Feature-based correspondence: an eigenvector approach
Image and Vision Computing - Special issue: BMVC 1991
A Graduated Assignment Algorithm for Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
A RKHS Interpolator-Based Graph Matching Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recent Advances in Structural Pattern Recognition with Applications to Visual Form Analysis
IWVF-4 Proceedings of the 4th International Workshop on Visual Form
Graph-Based Methods for Vision: A Yorkist Manifesto
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Inexact Multisubgraph Matching Using Graph Eigenspace and Clustering Models
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Graphical Models and Point Pattern Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
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This paper explores a formulation for attributed graph matching as an inference problem over a hidden Markov Random Field. We approximate the fully connected model with simpler models in which optimal inference is feasible, and contrast them to the well-known probabilistic relaxation method, which can operate over the complete model but does not assure global optimality. The approach is well suited for applications in which there is redundancy in the binary attributes of the graph, such as in the matching of straight line segments. Results demonstrate that, in this application, the proposed models have superior robustness over probabilistic relaxation under additive noise conditions.