Abstract
Image segmentation is the most important part of image processing, and has a large impact on quantitative image analysis. Among many segmentation methods, thresholding based segmentation is widely used. In thresholding method, selection of optimum thresholds has remained a challenge over decades. In order to determine thresholds, most of the methods analyze the histogram of the image. The optimal thresholds are found by optimizing an objective function built around image histogram. The classical segmentation methods often fails to give good result for images whose histograms have multiple peaks. Since Swarm algorithms have shown promising results on multimodal problems, hence the alternative methods for optimal image segmentation. This papers presents the comprehensive analysis of Swarm algorithms for determining the optimal thresholds on standard benchmark images. An exhaustive survey of various Swarm algorithms on multilevel image thresholding was carried out and finally comprehensive performance comparison is presented both in numerical and pictorial form.
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Ali, L. (2015). Multilevel Thresholding in Image Segmentation Using Swarm Algorithms. In: Satapathy, S., Govardhan, A., Raju, K., Mandal, J. (eds) Emerging ICT for Bridging the Future - Proceedings of the 49th Annual Convention of the Computer Society of India CSI Volume 2. Advances in Intelligent Systems and Computing, vol 338. Springer, Cham. https://doi.org/10.1007/978-3-319-13731-5_23
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DOI: https://doi.org/10.1007/978-3-319-13731-5_23
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