Kernel Based Fuzzy Clustering For Robust Image Segmentation: A Review And Comparison

Kaur, Prabhjot; Sharma, Poonam; Gupta, Pallavi; Kaur, Prabhjot; Sharma, Poonam; Gupta, Pallavi

ISBN 10: 3659281603 / ISBN 13: 9783659281600

Published by Lap Lambert Academic Publishing, 2012

About this Item

116 pages. 8.66x5.91x0.27 inches. In Stock.

Seller Inventory # 3659281603

Bibliographic Details

• Title: Kernel Based Fuzzy Clustering For Robust Image Segmentation: A Review And Comparison
• Publisher: Lap Lambert Academic Publishing
• Publication Date: 2012
• Language: English
• ISBN 10: 3659281603
• ISBN 13: 9783659281600
• Binding: Paperback
• Condition: Brand New
• Weight: 0.22 kilograms

About this title

Synopsis

The goal of image segmentation is partitioning of an image into a set of disjoint regions with uniform and homogeneous attributes such as intensity, color, tone etc. Image Segmentation plays an important role in a variety of applications like robot vision, object recognition, pattern recognition, image segmentation etc. Real digital Images generally contain unknown noise and considerable uncertainty. Although the Fuzzy C Means (FCM) algorithm functions well on noiseless images but it fails to segment images when corrupted with noise. To overcome this problem this book discussed well-known “kernel methods” that have been applied for noisy image segmentation. This book analysed the performance of the four algorithms FCM, Kernelized FCM (KFCM), Kernelized Intuitionistic FCM (KIFCM), Kernelized Type-2 FCM (K2FCM) with four synthetic images in noiseless case as well as when the images are corrupted with salt & pepper and Gaussian noise. The four algorithms are studied and analysed both qualitatively and quatitatively.

About the Author

Prabhjot Kaur, working as a Reader in Information Technology Department, Maharaja Surajmal Institute of Technology, Guru Gobind Singh Indraprastha University, New Delhi, India. Her research areas include soft Computing techniques, Image segmentation and Clustering.