Structural Matching by Discrete Relaxation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple view geometry in computer vision
Multiple view geometry in computer vision
Structural Graph Matching Using the EM Algorithm and Singular Value Decomposition
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Structural Matching in Computer Vision Using Probabilistic Relaxation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Correspondence Matching with Modal Clusters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Exact and Approximate Graph Matching Using Random Walks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Vectors from Algebraic Graph Theory
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Spectral Technique for Correspondence Problems Using Pairwise Constraints
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Robust mosaicking of stereo digital elevation models from the ames stereo pipeline
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part II
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A probabilistic matching of lines, which form a homography in two images, is formulated in the framework of the forward stepwise regression. A membership matrix represents the likelihood of line correspondences to the homography. The correspondence measure is borrowed from the forward stepwise regression so that the squared error of the homography and the number of correspondences are balanced simultaneously. An alternating scheme for optimizing the membership and homography is provided. The experimental results on synthetic and real images validate the proposed method.