An Edge Detection Algorithm for Online Image Analysis

Azzam Sleit, Abdel latif Abu Dalhoum, Ibraheem Al-Dhamari, Afaf Tareef

Department of Computer Science, King Abdulla II School for Information Technology

University of Jordan, Amman, Jordan

azzam.sleit@ju.edu.jo, a.latif@ju.edu.jo, ibr_ex@yahoo.com, a.tareef@yahoo.com

Abstract: - Online image analysis is used in a wide variety of applications. Edge detection is a fundamental tool used

to obtain features of objects as a prerequisite step to object segmentation. This paper presents a simple and relatively

fast online edge detection algorithm based on second derivative. The proposed edge detector is less sensitive to noise

and may be applied on color, gray and binary images without preprocessing requirements. The merits of the algorithm

are demonstrated by comparison with Canny’s and Sobel’s edge detectors.

Keywords: Edge detection, Canny’s edge detector, Sobel’s edge detector, Wavelet transforms, Derivative operators.

1. Introduction

Several real life applications related to medical

imaging, Geographical Information Systems (GIS) and

Object Character recognition (OCR) depend on the

discovery of edges surrounding objects since they hold

desired features for the objects which appear in the

images. Applying an edge detector to an image may

significantly reduce the amount of data to be processed

and may therefore filter out information that may be

regarded as less relevant, while preserving the

important structural properties of an image. An edge is

a set of connected pixels that lie on the boundary

between two regions reflecting discontinuities in the

brightness of the image due to surface, depth, color, or

illumination [1].

Sobel edge operator is one of the simplest operators

known since 1968. It is a discrete differentiation

operator which computes the approximated gradient of

the image intensity. For each pixel of the image Sobel

operator produces either the corresponding gradient

vector or the norm of the corresponding gradient

vector. The gradient approximation which Sobel

operator produces is crude for high frequency

variations in the image [2].

The Canny edge detector and its variations are

considered the state-of-the-art edge detectors. Canny

showed that the optimal filter is a sum of four

exponential terms. He also showed that this filter can

be well approximated by first-order derivatives of

Gaussians. Canny edge detector is relatively complex

and typically requires noise smoothing, edge

enhancement, and edge localization [3].

There are many other edge detection algorithms which

utilize more complex techniques such as k-means,

neural networks and wavelet transform [4, 5, 6]. Such

techniques have massive run-time requirements which

make them inappropriate for online analysis of video

steams or applications with large sets of images.

Section 2 of this article proposes a fast and simple

edge detection algorithm. Section 3 demonstrates

experimental runs for the proposed algorithm

including comparisons with Sobel and Canny. Section

4 concludes this article.

2. Proposed Edge Detector

Real-time video and image processing is used in a

wide variety of applications from video surveillance

and traffic management to medical imaging

applications. Edge detection is a fundamental tool used

in most image processing applications to obtain

information from the frames as a prerequisite step to

feature extraction and object segmentation. This

section presents a simple and relatively fast online

edge detection approach based on the second

derivative operator. The difference of the neighbor

pixels is a good indicator of an edge in digital images.

Second-order derivative operators such as Laplacian

are sensitive to noise. However, we address this issue

by the following steps:

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1. Get the negative value of the second

of the current pixel.

2. Remove the center pixel value.

3. Subtract the four diagonal pixels values.

Now we can write the operator equation

f(x, y) = - 2∇ f(x, y) – 4f(x, y) – f(x-1, y

– f(x-1, y+1) – f(x+1, y-1) – f(x+1, y+1)

The value of f(x, y) is the color value of the current

pixel with coordination (x, y), and 2

∇ f(x, y)

second derivate of the value f(x, y).

The following operator mask represents the

equation:

-1 1 -1

1 0 1

-1 1 -1

Adding the diagonal values and remove the center

value gives us the necessary balancing for edge

detection and removes undesired noise.

employs the differences between neighbor

with respect to the current pixel to become

value of the current center pixel. The operator

undesired data (colors and noise) and only

edges.

The following algorithm implements the proposed

operator for an image hxw, where, h is the number of

rows and w is the number of columns. The algorithm

has a runtime complexity of ( )hwΘ , where d is

constant.

Input: A =an image with size [height x width

Output: B=the edges image of the image A.

