Abstract
Multi-level image thresholding is an important aspect in many image processing and computer vision applications. In the last decade, many fuzzy based image thresholding techniques have been proposed. In this article a new method for multi-level image thresholding is proposed using Type II Fuzzy sets. A new entropy measure is defined which is maximized to obtain the optimal thresholds for an image. As the number of thresholds increases, exhaustive search appears to be very time consuming. So, Differential Evolution (DE), a meta-heuristic algorithm, is used for fast selection of optimal thresholds. The proposed algorithm is compared with a fuzzy entropy based algorithm using image quality assessment measures Feature Similarity Index Measurement (FSIM) and Gradient Similarity Measurement (GSM). The use of DE is also justified by comparing it with other modern state-of-art algorithms like Gravitational Search Algorithm (GSA), Particle Swarm Optimization (PSO) and Genetic Algorithm (GA).
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Burman, R., Paul, S., Das, S. (2013). A Differential Evolution Approach to Multi-level Image Thresholding Using Type II Fuzzy Sets. In: Panigrahi, B.K., Suganthan, P.N., Das, S., Dash, S.S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2013. Lecture Notes in Computer Science, vol 8297. Springer, Cham. https://doi.org/10.1007/978-3-319-03753-0_25
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DOI: https://doi.org/10.1007/978-3-319-03753-0_25
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