DEVELOPMENT OF A CLONAL SELECTION ALGORITHM BASED SYSTEM FOR AUTOMATIC DETECTION OF MULTIPLE SHAPES
DEVELOPMENT OF A CLONAL SELECTION ALGORITHM BASED SYSTEM FOR AUTOMATIC DETECTION OF MULTIPLE SHAPES
No Thumbnail Available
Date
2017-09
Authors
OBUTE, Simon Obute
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
In this dissertation a Clonal Selection Algorithm (CSA) based system that detected multiple
instances of circles, quadrilaterals and triangles in an image scene was developed. CSA models
how B-cell antibodies of the immune system protect the body from invading antigens. The
developed system eliminated the need for separate implementations of the CSA for detecting
the different shapes in an image. The implementation of the system was done using MATLAB
2015a, the edges and corners in the image scene were first extracted using Canny edge and
Harris corner detectors and the edge-only image was used as the antigen. A candidate solution
was formed by random selection of three (3) edge points to form a circle, four (4) corner points
to form a quadrilateral and three (3) corner points to form a triangle. The CSA iterated until
either an optimal solution (fitness of 1.0) is attained or when a maximum of 100 iterations is
reached. The antibodies with high fitness (above the set threshold of 0.9 on synthetic images
and 0.5 on real images) were then analysed using a distinctness factor to detect all instances of
the desired shapes and eliminate duplicate detections. A repository of five (5) synthetic images
generated with MATLAB 2015a and six (6) real images captured with digital camera were
used to test the performance of the algorithm. Simulation results showed sub pixel accuracy
with Mean Absolute Error (MAE) between 0.44 and 0.52, Mean Squared Error (MSE) between
0.4722 and 0.5208 and Peak Signal to Noise Ratio (PSNR) between 56.9233 and 57.2381 on
the synthetic test images. False Positive Rate (FPR) of 0% and False Negative Rate (FNR) of
3% were gotten on the synthetic images while the FPR and FNR on real images were 4.76% and
3.82% respectively. The implemented CSA had a mean error score of 0.14 for circle detection
compared to 0.36 and 0.34 for Circle CSA and Learning Automata (LA) representing 61.11%
and 58.82% improvements respectively.
Description
A Thesis Submitted to the Department of Electrical and Computer Engineering,
Ahmadu Bello University, Zaria in Partial Fulfilment of the Requirements for the Award
of Master of Science (M.Sc.) Degree in Control Engineering
Keywords
DEVELOPMENT,, CLONAL SELECTION ALGORITHM,, AUTOMATIC DETECTION,, MULTIPLE SHAPES