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Kniha Clustering Techniques for Image Segmentation Fasahat Ullah Siddiqui

Clustering Techniques for Image Segmentation

Autor: Fasahat Ullah Siddiqui, Abid Yahya
Jazyk: Angličtina
Vazba: Brožovaná
Vydavatel: Springer, Berlin
Dostupnost: Skladem u dodavatele
Odesíláme za 8-11 dnů
1 990 Kč
This book presents the workings of major clustering techniques along with their advantages and short...

Informace o knize

Autor
Fasahat Ullah Siddiqui, Abid Yahya
Jazyk
Angličtina
Vazba
Kniha - Brožovaná
Vydáno
2022
Stránek
108
EAN
9783030812324
Enbook ID
41900705
Vydavatel
Springer, Berlin
Hmotnost
230
Rozměry
155 x 235 x 7

Kompletní popis

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.

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