Contextual decision rule for region analysis
Image and Vision Computing - Special issue: papers from the second Alvey Vision Conference
Stereo Correspondence Through Feature Grouping and Maximal Cliques
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition
Linear feature compatibility for pattern-matching relaxation
Pattern Recognition
Fuzzy relaxation approach for inexact scene matching
Image and Vision Computing
A fuzzy relaxation technique for partial shape matching
Pattern Recognition Letters
Structural Matching in Computer Vision Using Probabilistic Relaxation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Matching Delauny Triangulations by Probabilistic Relaxation
CAIP '95 Proceedings of the 6th International Conference on Computer Analysis of Images and Patterns
Relational matching with dynamic graph structures
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Relaxation Matching Techniques-A Comparison
IEEE Transactions on Pattern Analysis and Machine Intelligence
Semantic Description of Aerial Images Using Stochastic Labeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Foundations of Relaxation Labeling Processes
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Metric for Comparing Relational Descriptions
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Dynamics of Nonlinear Relaxation Labeling Processes
Journal of Mathematical Imaging and Vision
An energy function and continuous edit process for graph matching
Neural Computation
Symbol Recognition by Error-Tolerant Subgraph Matching between Region Adjacency Graphs
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Algorithmics and applications of tree and graph searching
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Fuzzy morphisms between graphs
Fuzzy Sets and Systems
A RKHS Interpolator-Based Graph Matching Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Least Committment Graph Matching by Evolutionary Optimisation
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part I
Graph-Based Methods for Vision: A Yorkist Manifesto
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Non-bayesian Graph Matching without Explicit Compatibility Calculations
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Successive Projection Graph Matching
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
EMMCVPR '01 Proceedings of the Third International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition
A POCS-Based Graph Matching Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph Edit Distance from Spectral Seriation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic relaxation labelling using the Fokker-Planck equation
Pattern Recognition
A graph matching method and a graph matching distance based on subgraph assignments
Pattern Recognition Letters
Orthonormal Kernel Kronecker product graph matching
GbRPR'03 Proceedings of the 4th IAPR international conference on Graph based representations in pattern recognition
A graph spectral approach to consistent labelling
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part II
Asymmetric inexact matching of spatially-attributed graphs
GbRPR'05 Proceedings of the 5th IAPR international conference on Graph-Based Representations in Pattern Recognition
Pattern analysis with graphs: Parallel work at Bern and York
Pattern Recognition Letters
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This letter presents a new methodology for determining the compatibility coefficients required for performing graph matching by probabilistic relaxation. The adopted framework is Bayesian and commences by specifying the effects of segmentation errors in corrupting the connectivity structure or topology of the graphs under match. This model of relational constraint corruption leads to a pattern of compatibility coefficients that is completely determined by the global topological properties of the graphs under match. We illustrate the application of this new theory in two graph matching applications. The first of these is concerned with exploiting constraints provided by edges. Here the compatibility coefficient for consistent edges is equal to the inverse edge-density. Our second illustration extends the compatibility model to the level of graph faces; the required coefficients are again parameter-free. We provide experimental validation of our method in the matching of aerial images. Here we demonstrate that the theoretical values of our compatibility coefficients are close to their experimentally optimal values.