2024年5月22日发(作者：微生曼寒)

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@

Sara Sharifzadeh

Department of Communication Engineering, Faculty of Engineering

Shomal higher-education Institute,

P.O. Box: 731, Amol, Iran

sarasharifzade@

Hamid Hassanpour

Department of Computer Engineering, Faculty of Engineering

Mazandaran Institute of Technology

P.O. Box: 744, Babol, Iran

h_hassanpour@

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. I

NTRODUCTION

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

change in the intensity of an image. Edge detection is a fundamental of low-level image

processing and good edges are necessary for higher level processing. [1]

The problem is that in general edge detectors behave very poorly. While their behavior

may fall within tolerances in specific situations, in general edge detectors have

difficulty adapting to different situations. The quality of edge detection is highly

dependent on lighting conditions, the presence of objects of similar intensities, density

of edges in the scene, and noise. While each of these problems can be handled by

adjusting certain values in the edge detector and changing the threshold value for what

is considered an edge, no good method has been determined for automatically setting

these values, so they must be manually changed by an operator each time the detector is

run with a different set of data.

Since different edge detectors work better under different conditions, it would be ideal

to have an algorithm that makes use of multiple edge detectors, applying each one when

the scene conditions are most ideal for its method of detection. In order to create this

system, you must first know which edge detectors perform better under which

conditions. That is the goal of our project. We tested four edge detectors that use

different methods for detecting edges and compared their results under a variety of

situations to determine which detector was preferable under different sets of conditions.

This data could then be used to create a multi-edge-detector system, which analyzes the

scene and runs the edge detector best suited for the current set of data. For one of the

edge detectors we considered two different ways of implementation, one using intensity

only and the other using color information.

We also considered one additional edge detector which takes a different philosophy to

edge detection. Rather than trying to find the ideal edge detector to apply to traditional

photographs, it would be more efficient to merely change the method of photography to

one which is more conducive to edge detection. It makes use of a camera that takes

multiple images in rapid succession under different lighting conditions. Since the

hardware for this sort of edge detection is different than that used with the other edge

detectors, it would not be included in the multiple edge detector system but can be

considered as a viable alternative to this.

II.

R

EVIEW OF EDGE DETECTOR

A. The Marr-Hildreth Edge Detector

The Marr-Hildreth edge detector was a very popular edge operator before Canny

released his paper. It is a gradient based operator which uses the Laplacian to take the

second derivative of an image. The idea is that if there is a step difference in the

intensity of the image, it will be represented by in the second derivative by a zero

crossing:

2024年5月22日发(作者：微生曼寒)

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@

Sara Sharifzadeh

Department of Communication Engineering, Faculty of Engineering

Shomal higher-education Institute,

P.O. Box: 731, Amol, Iran

sarasharifzade@

Hamid Hassanpour

Department of Computer Engineering, Faculty of Engineering

Mazandaran Institute of Technology

P.O. Box: 744, Babol, Iran

h_hassanpour@

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. I

NTRODUCTION

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

change in the intensity of an image. Edge detection is a fundamental of low-level image

processing and good edges are necessary for higher level processing. [1]

The problem is that in general edge detectors behave very poorly. While their behavior

may fall within tolerances in specific situations, in general edge detectors have

difficulty adapting to different situations. The quality of edge detection is highly

dependent on lighting conditions, the presence of objects of similar intensities, density

of edges in the scene, and noise. While each of these problems can be handled by

adjusting certain values in the edge detector and changing the threshold value for what

is considered an edge, no good method has been determined for automatically setting

these values, so they must be manually changed by an operator each time the detector is

run with a different set of data.

Since different edge detectors work better under different conditions, it would be ideal

to have an algorithm that makes use of multiple edge detectors, applying each one when

the scene conditions are most ideal for its method of detection. In order to create this

system, you must first know which edge detectors perform better under which

conditions. That is the goal of our project. We tested four edge detectors that use

different methods for detecting edges and compared their results under a variety of

situations to determine which detector was preferable under different sets of conditions.

This data could then be used to create a multi-edge-detector system, which analyzes the

scene and runs the edge detector best suited for the current set of data. For one of the

edge detectors we considered two different ways of implementation, one using intensity

only and the other using color information.

We also considered one additional edge detector which takes a different philosophy to

edge detection. Rather than trying to find the ideal edge detector to apply to traditional

photographs, it would be more efficient to merely change the method of photography to

one which is more conducive to edge detection. It makes use of a camera that takes

multiple images in rapid succession under different lighting conditions. Since the

hardware for this sort of edge detection is different than that used with the other edge

detectors, it would not be included in the multiple edge detector system but can be

considered as a viable alternative to this.

II.

R

EVIEW OF EDGE DETECTOR

A. The Marr-Hildreth Edge Detector

The Marr-Hildreth edge detector was a very popular edge operator before Canny

released his paper. It is a gradient based operator which uses the Laplacian to take the

second derivative of an image. The idea is that if there is a step difference in the

intensity of the image, it will be represented by in the second derivative by a zero

crossing: