhood. We first show with synthetic images that minimization with graph cuts can indeed be used for non-rigid registration and show how landmarks can guide the minimization process towards a customized solution. We later use this method with real images and show how landmarks can successfully guide the registration of a coronary angiogram.