International Journal of Advanced Computer Engineering and Communication Technology (IJACECT)
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Edge detection using Fuzzy Logic
1
Richa Garg, 2Beant Kaur
1
M. Tech student, 2Assistant Professor, Department of Electronics & Communication,
Punjabi University, Patiala
Email: 1richapta@gmail.com
Abstract :- Edge detection is one of the most important
steps in image processing. In fact Image segmentation,
registration and identification are also based on edge
detection. There are some techniques for edge detection
such as Sobel, Preweitt, Laplacian and Laplacian of
Gaussian. But it has some limitations like fixed edge
thickness and some parameter like threshold is difficult to
implement. The fuzzy rule-based technique does not have
such limitation, as we can change the edge thickness simply
by changing rules and output parameters.
This paper presents the edge detection by fuzzy rule based
algorithm, which is able to detect edges efficiently from the
gray scale images. The proposed method is demonstrated
in comparison with the existing sobel edge detector.
Keywords — Fuzzy logic, Edge detection, Threshold,
Image Processing, Sobel edge detector
Fuzzy logic has many unique features that make it a
good choice for many control problems. It does not
require precise i.e. noise-free inputs and output control
is a smooth control function despite a wide range of
input variations. Any reasonable number of inputs can
be processed and numerous outputs can be generated,
Fuzzy logic can control nonlinear systems that would be
difficult or impossible to model mathematically. Fuzzy
rule structures are easily interpreted by the human
beings [2].
C.
Edge detection
Edge detection is a very important low-level image
processing operation, and is used in various higher level
tasks such as motion and feature analysis,
understanding, recognition and retrieval from databases.
[3].
I. INTRODUCTION
Fuzzy Image processing is the technique which is used
for understanding, representing and processing the
images, their segments and features as fuzzy sets [1].
A.
Image Processing
Image processing is any form of signal processing in
which the input is an image, such as a photograph or
video frame, the output of image processing may be
either an image or a set of characteristics or parameters
related to the image. Image processing technique may be
Image enhancement, Image restoration and Image
compression etc.[1]
B.
Fuzzy logic
Fuzzy logic is a form of many-valued logic. Fuzzy logic
variables may have a truth value that ranges in degree
between 0 and 1. It aims at modeling the imprecise
modes with reasoning that is approximate rather than
fixed of reasoning that play an essential role in the
remarkable human ability to make rational decisions in
an environment of uncertainty and imprecision
Edge detection is one of the most important tasks in
image processing including segmentation, registration;
identification and recognition are based on edge
detection algorithm. Most of the edge detection has
fixed result such as thickness of edges or some
parameters must be selected certainly for good result
such as threshold and σ. But the fuzzy logic approach
doesn’t have this restricts. Simply, fixing some
parameters changes the result of processing.
1.
Image gradient:
Image gradient is the method which is used to extract
information from images. It gives us the information in
two pieces. The gradient has a magnitude and direction
in which the magnitude tells us how quickly the image is
changing and the direction tell us the direction in which
the image is changing most rapidly. [4]
The gradient of an image is given by the formula:
…………(1)
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ISSN (Print): 2319-2526, Volume-3, Issue-3, 2014
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International Journal of Advanced Computer Engineering and Communication Technology (IJACECT)
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where :
Ix and Iy are input of image gradient along x-axis and yaxis and Iout is the desired Output.
is the gradient in the x direction
is the gradient in the y direction.
2.
Step 5: Defuzzify the image to get the output. The
output is as shown in fig. 4(d). If sobel operator is used
then the output image after applying fuzzy rules is as
shown in fig. 4(c).
Sobel:
The Sobel operator performs a 2-D spatial gradient
measurement on an image and so emphasizes regions
of high spatial frequency that correspond to edges.
Typically it is used to find the approximate absolute
gradient magnitude at each point in an input grayscale
image. The gradient magnitude is given by:
……….(2)
Start
Input Image
convert RGB image to Gray scale
image
Where
Gx and Gy are the sobel gradient along x-axis and y-axis
and G is the mean square value.
II. PROPOSED METHOD
Apply Sobel Edge Detection and
obtian the output
Give the values of two parameters
to the Fuzzy Integral System
The Proposed methods which is used for edge detection
are in the following steps:
Step 1: Input an RGB Image and convert the RGB
image to Gray scale image. It is given by
Y’ = 0.299R’ + 0.587G’ + 0.114B’ …….(3)
Step 2: Obtain the gradient along magnitude and
direction for image gradient.
