Prabhjot Kaur, Poonam Sharma, Pallavi Gupta

Kernel based Fuzzy Clustering for Robust Image Segmentation

A Review and Comparison

• Broschiertes Buch

Produktbeschreibung

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.

Produktdetails

• Verlag: LAP Lambert Academic Publishing
• Seitenzahl: 116
• Englisch
• Abmessung: 220mm x 150mm
• ISBN-13: 9783659281600
• ISBN-10: 3659281603
• Artikelnr.: 36677332

Autorenporträt

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.