Active shape models—their training and application
Computer Vision and Image Understanding
A Graduated Assignment Algorithm for Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Framework for Automatic Landmark Identification Using a New Method of Nonrigid Correspondence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shock Graphs and Shape Matching
International Journal of Computer Vision
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discovering Shape Categories by Clustering Shock Trees
CAIP '01 Proceedings of the 9th International Conference on Computer Analysis of Images and Patterns
A unified framework for alignment and correspondence
Computer Vision and Image Understanding
Statistical Shape Analysis: Clustering, Learning, and Testing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Registration in Implicit Spaces Using Information Theory and Free Form Deformations
IEEE Transactions on Pattern Analysis and Machine Intelligence
A new shape decomposition scheme for graph-based representation
Pattern Recognition
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A new method for shape learning is presented in this paper. This method incorporates abilities from both statistical and structural pattern recognition approaches to shape analysis. It borrows from statistical pattern recognition the capability of modelling sets of point coordinates, and from structural pattern recognition the ability of dealing with highly irregular patterns, such as those generated by points missingness. To that end we use a novel adaptation of Procrustes analysis, designed by us to align sets of points with missing elements. We use this information to generate sets of attributed graphs (AGs). From each set of AGs we synthesize a function-described graph (FDG), which is a type of compact representation that has the capability of probabilistic modelling of both structural and attribute information. Multivariate normal probability density estimation is used in FDGs instead of the originally used histograms. Comparative results of classification performance are presented of structural vs. attributes + structural information.