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2016 | OriginalPaper | Chapter

Multi-level Thresholding Segmentation Approach Based on Spider Monkey Optimization Algorithm

Authors : Swaraj Singh Pal, Sandeep Kumar, Manish Kashyap, Yogesh Choudhary, Mahua Bhattacharya

Published in: Proceedings of the Second International Conference on Computer and Communication Technologies

Publisher: Springer India

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Abstract

Image Segmentation is an open research area in which Multi-level thresholding is a topic of current research. To automatically detect the threshold, histogram-based methods are commonly used. In this paper, histogram-based bi-level and multi-level segmentation are proposed for gray scale image using spider monkey optimization (SMO). In order to maximize Kapur’s and Otus’s objective functions, SMO algorithm is used. To test the results of SMO algorithm, we use standard test images. The standard images are pre-tested and Benchmarked with Particle Swarm Optimization (PSO) Algorithm. Results confirm that new segmentation method is able to improve upon result obtained by PSO in terms of optimum threshold values and CPU time.

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Metadata
Title
Multi-level Thresholding Segmentation Approach Based on Spider Monkey Optimization Algorithm
Authors
Swaraj Singh Pal
Sandeep Kumar
Manish Kashyap
Yogesh Choudhary
Mahua Bhattacharya
Copyright Year
2016
Publisher
Springer India
Book
Proceedings of the Second International Conference on Computer and Communication Technologies

Print ISBN: 978-81-322-2522-5

Electronic ISBN: 978-81-322-2523-2

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-81-322-2523-2_26