Paper Title :A Probabilistic Framework for Shape Recognition

Author :Abdullah A. Al-Shaher, Edwin R. Hancock

Article Citation :Abdullah A. Al-Shaher ,Edwin R. Hancock , (2017 ) " A Probabilistic Framework for Shape Recognition " , International Journal of Advance Computational Engineering and Networking (IJACEN) , pp. 8-14, Volume-5,Issue-7

Abstract : This paper describes a probabilistic framework for recognizing 2D shapes with articulated components. The shapes are represented using both geometrical and a symbolic primitives, that are encapsulated in a two layer hierarchical architecture. Each primitive is modelled so as to allow a degree of articulated freedom using a polar point distribution model that captures how the primitive movement varies over a training set. Each segment is assigned a symbolic label to distinguish its identity, and the overall shape is represented by a configuration of labels. We demonstrate how both the point-distribution model and the symbolic labels can be combined to perform recognition using a probabilistic hierarchical algorithm. This involves recovering the parameters of the point distribution model that minimize an alignment error, and recovering symbol configurations that minimize a structural error. We apply the recognition method to human pose recognition. Keywords - Polar Point Distrubtion Models, Discrete Relaxation, Shape Recognition, Expectation Maximization Algorithm, Hierarchical Mixtures Of Shapes, Human Posture.

Type : Research paper

Published : Volume-5,Issue-7

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