ABSTRACT
This research work presents an improved edge detection algorithm using
particle swarm optimization based on vector order statistics. The proposed
algorithm was implemented using MATLAB 2013 script. The algorithm addressed the
performance of edge detection in images, with a view to minimizing broken,
false and thick edges whilst reducing the presence of noise as well as
computational time. A collection scheme based on step and ramp edges was
developed for the edge detection algorithm, which explores a larger area in the
images in order to reduce false and broken edges. The efficiency of this
algorithm was tested on two Berkeley benchmark images in clean and noisy
environments with a view to comparing results, both visually and
quantitatively, with those obtained using proven edge detection algorithms such
as the Sobel, Prewitt, Roberts, Laplacian and Canny edge detection algorithms.
The algorithm was also applied to facial and remotely sensed images with a view
to testing the algorithm on real life images. The Pratt Figure of Merit (PFOM)
was used as a quantitative comparison between the developed algorithm and the
proven edge detection algorithms. The benchmark value for the PFOM is between
0-1, which shows efficient detection of edges as the value tends towards 1. The
quantitative results obtained using PFOM on the test images in clean
environment for the Sobel, Prewitt, Roberts, Laplacian, Canny and the proposed
edge detection algorithms are 0.4209, 0.4195, 0.4181, 0.7048, 0.8421 and
0.8480, respectively. This showed that the proposed algorithm detected more
edges in clean environment as the value obtained is nearest to 1. The PFOM on
the test images in noisy environment for the Sobel, Prewitt, Roberts,
Laplacian, Canny and the proposed edge detection algorithms are 0.4191, 0.4191,
0.2807, 0.2811, 0.5606 and 0.8458 respectively. This showed that the proposed
algorithm detected more edges in noisy environment as the value obtained is
nearest to 1. The proposed algorithm achieved a PeakSignal-to Noise Ratio (PSNR)
of 57.7320dB in environment containing ≤ 33% of noise level. This result
signifies 3% improvement in detection of edges in noisy environment as compared
with the proven traditional edge detection algorithms which achieved an average
PSNR of 22-35dB.
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LIST OF
FIGURES
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Figure
2.1: Relationship between Image Processing and Other Fields
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Figure
2.2: An Illustration of Gray Scale Image
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Figure
2.3: Illustration of RGB Colour Space as 3-D Cube
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Figure
2.4: HSV colour space as 3-D Cone
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Figure
2.5: A 3x3 Filter Kernel
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Figure 2.6:A
3x3 Convolution Kernel with an Image
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Figure
2.7: Gradient Direction of an Image
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Figure
2.8:4 & 8-Neighbors of C
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Figure
2.9: Arrangement of Neighborhood Pixels
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Figure
2.10: 8-Connected and Adjacent Pixel
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Figure
2.11: A 2x2 Kernel of Robert Edge Detection Algorithm
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Figure
2.12: A 2x2 Kernel of Sobel Edge Detection Algorithm
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Figure
2.13: A 3x3 Kernel of Prewitt Edge Detection Algorithm
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Figure2.14:
Three Commonly used Laplacian Convolution Kernel
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Figure
2.15: General Flow Chart of PSO algorithm
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Figure
2.16: Edge Detection Pixel Arrangement
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Figure
2.17: A Representation of Step and Roof Edges
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Figure
2.18: Test Images Used
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Figure
2.19: Application of the Edge Detection Algorithm to Face Detection
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Figure
2.20: Sample of Natural Faces
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Figure
2.21: Remotely Sensed Image Used
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Figure
3.1: Block diagram of the Proposed Algorithm
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Figure
3.2: A Collection of Pixels Based on Step Edges
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Figure
3.3: A Collection of Pixels Based on Roof Edges
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Figure
3.4: Integer Notation for 8-Neighborhood Pixel in a 3x3 Window
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Figure
3.5: Collection Scheme for Step Edge
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Figure
3.6: Collection Scheme for Roof Edge
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Figure
3.7: A 3x3 Window Used to Find the Gradient of the Image
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Figure
3.8: Flow Chart of PSO based on VOS algorithm
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Figure
3.9: Extracted Input Image from Computer Databases
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Figure
3.10: Output of the Vector Order Statistics
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Figure
3.11: Output Result of the Suppressed Edges
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Figure4.1:
Output Result on Test Image (1)
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Figure4.2:
Output Result on Test Image (2)
