Applied Mathematical Sciences, Vol. 2, 2008, no. 31, 1507 - 1520
Edge Detection Techniques:
Evaluations and Comparisons
Ehsan Nadernejad
Department of Computer Engineering, Faculty of Engineering
Mazandaran Institute of Technology
P.O. Box: 744, Babol, Iran
ehsan_nader@yahoo.com
Sara Sharifzadeh
Department of Communication Engineering, Faculty of Engineering
Shomal higher-education Institute,
P.O. Box: 731, Amol, Iran
sarasharifzade@yahoo.com
Hamid Hassanpour
Department of Computer Engineering, Faculty of Engineering
Mazandaran Institute of Technology
P.O. Box: 744, Babol, Iran
h_hassanpour@yahoo.com
Abstract
Edge detection is one of the most commonly used operations in image analysis, and
there are probably more algorithms in the literature for enhancing and detecting edges
than any other single subject. The reason for this is that edges form the outline of an
object. An edge is the boundary between an object and the background, and indicates
the boundary between overlapping objects. This means that if the edges in an image can
be identified accurately, all of the objects can be located and basic properties such as
area, perimeter, and shape can be measured. Since computer vision involves the
identification and classification of objects in an image, edge detections is an essential
tool. In this paper, we have compared several techniques for edge detection in image
processing. We consider various well-known measuring metrics used in image
processing applied to standard images in this comparison.
Keywords: image processing, edge detection, Euclidean distance, canny detector
1508 E. Nadernejad, S. Sharifzadeh and H. Hassanpour
I. INTRODUCTION
Edge detection is a very important area in the field of Computer Vision. Edges define
the boundaries between regions in an image, which helps with segmentation and object
recognition. They can show where shadows fall in an image or any other distinct
chang