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

116 pages. 8.66x5.91x0.27 inches. In Stock. Seller Inventory # 3659281603

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.

Bibliographic Details

Title: Kernel Based Fuzzy Clustering For Robust ...
Publisher: Lap Lambert Academic Publishing
Publication Date: 2012
Binding: Paperback
Condition: Brand New