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Proceedings Paper

Elastic shape recognition using Bayesian inference
Author(s): Bijan G. Mobasseri; Shubha Rao
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Paper Abstract

Energy minimization approaches in the form of deformable model fittings have recently attracted considerable attention. Such models are particularly effective when the sought after shapes undergo elastic deformations that cannot readily be accounted for by RST effects. Deformable models are by their nature iterative processes. Such approaches are computationally costly and require a detailed model building step, frequently in the form of cubic Bsplines. It is reported that almost all misclassifications in the energy minimization approaches can be attributed to two problems: local minima and modeling difficulties[3]. In this paper we have taken handwritten character recognition problem and proposed a solution that bypasses the two bottlenecks above. The algorithm requires no parametric shape models and is non-iterative. Character classes are modeled from a library of handwritten digits and described by discrete spatial processes. Contour classification is then performed by a MAP rule implemented on an unknown observed digit. We have shown classification rates that match or exceed those obtained by complex deformable templates. Keywords: deformable models, character recognition

Paper Details

Date Published: 30 October 1997
PDF: 7 pages
Proc. SPIE 3164, Applications of Digital Image Processing XX, (30 October 1997); doi: 10.1117/12.628230
Show Author Affiliations
Bijan G. Mobasseri, Villanova Univ. (United States)
Shubha Rao, Villanova Univ. (United States)

Published in SPIE Proceedings Vol. 3164:
Applications of Digital Image Processing XX
Andrew G. Tescher, Editor(s)

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