Least-Squares Fitting of Two 3-D Point Sets
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
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
Improved algorithms for robust point pattern matching and applications to image registration
Proceedings of the fourteenth annual symposium on Computational geometry
A graph distance metric combining maximum common subgraph and minimum common supergraph
Pattern Recognition Letters
Structural Graph Matching Using the EM Algorithm and Singular Value Decomposition
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Efficient Visual Recognition Using the Hausdorff Distance
Efficient Visual Recognition Using the Hausdorff Distance
Spotlight-Mode Synthetic Aperture Radar: A Signal Processing Approach
Spotlight-Mode Synthetic Aperture Radar: A Signal Processing Approach
Comparing Images Using the Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Correspondence Matching with Modal Clusters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Handbook of Fingerprint Recognition
Handbook of Fingerprint Recognition
A minutia-based partial fingerprint recognition system
Pattern Recognition
A new Hausdorff distance for image matching
Pattern Recognition Letters
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
Model-based classification of radar images
IEEE Transactions on Information Theory
Attributed scattering centers for SAR ATR
IEEE Transactions on Image Processing
Object matching algorithms using robust Hausdorff distance measures
IEEE Transactions on Image Processing
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This paper presents a classification approach, where a sample is represented by a set of feature vectors called an attributed point pattern. Some attributes of a point are transformational-variant, such as spatial location, while others convey some descriptive feature, such as intensity. The proposed algorithm determines a distance between point patterns by minimizing a Hausdorff-based distance over a set of transformations using a particle swarm optimization. When multiple training samples are available for each class, we implement multidimensional scaling to represent the point patterns in a low-dimensional Euclidean space for visualization and analysis. Results are demonstrated for latent fingerprints from tenprint data and civilian vehicles from circular synthetic aperture radar imagery.