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Comparative Analysis of Edge Detection between Gray Scale and Color Image

Shaveta Malik, Tapas Kumar. Published in Image Processing.

Communications on Applied Electronics
Year of Publication: 2016
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors: Shaveta Malik, Tapas Kumar

Shaveta Malik and Tapas Kumar. Comparative Analysis of Edge Detection between Gray Scale and Color Image. Communications on Applied Electronics 5(2):38-43, May 2016. BibTeX

	author = {Shaveta Malik and Tapas Kumar},
	title = {Comparative Analysis of Edge Detection between Gray Scale and Color Image},
	journal = {Communications on Applied Electronics},
	issue_date = {May 2016},
	volume = {5},
	number = {2},
	month = {May},
	year = {2016},
	issn = {2394-4714},
	pages = {38-43},
	numpages = {6},
	url = {},
	doi = {10.5120/cae2016652230},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


One of the essential tasks in Digital image processing is edge detection. Edge detection generally makes the process of image segmentation and pattern recognition a bit more comfort. Edge detection significantly reduces the amount of data and filters out useless information, while preserving the relevant structural properties in an image. The paper applied the edge detection techniques on color images as well as on grey scale images. It has been shown that the Canny’s edge detection algorithm performs better than all other methods under almost all scenarios. It has been observed that canny edge detection method gives better result in gray scale images as well as in color. Moreover just to add Edge detection in color images has not received the same attention as gray scale images. The difference between color images and gray-level images is that, in a color image, a color vector (which generally consists of three components (RGB) is assigned to a pixel. Thus, in color image processing, vector-valued image functions are used instead of scalar image functions. The paper is implemented using MATLAB 12.0.


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Edge Detection, Gradient based edge detection, Laplacian based edge detection.