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Figure4.3:
PSNR for Various Noise Levels
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Figure4.4:
Output Result of Applying the Proposed Algorithm on Facial Image
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Figure4.5:
Output Result of Applying Proposed Algorithm on Remotely Sensed Image
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Figure4.6:
Sobel Edge Detection Algorithm in Clean and Noisy environment
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Figure4.7:
Prewitt Edge Detection Algorithm in Clean and Noisy environment
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Figure4.8:
Robert Edge Detection Algorithm in Clean and Noisy environment
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Figure4.9:
Laplacian Edge Detection Algorithm in Clean and Noisy environment
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Figure4.10:
Canny Edge Detection Algorithm in Clean and Noisy environment
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Figure4.11:Proposed
Edge Detection Algorithm in Clean and Noisy environment
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Figure4.12:
Sample Shape Used to Test the Algorithm
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Figure 4.13:Comparison of
Images Produced by
Different Algorithms on
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Sample
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Shape
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Figure4.14:Result
of the Proposed Algorithm in Visual Comparison with other work
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Figure 4.15:
Result of the
Proposed Algorithm in
Visual Comparison with
other
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work
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Figure4.16:
Quantitative Comparison Using Pratt Figure of Merit (PFOM)
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LIST OF
TABLES
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Table
4.1: Noise Levels with their respective PSNR
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Table
4.2: Computation Time
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Table
4.3: Pratt Figure of Merit for Various Edge Detection Algorithm
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PSO Particle Swarm Optimization
VOS Vector Order Statistics
PFOM Pratt Figure of Merit
PSNR Peak Signal-to-Noise Ratio
SEDA Sobel Edge Detection Algorithm
REDA Roberts Edge Detection Algorithm
PEDA Prewitt Edge Detection Algorithm
LEDA Laplacian Edge Detection Algorithm
CEDA Canny Edge Detection Algorithm
DEDA Proposed Edge Detection Algorithm
CHAPTER ONE
INTRODUCTION
1.1 Background
Edge detection can be defined as the process of identifying set of
connected pixels that forms a boundary between two disjoint regions (Gang et al., 2008).
It can also be defined as the process of locating and identifying sharp
discontinuities in images (Rashmi et al., 2013). It is mostly used in image analysis to
preserve image features and partition images into regions of interest. The
discontinuities in these images can be caused by (Ghasemi et al., 2011):
i.
Discontinuity in depth and/or surface colour and
texture.
ii.
Reflection of light, Shadows and Illumination.
Edge detection is an image segmentation technique in which images are
partitioned into meaningful regions of interest. Some of the practical
applications of edge detection algorithms are in face and finger print
recognition, location of objects in satellite images, medical images, and
computer aided surgery or diagnosis amongst others (Rashmi et al., 2013). One of the
most important challenges of edge detection algorithm is to detect the edges in
noisy images. Many traditional edge detection algorithms have been developed to
overcome noise such as Sobel, Prewitt, Roberts and Gradient based edge
detection algorithms etc. (Rashmi et al., 2013). These traditional edge detection
algorithms are very fast but they cannot perform well on noisy images. Hence,
the significant problem of these edge detection algorithms are displacement,
removed edges, false and broken edges(Maini &
Aggarwal, 2011). Noise phenomenon is an obstacle in detection of
continuous edges as it causes some variation of pixel intensities, thus
reducing the performance of an edge detection algorithm in noisy images (Setayesh et al., 2013).
It also leads to unclear and displaced edges (Chaudhary
& Gulati, 2013). Many edge detection algorithms
have been developed in the literature over the past years to improve precision
of recognized edges. However, they still suffer from producing broken edges and
false edges due to noise effect (Maini & Aggarwal, 2011).
Therefore, an improved edge detection algorithm is required to detect edges
with greater continuity in noisy images in order to reduce the shortcomings of
traditional edge detection algorithms. In the field of image processing, there
exists basically two types of images which are the gray scale and the coloured
images. Numerous researchers have developed edge detection algorithms for gray
scale images in the past. But in recent times, with improvement in computer
capabilities and the increased applications of coloured images there is need to
develop an effective edge detection algorithm for coloured images (Haque & Aljahdali, 2013). Some of the applications of the
edge detection algorithms are in image segmentation, image compression, face
recognition, computer vision, computer surveillance, medical diagnosis, image
encryption/communication multimedia and remotely sensed images, amongst others(Vijayarani & Vinupriya, 2013). In the areas of
medical diagnosis, satellite images, face recognition, and computer
surveillance, representation of images by its edges reduced the amount of data
required to be stored whilst retaining useful information in the image.
