Clustering Techniques for Image Segmentation

Name: Clustering Techniques for Image Segmentation
Brand: Springer
SKU: 9783030812294
Price: 33508 HUF
Availability: InStock

Szerző: Fasahat Ullah Siddiqui, Abid Yahya

Nyelv:

Kötés: Kemény kötésű

Kiadó: Springer

Elérhetőség: Beszállítói készleten

Küldés 10-13 napon belül

33 508 Ft

This book presents the workings of major clustering techniques along with their advantages and short...

Információk a könyvről

Szerző

Fasahat Ullah Siddiqui, Abid Yahya

Nyelv

Angol

Kötés

Könyv - Kemény kötésű

Kiadva

2021

oldal

128

EAN

9783030812294

ISBN

3030812294

Enbook ID

38302729

Kiadó

Springer

Súly

389

Méretek

160 x 241 x 12

Teljes leírás

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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Clustering Techniques for Image Segmentation

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