If sobel detector is used then sobel algorithm is applied.
Now obtain the output of the sobel before applying
fuzzy rules as shown in fig. 4(b).
Step 3: Define Membership function for the fuzzy
inference system. The input 1 , input 2 and output
membership is shown in figure 3(a), (b) & (c) resp.
Step 4: Define the fuzzy rules to obtain the output
image. If else rules is applied as given
Define Membership function of
inputs and output
Define Fuzzy rules for obtaining
Image Edges
Defuzzification
Output Image
Stop
Fig. 1 Shows the flowchart for the sobel edge detection
method before and after applying fuzzy rules
If
Ix is zero and Iy is zero then Iout is white
Else If
Ix is not zero or Iy is not zero then Iout is black
Where
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International Journal of Advanced Computer Engineering and Communication Technology (IJACECT)
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Start
Input Image
convert RGB image to Gray
scale image
Obtain image gradient along
X-direction and Y-direction
(b)
Apply these values to the
Fuzzy Integral System
Define Membership function
of inputs and output
Define Fuzzy rules for
obtaining Image Edges
(c)
Defuzzification
Fig. 3 Membership functions for (a) Ix (b) Iy (c) output
III. RESULTS
Output Image
Stop
Fig. 2 Shows the flowchart for edge detection based on
fuzzy logic.
In this paper, we represent some experimental results of
our proposed method. The original image, the result
based images on Sobel edge detector before applying
fuzzy rules, Sobel edge detector after applying fuzzy
rules and Edge detector using Fuzzy logic are as shown
in fig. 4.
(a)
(b)
(a)
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International Journal of Advanced Computer Engineering and Communication Technology (IJACECT)
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(c)
[3]
Debashis, Sen; Sankar, K. Pal; “Gradient
histogram: Thresholding in a region of interest
for edge detection,” Image and Vision
Computing, Vol. 28, pp. 677–695, 2010.
[4]
David Jacobs; “Image Gradients”, Class Notes
for CMSC 426, Fall 2005
[5]
Thakkar,
M.;
Shah,
H.;
“Automatic
Thresholding in Edge Detection Using Fuzzy
Approach,” Dec 2010, IEEE conference on
Computational Intelligence and Computing
Research, pp. 1-4
[6]
Somasundaram, K.; and Ezhilarasan, K.; “Edge
Detection
using
Fuzzy
Logic
and
Thresholding”, 2012 National conference on
signal and image processing, pp. 157 - 160.
[7]
Patel,D.K.; and Sagar, A. K.; “Edge Detection
Technique by Fuzzy Logic and Cellular
Learning Automata using Fuzzy Image
Processing”, Dec 2013, IEEE conference on
computer communication and Informatics, pp.
1 - 6.
[8]
Kaur, K.; Gill, I.S. and Mutenja, V.; “Fuzzy
Logic Based Image Edge Detection Algorithm
in MATLAB”, 2010 International journal of
computer application, pp. 55 - 59.
[9]
M. Abdullah-Al-Wadud et al., “A Dynamic
Histogram Equalization for Image Contrast
Enhancement”, IEEE Trans., Consumer
Electronics, vol.53, no. 2, pp. 593–600, May
2007.
(d)
Fig. 4(a) Original Image (b) Sobel Edge Detection
before applying fuzzy rule(c) Sobel Edge Detection after
applying Fuzzy rule. (d) Edge Detection using Fuzzy
Logic
IV. CONCLUSION
It has been concluded from the subjective approach that
Fuzzy logic based Edge detection gives more accurate
result as compared to sobel.
REFERENCES
[1]
[2]
Iqbal, J.; mehmood, A.K.; Saadia, T.; Sabahat,
z.; “IMPLEMENTING BALL BALANCING
BEAM
USING
DIGITAL
IMAGE
PROCESSING AND FUZZY LOGIC”, 2005
IEEE, may 2005 canadian conference on
electrical and computer engineering, pp. 2241 2244.
salinas, R.M.; aguirre, E.; cordon, O.; silvente,
M.G “Automatic Tuning of a Fuzzy Visual
System Using Evolutionary Algorithms:
Single-Objective Versus Multi objective
Approaches” April 2008 IEEE Trans. on Fuzzy
System, vol.16, no. 2, pp.485-500.
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