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Anotace knihy

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.

Parametry knihy

• Plný název: Kernel based Fuzzy Clustering for Robust Image Segmentation
• Podnázev: A Review and Comparison
• Autor: Prabhjot Kaur, Poonam Sharma, Pallavi Gupta
• Jazyk: Angličtina
• Vazba: Brožovaná
• Počet stran: 116
• EAN: 9783659281600
• ID: 06983642
• Nakladatelství: LAP Lambert Academic Publishing
• Hmotnost: 189 g
• Rozměry: 220 × 150 × 7 mm
• Rok vydání: 2012

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