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Erschienen in:
Detection and Description of Image Features: An Introduction Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

Image Segmentation Using an Evolutionary Method Based on Allostatic Mechanisms

verfasst von : Valentín Osuna-Enciso, Virgilio Zúñiga, Diego Oliva, Erik Cuevas, Humberto Sossa

Erschienen in: Image Feature Detectors and Descriptors

Verlag: Springer International Publishing

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Abstract

In image analysis, segmentation is considered one of the most important steps. Segmentation by searching threshold valuesassumes that objects in a digital image can be modeled through distinct gray level distributions. In this chapter it is proposed the use of a bio-inspired algorithm, called Allostatic Optimisation (AO), to solve the multi threshold segmentation problem. Our approach considers that an histogram can be approximated by a mixture of Cauchy functions, whose parameters are evolved by AO. The contributions of this chapter are on three fronts, by using: a Cauchy mixture to model the original histogram of digital images, the Hellinger distance as an objective function, and AO algorithm. In order to illustrate the proficiency and robustness of the proposed approach, it has been compared to the well-known Otsu method, over several standard benchmark images.

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Vorheriges Kapitel Spectral Reflectance Images and Applications
Nächstes Kapitel Image Analysis and Coding Based on Ordinal Data Representation
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Metadaten
Titel
Image Segmentation Using an Evolutionary Method Based on Allostatic Mechanisms
verfasst von
Valentín Osuna-Enciso
Virgilio Zúñiga
Diego Oliva
Erik Cuevas
Humberto Sossa
Copyright-Jahr
2016
Buch
Image Feature Detectors and Descriptors

Print ISBN: 978-3-319-28852-9

Electronic ISBN: 978-3-319-28854-3

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-28854-3_10

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