IEEE Transactions on Pattern Analysis and Machine Intelligence · 2004 · 102 citations · 21 references
EngineeringMachine LearningStatistical Shape AnalysisShape ClassificationShape AnalysisImage AnalysisData SciencePattern RecognitionRobot LearningComputational GeometryGeometric ModelingMachine VisionPlanar ShapesCurvature CoefficientsComputer ScienceStatistical Pattern RecognitionMedical Image ComputingComputer VisionNatural SciencesShape ModelingHidden Markov ModelsPattern Recognition Application
In this paper, Hidden Markov Models (HMMs) are investigated for the purpose of classifying planar shapes represented by their curvature coefficients. In the training phase, special attention is devoted to the initialization and model selection issues, which make the learning phase particularly effective. The results of tests on different data sets show that the proposed system is able to accurately classify objects that were translated, rotated, occluded, or deformed by shearing, also in the presence of noise.
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