[h w d]=size(A);// the dimensions of the image

for k=1 to d

for x=1 to h

for y=1 to w

B(x,y,k)=-A(x-1,y-1)+A(x-1,y)-A(x-

+A(x,y-1) -A(x,y+1) -A(x+1,y-

-A(x+1,y+1);

Get the negative value of the second derivative

diagonal pixels values.

operator equation as follow:

1, y-1)

f(x+1, y+1)

the color value of the current

f(x, y) is the

the above

Adding the diagonal values and remove the center

value gives us the necessary balancing for edge

undesired noise. The operator

between neighboring pixels

come the new

The operator removes

only holds the

algorithm implements the proposed

operator for an image hxw, where, h is the number of

columns. The algorithm

, where d is

height x width] .

mage A.

d]=size(A);// the dimensions of the image

-1,y+1)

-1)+A(x+1,y)

The simplicity of the algorithm

be implemented by hardware which

resolution and large size

frames.

3. Experiments and Sample Runs

We have implemented the proposed operator along

with Sobel and Canny using M

the outcome of the proposed operator with Sobel and

Canny for the leaf image.

in figures 2-7. It is clear that the proposed operator is

less sensitive to noise than both Sobel and Canny. The

fact that our operator detects edges with exactly one

pass over the image with simple mathematical

operations applied on each pixel makes it appropriate

for online image analysis.

(c)

(a)

Fig 1. (a) Leaf image

Sobel (c) Edge-detection using Canny

detection using proposed

the algorithm makes it possible to

hardware which is suitable for high

large size images such as satellite

and Sample Runs

the proposed operator along

using Matlab. Fig 1 compares

the outcome of the proposed operator with Sobel and

More runs are demonstrated

It is clear that the proposed operator is

less sensitive to noise than both Sobel and Canny. The

fact that our operator detects edges with exactly one

pass over the image with simple mathematical

operations applied on each pixel makes it appropriate

(b)

(d)

(b) Edge-detection using

etection using Canny (d) Edge-

etection using proposed algorithm.

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4. The Original Image

Sobel Edges

Canny Edges

Proposed Algorithm

Fig 2. Chalks image

The Original Image

Sobel Edges

Canny Edges

Proposed Algorithm

Fig 3. LKMagenta image Fig

The Original Image

Sobel Edges

Canny Edges

Proposed Algorithm

Fig 4. Random image

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The Original Image

Sobel Edges

Canny Edges

Proposed Algorithm

Fig 5. Sample image

The Original Image

Sobel Edges

Canny Edges

Proposed Algorithm

Fig 6. Rose image

The Original Image

Sobel Edges

Canny Edges

Proposed Algorithm

Fig 7. Washington image

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Conclusion

This paper introduces a fast edge detection algorithm

which runs in ( )hwΘ , where h and w are the height

and width of the source image. The algorithm utilizes

8 operations per pixel in the source image which

makes it appropriate for large size images and image

streams. Experiments demonstrate the merits of the

proposed operator as it is less sensitive to noise than

Sobel’s and Canny’s edge detectors.

As future work, we will investigate the merits of the

operator for optical character recognition.

References

[1] Rafael C. Gonzalez and Richard E. Woods, 2008,

Digital Image Processing, 3rd edition.

[2] Sobel, I., Feldman,G., A 3x3 Isotropic Gradient

Operator for Image Processing", presented at a talk at

the Stanford Artificial Project in 1968, unpublished.

[3] Canny, J., 1986, A Computational Approach to

Edge Detection, IEEE Trans. Pattern Analysis and

Machine Intelligence, Vol. 8, Issue 6, pp. 679 - 698.

[4] Suzuki, K., Horiba, I., and Sugie, N., 2003, Neural

Edge Enhancer for Supervised Edge Enhancement

from Noisy Image, IEEE Trans. Pattern Analysis and

Machine Intelligence, Vol. 25 , Issue 12, pp. 1582 –

1596.

[5] Guowei T., Xiaoqing Z., Fangzhou Z., Zhenying J.,

2009, X-Ray Image Edge Detection Based on

Wavelet Transform and Lipschitz Exponent, Second

International Symposium on Intelligent Information

Technology and Security Informatics, pp. 56-66.

[6] Ganguly, D., Mukherjee, S., Mitra, K., Mukherjee,

P., 2009, A Novel Approach for Edge Detection of

Images, International Conference on Computer

and Automation Engineering, pp. 49-53.

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ISSN: 1790-2769 254 ISBN: 978-960-474-150-2