Development Of An Improved Edge Detection Algorithm For Noisy Coloured Images Using Particle Swarm Optimization

📄 Project 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 ...