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.