Optimal thresholding using multi-state stochastic connectionist approach

Babu, Phanendra G and Murty, Narasimha M (1995) Optimal thresholding using multi-state stochastic connectionist approach. In: Pattern Recognition Letters, 16 (1). pp. 11-18.

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Abstract

In this paper, we describe the applicability of the K-means clustering algorithm for locating thresholds in a given histogram. In order to find optimal thresholds a probabilistic method called Multi-state Stochastic Connectionist Approach (MSCA) is em-ployed. Mean Field Annealing (MFA), a deterministic counterpart of MSCA, is also studied in this context. A parallel model to parallelize the above methods is presented. Results of MFA and MSCA are compared with that of the K-means algorithm.

Item Type:	Journal Article
Publication:	Pattern Recognition Letters
Publisher:	Elsevier
Additional Information:	The copyright belongs to Elsevier.
Keywords:	Neural networks;Image processing;Image segmentation:Thresholding;Clustering methods
Department/Centre:	Division of Electrical Sciences > Computer Science & Automation
Date Deposited:	14 Jun 2006
Last Modified:	19 Sep 2010 04:29
URI:	http://eprints.iisc.ac.in/id/eprint/7613

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