Publication Type : Journal Article
Publisher : International Journal of Engineering and Technology(UAE),
Source : International Journal of Engineering and Technology(UAE), Volume 7, Issue 4, p.3131-3134 (2018)
Url : https://www.scopus.com/record/display.uri?eid=2-s2.0-85057600087&origin=resultslist
Keywords : Canny edge, Clustering watershed, Color image, Detection K-means, Fuzzy C-Means, Segmentation
Campus : Mysuru
School : School of Arts and Sciences
Department : Computer Science
Year : 2018
Abstract : Image segmentation techniques aims at identification and extraction of foreground objects in an image resulting into individual segments. Segmentation of images basically are so varied from one type of image to other images as each had its own context and varied geometrical properties and thus leading to a challenge in design of a generic algorithmic procedure. In this paper, an effort is formed to compare and study the efficiency of color image segmentation victimization color areas, watersheds, fuzzy c-means and edge detection techniques towards the segmentation of fruit images. The fruit images employed for segmentation are downloaded from various sources of online and also few of the images are synthetically gathered by capturing the fruits images over a plain background. The analysis had resulted in conclusion that performance of fuzzy c -means and watersheds had led to optimal outcomes than other techniques. © 2018 Brunda R et. al.
Cite this Research Publication : Brunda, R., Divyashree, B., Shobha Rani, N., "Image segmentation technique- A comparative study," International Journal of Engineering and Technology(UAE), 7 (4), pp. 3131-3134, 2018.