International Journal of Advance Computational Engineering and Networking (IJACEN)
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Sep. 2020
Submitted Papers : 80
Accepted Papers : 10
Rejected Papers : 70
Acc. Perc : 12%
Issue Published : 90
Paper Published : 1248
No. of Authors : 3136
  Journal Paper

Paper Title
A Probabilistic Framework for Shape Recognition

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.


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

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| Published on 2017-09-08
   
   
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