A particle Swarm Optimization Based Edge Detection Algorithm for Noisy Coloured Images
No Thumbnail Available
Date
2016-12
Journal Title
Journal ISSN
Volume Title
Publisher
Indian Academicians and Researchers Association, Indian
Abstract
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.
Description
Keywords
Image Edge Detection, Particle Swarm Optimization (PSO), Pratt Figure of Merit (PFOM), Noisy coloured Images, Vector Order Statistics