A Comparative Study of Edge Detection Techniques in Digital Images

Masters Thesis from the year 2016 in the subject Computer Science - Applied, course: MSIT (Hons), language: English, abstract: In digital image processing, edge detection is a process for getting the boundaries of objects in digital image, structural image and removing the unwanted area from digital image, edge is important and basic information which can be manipulated by different edge detection techniques. In this work, edge detections methodologies and their mathematical formulas are investigated and detailed concepts of fundamental operations of edge detection techniques and their comparison are presented. Old theories and research papers are also repeated and replicated in this paper. Methodologies that are investigated in research are first order edge detection that includes classical operators like Prewitt, Sobel and Robert, and the canny edge detection. These edge detection techniques are applied to different images like human face, plants, birds and cups etc. Classical operator as well as Canny operators are applied on digital image and investigated the simulated results of these operators and also investigate the mathematical reasons for their performances. After doing this, the research done makes a conclusion that Canny operator offers the best performance among the operators considered in this work but not give same results in all type of images, canny also failed to detect edges where image have more data. Simulation of edge detection techniques are carried out in Matlab and the comparison is made on the basis of the Matlab results and mathematical equations.