Threshold Values of Different Classical Edge Detection Algorithms

Abstract

The subject of Detecting edges in images is considered one of the main topics in digital image processing and the most common one, due to its wide applications in many fields. Classical methods for detecting edges in digital images still give excellent results if the threshold is chosen correctly. In this paper, a group of classical edge algorithms was taken and tested on different types of images, Canny edge detection algorithm gave the best results in all circumstances if the threshold value of it was set between 0.30-0.45. The range of the threshold values was from 0.1 to 0.45 in Roberts, Sobel, and Prewitt's edge detection algorithms. In this paper, four famous classical algorithms were tested on some standard RG BA images which had some noises by purpose and by holding different frequencies, containing different types of noise. Since the number of images has reached 50 thousand in total, a lot of data has been obtained and these algorithms have been tested on a large number of images. Indicating the algorithms implemented to perform on different image types, the threshold value was changed from 0 to 1 thousand times with each image by 0.001 value.