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Clustering techniques for image segm...

Siddiqui, Fasahat Ullah.

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Clustering techniques for image segmentation

Record Type:	Electronic resources : Monograph/item
Title/Author:	Clustering techniques for image segmentation/ by Fasahat Ullah Siddiqui, Abid Yahya.
Author:	Siddiqui, Fasahat Ullah.
other author:	Yahya, Abid.
Published:	Cham :Springer International Publishing : : 2022.,
Description:	xx, 108 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	Introduction -- Introduction to Image Segmentation and Clustering -- Hard and Soft Clustering Techniques -- New Enhanced Clustering Techniques -- Mathematical Model of clustering techniques and evaluation methods -- Conclusion.
Contained By:	Springer Nature eBook
Subject:	Image segmentation. -
Online resource:	https://doi.org/10.1007/978-3-030-81230-0
ISBN:	9783030812300

Clustering techniques for image segmentation
Siddiqui, Fasahat Ullah.

Clustering techniques for image segmentation[electronic resource] /by Fasahat Ullah Siddiqui, Abid Yahya. - Cham :Springer International Publishing :2022. - xx, 108 p. :ill. (some col.), digital ;24 cm.

Introduction -- Introduction to Image Segmentation and Clustering -- Hard and Soft Clustering Techniques -- New Enhanced Clustering Techniques -- Mathematical Model of clustering techniques and evaluation methods -- Conclusion.

This book presents the workings of major clustering techniques along with their advantages and shortcomings. After introducing the topic, the authors illustrate their modified version that avoids those shortcomings. The book then introduces four modified clustering techniques, namely the Optimized K-Means (OKM), Enhanced Moving K-Means-1(EMKM-1), Enhanced Moving K-Means-2(EMKM-2), and Outlier Rejection Fuzzy C-Means (ORFCM) The authors show how the OKM technique can differentiate the empty and zero variance cluster, and the data assignment procedure of the K-mean clustering technique is redesigned. They then show how the EMKM-1 and EMKM-2 techniques reform the data-transferring concept of the Adaptive Moving K-Means (AMKM) to avoid the centroid trapping problem. And that the ORFCM technique uses the adaptable membership function to moderate the outlier effects on the Fuzzy C-meaning clustering technique. This book also covers the working steps and codings of quantitative analysis methods. The results highlight that the modified clustering techniques generate more homogenous regions in an image with better shape and sharp edge preservation. Showcases major clustering techniques, detailing their advantages and shortcomings; Includes several methods for evaluating the performance of segmentation techniques; Presents several applications including medical diagnosis systems, satellite imaging systems, and biometric systems.

ISBN: 9783030812300

Standard No.: 10.1007/978-3-030-81230-0doiSubjects--Topical Terms:

2132796
Image segmentation.

LC Class. No.: TA1638.4 / .S53 2022

Dewey Class. No.: 621.367

Clustering techniques for image segmentation
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100 1 $a Siddiqui, Fasahat Ullah. $3 3590309
245 1 0 $a Clustering techniques for image segmentation $h [electronic resource] / $c by Fasahat Ullah Siddiqui, Abid Yahya.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2022.
300 $a xx, 108 p. : $b ill. (some col.), digital ; $c 24 cm.
505 0 $a Introduction -- Introduction to Image Segmentation and Clustering -- Hard and Soft Clustering Techniques -- New Enhanced Clustering Techniques -- Mathematical Model of clustering techniques and evaluation methods -- Conclusion.
520 $a This book presents the workings of major clustering techniques along with their advantages and shortcomings. After introducing the topic, the authors illustrate their modified version that avoids those shortcomings. The book then introduces four modified clustering techniques, namely the Optimized K-Means (OKM), Enhanced Moving K-Means-1(EMKM-1), Enhanced Moving K-Means-2(EMKM-2), and Outlier Rejection Fuzzy C-Means (ORFCM) The authors show how the OKM technique can differentiate the empty and zero variance cluster, and the data assignment procedure of the K-mean clustering technique is redesigned. They then show how the EMKM-1 and EMKM-2 techniques reform the data-transferring concept of the Adaptive Moving K-Means (AMKM) to avoid the centroid trapping problem. And that the ORFCM technique uses the adaptable membership function to moderate the outlier effects on the Fuzzy C-meaning clustering technique. This book also covers the working steps and codings of quantitative analysis methods. The results highlight that the modified clustering techniques generate more homogenous regions in an image with better shape and sharp edge preservation. Showcases major clustering techniques, detailing their advantages and shortcomings; Includes several methods for evaluating the performance of segmentation techniques; Presents several applications including medical diagnosis systems, satellite imaging systems, and biometric systems.
650 0 $a Image segmentation. $3 2132796
650 0 $a Cluster analysis. $3 562995
650 1 4 $a Signal, Image and Speech Processing. $3 891073
650 2 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Image Processing and Computer Vision. $3 891070
700 1 $a Yahya, Abid. $3 3217033
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856 4 0 $u https://doi.org/10.1007/978-3-030-81230-0
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