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Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/355

Title: Parallel Genetic Algorithm Based Thresholding for Image Segmentation
Authors: Kanungo, P
Nanda, P K
Keywords: Thresholding
Segmentation
Object Background Classification
Genetic Algorithm
Issue Date: 2006
Publisher: IMT, Nagpur, India
Citation: Proceedings of the National Seminar on IT and Softcomputing ITSC06, Nov 17-18 IMT, Nagpur, India
Abstract: Threshold plays a vital role in classification of objects and background in a given scene and hence segmentation. Determination of optimal threshold is hard for images exhibiting overlapping histogram distributions. In this paper, we propose a novel strategy of determining the threshold from histogram distributions. A feature image is generated from the given image and the optimal threshold is determined using the histogram of the featured pixels. The featured pixels are generated by considering a fixed window around a pixel. The histogram distributions are discrete in nature and hence Genetic Algorithm (GA) and Parallel Genetic Algorithm (PGA) based clustering algorithms are proposed to determine the optimal thresholds for two and three class problems. The optimal thresholds, thus determined could segment the noisy image. The efficacy of the proposed scheme is compared with that of the Otsu’s approach. Results obtained by the proposed scheme was comparable to that O...
Description: Copyright for this article belongs to the publisher of the proceedings
URI: http://hdl.handle.net/2080/355
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