Abstract
an efficient approach for color image segmentation is always sought by the researchers in the color image processing research. We have different clustering based and region based methods for the same. But still there arises the requirement of an optimal method. In this paper, a new approach for color image segmentation is proposed. Here the segmentation is carried out on the L channel of LAB color space. The input color image is first converted from RGB to LAB. Then L channel is extracted from the LAB converted image and sent as input to FCM algorithm. After this initial segmentation, the segmented image is filtered by sobel filter. The filtered image is then segmented by Meyer's Watershed algorithm to produce the final segmented image of the original image. The results of the proposed approach are found efficient when the same are analyzed in terms of MSE and PSNR. Also the segmented images are found free from over segmentation.
Highlights
In image processing research, image segmentation always plays a crucial rule
The topics involved in the proposed approach : LAB color space, fuzzy cmeans (FCM) algorithm, Sobel Filter And Watershed algorithm are discussed in the sections 4, 5,6 and 7 respectively
The images for the experiments are collected from Matlab demo images [26] and Berkeley Image Segmentation dataset [27]
Summary
Image segmentation always plays a crucial rule. It is the most important phase as the result of which, the whole image analysis process depends. As a result of image segmentation process, the image is divided into a set of segments which are homogeneous with respect to some criteria like color, intensity, or texture [1]. H(R) denotes the homogeneity attributes of pixels over region R on the basis of which the whole segmentation process carried out [3]. It is obvious from (iii) that pixels within a cluster must share same featured components. The topics involved in the proposed approach : LAB color space, FCM algorithm, Sobel Filter And Watershed algorithm are discussed in the sections 4, 5,6 and 7 respectively. Conclusion and future scope is given in the section 9
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