design and development of pest image segmentation technique using soft computing algorithm
Pravin Kumar.S.K,Sumithra.M.G,Saranya.N
Published in International Journal of Advanced Research in Electronics, Communication & Instrumentation Engineering and Development
ISSN: 2347 -7210 Impact Factor:1.9 Volume:2 Issue:2 Year: 25 October,2014 Pages:29-33
Abstract
Image segmentation is a major step for automated object recognition systems. In many cases, image processing is affected by illumination conditions, random noise and environmental disturbances due to atmospheric pressure or temperature fluctuation. The quality of pest images is directly affected by atmosphere medium, pressure and temperature. This emphasizes the necessity of image segmentation, which divides an image into parts that have strong correlations with objects to reflect the actual information collected from the real world. Image segmentation is the most practical approach among virtually all automated image recognition systems. The performance of an image segmentation algorithm depends on its simplification of image. The different segmentation algorithms namely, fixed threshold, Experience threshold, Iteration method, OTSU method and fuzzy c-means segmentation [5] are implemented for pest images and they are compared using nonlinear assessment or the quantitative measures like gray level energy, entropy, and normalized mutual information. Out of the above methods the experimental results show that fuzzy c means clustering algorithm performs better than other methods in processing pest images. FCM based simulated annealing algorithm [2] provides better results than other intelligent techniques .
Kewords
segmentation, energy, entropy, Mutual information, simulated-annealing algorithm, fcm clustering
Reference
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