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Model-based shape classification using shape-transformation-invariant descriptors

The shape classification methods derived from similarity measures based on the shape-transformation-variant descriptors often require shape normalization/standardization that involves complicated computations and contour or code matching schemes. In this paper, we introduce a quantitative similarity measure and a new model-based shape classification method which uses exclusively the shape-transformation-invariant descriptors. This method eliminates all possible variations and potential problems caused by shape transformation, and complicated contour matching and/or shape normalization/standardization procedures.