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
DOIONLINE NO - IJACEN-IRAJ-DOIONLINE-8538
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Copyright: © Institute of Research and Journals
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Published on 2017-09-08 |
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