Topics in matrix analysis
Stereo Correspondence Through Feature Grouping and Maximal Cliques
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Feature grouping in a hierarchical probabilistic network
Image and Vision Computing
A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Object modelling by registration of multiple range images
Image and Vision Computing - Special issue: range image understanding
Iterative point matching for registration of free-form curves and surfaces
International Journal of Computer Vision
A Graduated Assignment Algorithm for Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Structural Matching by Discrete Relaxation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Spectrum Based View Grouping and Matching of 3D Free-Form Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Markov random field modeling in image analysis
Markov random field modeling in image analysis
ACM Transactions on Graphics (TOG)
Structural Matching in Computer Vision Using Probabilistic Relaxation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shock-Based Indexing into Large Shape Databases
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
A new point matching algorithm for non-rigid registration
Computer Vision and Image Understanding - Special issue on nonrigid image registration
Graph Edit Distance from Spectral Seriation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Registration of point cloud data from a geometric optimization perspective
Proceedings of the 2004 Eurographics/ACM SIGGRAPH symposium on Geometry processing
Matching 2.5D Face Scans to 3D Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Deformation Modeling for Robust 3D Face Matching
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
3D Face Recognition Using 3D Alignment for PCA
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Graphical Models and Point Pattern Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Multiple Graph Alignment for the Structural Analysis of Protein Active Sites
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Pose-Oblivious Shape Signature
IEEE Transactions on Visualization and Computer Graphics
Shape matching and modeling using skeletal context
Pattern Recognition
A robust Graph Transformation Matching for non-rigid registration
Image and Vision Computing
Entropy and Distance of Random Graphs with Application to Structural Pattern Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Metric for Comparing Relational Descriptions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Organization of Relational Models for Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
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In this paper we propose a new approach for the comparison and retrieval of geometric graphs formulated from an alignment perspective. The algorithm presented here is quite general in nature and applies to geometric graphs of any dimension. The method involves two major steps. Firstly graph alignment is effected making use of an optimisation approach whose target function arises from a diffusion process over the graphs under study. This provides, from the theoretical viewpoint, a link between stochastic processes on graphs and the heat kernel. The second step involves using a probabilistic approach to recover the transformation parameters that map the graph-vertices to one another so as to permit the computation of a similarity measure based on the goodness of fit between the two graphs under study. Here, we view the transformation parameters as random variables and aim at minimising the Kullback-Liebler divergence between the two graphical structures under study. We provide a sensitivity analysis on synthetic data and illustrate the utility of the method for purposes of comparison and retrieval of CAD objects and binary shape categorisation. We also compare our results to those yielded by alternatives elsewhere in the literature.