Abstract
Nowadays understanding of image processing and pattern recognition is the great focus to develop an application of image segmentation. Edge detection is the fundamental technique that can provide information of the boundaries in different objects in an image. Selecting a suitable edge detection algorithm is important to obtain the best performance of segmentation. This paper presents an analysis of implementation for various edge detection techniques on color spaces. We implement Sobel, Prewitt, Robert, and Laplacian edge detection on Hue, Saturation, Value (HSV) and YUV color space by using threshold = 65. We calculated the mean square error (MSE), root mean square error (RMSE), peak signal to noise ratio (PSNR) and execution time as a comparison. This paper tested images from Berkeley Segmentation Dataset (BSDS). The results showed that Robert edge detection in HSV color space especially hue channel can be utilized to obtain more precise boundaries in different objects and it is also quick to compute.
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