An Eigendecomposition Approach to Weighted Graph Matching Problems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Least-Squares Estimation of Transformation Parameters Between Two Point Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature-based correspondence: an eigenvector approach
Image and Vision Computing - Special issue: BMVC 1991
Parameterized Point Pattern Matching and Its Application to Recognition of Object Families
IEEE Transactions on Pattern Analysis and Machine Intelligence
Active shape models—their training and application
Computer Vision and Image Understanding
Graphical Templates for Model Registration
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Graph Matching With a Dual-Step EM Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distortion Invariant Object Recognition in the Dynamic Link Architecture
IEEE Transactions on Computers
Modal Matching for Correspondence and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Similarity and Affine Invariant Distances Between 2D Point Sets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph-Based Methods for Vision: A Yorkist Manifesto
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
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In this paper we present a new method for aligning point distribution models to noisy and unlabelled image data. The aim is to construct an enhanced version of the point distribution model of Cootes and Taylor in which the point-position information is augmented with a neighbourhood graph which represents the relational arrangement of the landmark points. We show how this augmented point distribution model can be matched to unlabelled point-sets which are subject to both additional clutter and point drop-out. The statistical framework adopted for this study interleaves the processes of finding point correspondences and estimating the alignment parameters of the point distribution model. The utility measure underpinning the work is the cross entropy between two probability distributions which respectively model alignment errors and correspondence errors. In the case of the point alignment process, we assume that the registration errors follow a Gaussian distribution. The correspondence errors are modelled using probability distribution which has been used for symbolic graph-matching. Experimental results are presented using medical image sequences.