By Topic

Colour Image Segmentation Using Fuzzy Clustering Techniques

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Sowmya, B. ; Dept. of Electronics & Control Engg., Sathyabama Institute of Science & Technology, Deemed University, Chennai - 119 Ph: 044 - 22440676, Mobile: 9841127316, Email: bsowya@yahoo.com ; Bhattacharya, S.

Segmentation of an image entails the division or separation of the image into regions of similar attribute. The most basic attribute for segmentation of an image is its luminance amplitude for a monochrome image and color components for a color image. Fuzzy clustering is one of the methods used for image segmentation. This paper describes two fuzzy clustering methods to analyze and segment the color space. The clustering algorithms, namely, Fuzzy c means algorithm(FCM) and Possibilistic c means algorithm(PCM) are used for image segmentation. A self estimation algorithm has been developed for determining the number of clusters. The quality of the segmented image is estimated by their Peak Signal to noise ratio(PSNR).

Published in:

INDICON, 2005 Annual IEEE

Date of Conference:

11-13 Dec. 2005