Clustering Based Image Segmentation for Multi Resolution Images

Name: Clustering Based Image Segmentation for Multi Resolution Images
Brand: LAP LAMBERT Academic Publishing
SKU: 9786206159803
Price: 293.94 PLN
Availability: InStock

Autor: Dr. M. Ganesh

Język:

Oprawa: Miękka

Wydawca: LAP LAMBERT Academic Publishing

Dostępność: Dostępna u dostawcy

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293.94 zł

Clustering is a process of classifying objects or patterns in such a way that the samples in the sam...

Informacje o książce

Autor

Dr. M. Ganesh

Język

Angielski

Oprawa

Książka - Miękka

Data wydania

2023

strony

204

EAN

9786206159803

ISBN

6206159809

Enbook ID

43510465

Wydawca

LAP LAMBERT Academic Publishing

Waga

322

Wymiary

150 x 220 x 13

Pełny opis

Clustering is a process of classifying objects or patterns in such a way that the samples in the same group are more similar than the samples in different groups. Based on the fuzzy theory, the fuzzy clustering method, which produces the idea of partial membership of clustering. One of the most important and widely used fuzzy clustering methods is the Fuzzy C-Means (FCM) algorithm. The main purpose of the FCM algorithm is to make the vector space of a sample point be divided into a number of sub-spaces in accordance with a distance measure. However, the FCM algorithm does not take the local spatial property of images into consideration, and hence suffers from high sensitivity to noise.Image segmentation plays a crucial role in many medical imaging applications also. Many algorithms and techniques have been developed to solve image segmentation problems. Spectral pattern is not sufficient in high resolution image for image segmentation due to variability of spectral and structural information.In order to further improve the segmentation performance and convergence speed for gray images corrupted by noise, a kernel version of GFCM with spatial information is proposed.

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Clustering Based Image Segmentation for Multi Resolution Images

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