CLASSIFICATION AND SAMPLING OF SHAPES THROUGH SEMIPARAMETRIC SKEW-SYMMETRIC SHAPE MODEL (TuePmSS3)
Author(s) :
Sajjad Baloch (North Carolina State University, USA)
Hamid Krim (North Carolina State University, USA)
Abstract : We present a novel method for shape modeling using an extended class of semiparametric skew-symmetric ($SSS$) distributions. Given several realizations of a simple shape, the proposed method models it as a joint distribution of angle and distance from the centroid of all points on the boundary. The model, called ``{\it Semiparametric Skew-Symmetric Shape Model}'' ($SSSM$), is capable of capturing inherent variability of shapes provided the realization contours remain within a certain neighborhood range around a ``mean'' with high probability. In this paper, we will discuss $SSSM$ learning, classification through $SSSM$ and sampling shapes from the learned models.

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