Kernel based Fuzzy Clustering for Robust Image Segmentation : A Review and Comparison

Language: English

Published by LAP Lambert Academic Publishing, 2012

3659281603 / 9783659281600

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nach der Bestellung gedruckt Neuware - Printed after ordering - 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.

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Title
Kernel based Fuzzy Clustering for Robust Image Segmentation : A Review and Comparison
Author
Prabhjot Kaur
Publisher
LAP Lambert Academic Publishing
Publication year
2012
Condition
Neu
Binding
Taschenbuch
Language
English
ISBN 10
3659281603
ISBN 13
9783659281600
Item weight
189 grams
Dimensions
220x150x33 mm

AHA-BUCH GmbH

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