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Erschienen in:
Study and Analysis of Subthreshold Leakage Current in Sub-65 nm NMOSFET Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

Robust Color Image Multi-thresholding Using Between-Class Variance and Cuckoo Search Algorithm

verfasst von : Venkatesan Rajinikanth, N. Sri Madhava Raja, Suresh Chandra Satapathy

Erschienen in: Information Systems Design and Intelligent Applications

Verlag: Springer India

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Abstract

Multi-level image thresholding is a well known pre-processing procedure, commonly used in variety of image related domains. Segmentation process classifies the pixels of the image into various group based on the threshold level and intensity value. In this paper, colour image segmentation is proposed using Cuckoo Search (CS) algorithm. The performance of the proposed technique is validated with the Bacterial Forage Optimization (BFO) and Particle Swarm Optimization (PSO). The qualitative and quantitative investigation is carried out using the parameters, such as CPU time, between-class variance value and image quality measures, such as Mean Structural Similarity Index Matrix (MSSIM), Normalized Absolute Error (NAE), Structural Content (SC) and PSNR. The robustness of the implemented segmentation procedure is also verified using the image dataset smeared with the Gaussian Noise (GN) and Speckle Noise (SN). The study shows that, CS algorithm based multi-level segmentation offers better result compared with BFO and PSO.

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Vorheriges Kapitel Apply of Sum of Difference Method to Predict Placement of Students’ Using Educational Data Mining
Nächstes Kapitel Social Media: A Review
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Metadaten
Titel
Robust Color Image Multi-thresholding Using Between-Class Variance and Cuckoo Search Algorithm
verfasst von
Venkatesan Rajinikanth
N. Sri Madhava Raja
Suresh Chandra Satapathy
Copyright-Jahr
2016
Verlag
Springer India
Buch
Information Systems Design and Intelligent Applications

Print ISBN: 978-81-322-2753-3

Electronic ISBN: 978-81-322-2755-7

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-81-322-2755-7_40

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