A particle Swarm Optimization Based Edge Detection Algorithm for Noisy Coloured Images

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Indian Academicians and Researchers Association, Indian


This paper presents an improved edge detection algorithm using particle swarm optimization (PSO) based on vector order statistics. The proposed algorithm was implemented using MATLAB 2013 script. The algorithm addressed the performance of edge detection in noisy coloured images, with a view to minimizing broken, false and thick edges whilst reducing the presence of noise. A collection scheme based on step and ramp edges was applied to 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 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, Cannyand Laplacian edge detection algorithms. The Pratt Figure of Merit (PFOM) was used as a quantitative comparison between the proposed algorithm and the proven edge detection algorithms. 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 developed algorithm will perform better than the existing edge detection algorithm in multimedia systems.



Image Edge Detection, Particle Swarm Optimization (PSO), Pratt Figure of Merit (PFOM), Noisy coloured Images, Vector Order Statistics