Kniha Clustering Techniques for Image Segmentation Fasahat Ullah Siddiqui

Clustering Techniques for Image Segmentation

Jazyk: Angličtina
Väzba: Pevná
Vydavateľ: Springer
Dostupnosť: Skladom u dodávateľa
Odosielame za 10-13 dní
87.03
This book presents the workings of major clustering techniques along with their advantages and short...

Informácie o knihe

Jazyk
Angličtina
Väzba
Kniha - Pevná
Vydalo
2021
Stránok
128
EAN
9783030812294
ISBN
3030812294
Enbook ID
38302729
Vydavateľ
Hmotnosť
389
Rozmery
160 x 241 x 12

Kompletný 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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