Most edge detection algorithms process a single pixel on an image at a
time and calculate a value which shows the edge magnitude of the pixel, and the
edge orientation. Then, a thresholding technique is utilized to recognize if a
pixel is an edge or not (Rai & Dutta, 2013) and
the result will be a binary image which indicates the location of all existing
edges on the original image. The edges detected by these algorithms are not
usually linked and there is no relation among the edge pixels. To solve this
problem, most edge detection algorithms utilize a linking technique such as the
Hough transform, sequential edge linking etc. However, the linking process in
such techniques is not good except for the edges on simple
shapes such as circles or lines (Setayesh et al., 2013). Most of the edge detection algorithms
were developed to perform on gray level images
as compared with coloured images. This is because edge detection in coloured
images is a far more challenging task, and the criteria for a good edge
detection algorithm are (Maini & Aggarwal, 2011):
i.
The optimal detection must
minimize the probability of false positives (detecting spurious edges caused by
noise), as well as that of false negatives (missing real edges).
ii.
The edges detected must be as close as possible to
the true edges.
1.2 Statement of Problem
The traditional edge detection algorithms use limited or small area to
detect a pixel as an edge. Edge detection is very crucial in image processing (Ghasemi et al., 2011) and
size of the area being considered has strong influence on the accuracy of the
detection (Setayesh et al.,
2013). The larger the area, the less the sensitivity to noise, but
at the same time, the localization accuracy is lower. In order to increase the
localization accuracy of the algorithm, there is the need to consider all the
edge patterns. However, this increases the computation time and produces broken
and false edges. In this research work, a particle swarm optimization edge
detection algorithm for coloured images based on vector order statistics is
proposed in order to reduce false and broken edges as well as computational
time by exploring a larger area in the noisy images
1.3 Aim and Objectives
The aim
of this research work is to develop a particle swarm optimization (PSO) edge
detection algorithm for noisy coloured images based on vector order statistics
with a view to reducing false and broken edges as well as computational time by
exploring a larger area in the noisy images. The objectives are as follows:
i.
Development of a collection scheme
for set of pixels in coloured images with a view to reducing false and broken
edges in the image.
ii.
Development of a particle swarm
optimization edge detection algorithm for noisy coloured images based on vector
order statistics.
iii.
Validation of the proposed
algorithm in (ii) and comparison with the traditional edge detection algorithms
using Pratt Figure of Merit (PFOM)
1.4 Methodology
The following methodology was
adopted in carrying out this research:-
i.
Developingacollection scheme to detect edges in
coloured images.
ii.
Exploring a larger area and
examine normally occurring edge patterns in order to increase the localization
accuracy of edge detection and determine which edge pixels should be discarded
as noise and which should be retained.
iii.
Extracting the global structure
of edges in order to detect the edges with greater continuity. Hence, determine
the exact location of an edge.
iv.
Testing the proposed algorithm in
MATLAB 2013b image processing toolbox on two coloured images obtained from the
Berkeley benchmark image database, remotely sensed image generated using Google
earth software and real facial images.
v.
Validation and testing of the proposed edge
detection algorithm
1.5 Significant Contributions
The existing traditional edge detection algorithms use a single pixel on
an image at a time to calculate a value which shows the edge magnitude of the
pixel, and the edge orientation. However, this leads to false and broken edges
in the generated output edge map. Therefore, the significant contributions of
this research work are itemized as follows:-
i
Development of a scheme for
collection of pixels based on Step and Ramp edges with a view to reducing false
and broken edges that exists when generating the output edge map.
ii
Application of Vector order
Statistics based on collection of pixels for edge detection in noisy coloured
images. An improved Pratt Figure of Merit (PFOM) value of 0.09% and 33.7% were
obtainedin clean and noisy environments, as compared to the best amongst the
existing traditional edge detection algorithms.
iii
The proposed edge detection
algorithm produced thin and continuous edges in noisy environment and achieved
a PSNR of 57.732dB. This represented a 3% improvement in detection of edges in
noisy environments as compared with other proven techniques such as the Sobel,
Prewitt, Roberts, Laplacian and Canny.
1.6 Dissertation Organization
The general introduction has been presented in chapter one. The rest of
the chapters are presented as follows: a detailed review of the fundamental
concepts of image processing, edge detection as well as a review of similar
research works is presented in chapter two. Profile modelling of edge intensity,
mathematical equations and formulation of the problem are presented
in chapter three. Analysis and discussions of the results are presented in
chapter four.
Summary, conclusions limitations
and recommendations are presented in Chapter Five.
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