Clustering Techniques for Image Segmentation

Author: Fasahat Ullah Siddiqui Abid Yahya

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Description

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. show more

Additional information

Weight 389 g
Author

Fasahat Ullah Siddiqui Abid Yahya

Publisher

Springer Nature Switzerland AG

Binding

Hardback

ISBN-10

3030812294

Dimensions

235 x 155

Language

English

Country of Pub

Switzerland

Book Condition

New

Notes

50 Tables, color; 16 Illustrations, color; 39 Illustrations, black and white; XX, 108 p. 55 illus., 16 illus. in color.

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