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Research Paper | Engineering Science | India | Volume 3 Issue 6, June 2014

A Novel Image Segmentation of Galaxy Images Through Optimized Fuzzy K-Means Clustering

J. Saranya | Dr. Velmurugan

Abstract: The image segmentation is the process of partition an image into meaningful regions with respect to a particular application. The segmentation is based on measurements taken from the image and might be grey level; colour; texture; depth or motion. Usually image segmentation is an initial and vital step in a series of processes aimed at overall image understanding. The applications of image segmentation include Identifying objects in a scene for object-based measurements such as size and shape. The objects are identified in the moving scene for the application of video compression. The identification of objects which are at different distances from a sensor using depth measurements; from a laser range finder that enabling path planning for a mobile robot. The traditional hyper connection algorithm provide the applications of image segmentation. It will segment and produce the result for normal images but it cant segment the overlapping image which present in the galaxy images. The fuzzy c-means algorithm is applied to produce the segmented results object in the form of clustering and it will take more computation time for each iterations of an image. The fuzzy c-means algorithm required a number of iterations to produce proper segmentation. In this paper the Fuzzy k-means algorithm is proposed to provide the effective image clustering and segmentation with less computation time and the number of iterations will not affect the image segmentation. To provide a effective segmentation initially the noises will be removed from the image using median filter. The median filter will reduce the noise content and it helps to bring the higher efficient image segmentation. The experimental results shown that the proposed system achieves high precision and outperforms the existing methods.

Keywords: Image Segmentation, Fuzzy k-means, Median filter, hyper connections

Edition: Volume 3 Issue 6, June 2014,

Pages: 2166 - 2170

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