Performance Analysis of Edge Detection Algorithms on Various Image Types

Objective: Various edge detection algorithms are analyzed to find the best and worst performance of edge detection algorithm on various image types. Methods/Statistical Analysis: Only .tif image files are considered for the analysis. Some of the sample images in MATLAB tools and some from web are considered as source for the performance analysis. The performance of the edged image is measured using the entropy and signal noise ratio. High entropy and SNR values specified the high quality of the edged image and the low values indicated the low quality of the image. Findings: Making a deep analysis on various edge detection algorithms is really worth enough in Image processing. Here, five commonly used edge detection algorithms such as Prewitt, Sobel, Robert, Log and Canny are consider for analysis. Matrix form of grayscaled, graysliced, indexed, binary and dither binary image information are taken for the analysis. The analysis is done to find the best and worst performance of edge detection algorithm on various image types. For an image, five different edge detection algorithms applied on five different image information. Totally twenty five edged images are generated as output of an image. From the analysis, it is identified that Canny edge detection algorithm is performing better among the five algorithms. Out of the five image information, Canny algorithm on Dither binary image information yields the high entropy and SNR values. But, the Robert algorithm with indexed image information generates the very low entropy with low SNR values. Applications/Improvements: Edge detection is an important and basic operation to be completed for any image processing activities, image analysis, pattern recognition on various images such as satellite images, medical images etc.,