Flexible Skew-Symmetric Shape Model for Shape Representation, Classification, and Sampling
Baloch, S. H., & Krim, H. (2007, February 1). IEEE Transactions on Image Processing, Vol. 16, pp. 317–328.
author keywords: flexible skew-symmetric distributions (FSSM); principal curves; sampling; shape classification; shape modeling; template learning
MeSH headings : Algorithms; Artificial Intelligence; Computer Simulation; Image Enhancement / methods; Image Interpretation, Computer-Assisted / methods; Information Storage and Retrieval / methods; Models, Statistical; Pattern Recognition, Automated / methods; Reproducibility of Results; Sensitivity and Specificity
topics (OpenAlex): Morphological variations and asymmetry; Image Retrieval and Classification Techniques; Medical Image Segmentation Techniques
TL;DR:
A novel statistical method for shape modeling, which is sufficiently flexible to accommodate a departure from Gaussianity of the data and is fairly general to learn a "mean shape" (template), with a potential for classification and random generation of new realizations of a given shape.
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