Aitali, N. and Cherradi, B. and El Abbassi, A. and Bouattane, O. and Youssfi, M. (2016) GPU based implementation of spatial fuzzy c-means algorithm for image segmentation. In: UNSPECIFIED.

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Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....

Abstract

In this paper a meaningful parallel implementation of spatial fuzzy c-means (SFCM) is presented. It has an advantage of being a powerful tool of classical fuzzy c-means. The great effort made to come up with this work is to reduce significantly its complexity and time execution simultaneously. This technique is inspired by the technological progress of GPUs hardware. The studies we have conducted are very relevant especially for large images. We have implemented this parallel algorithm using Compute Unified Device Architecture (CUDA) on different NVidia GPU cards. The numerical results in terms of execution time demonstrate a gain up to 80x for GTX 580 versus the sequential implementation. © 2016 IEEE.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Uncontrolled Keywords: Clustering algorithms; Copying; Fuzzy clustering; Fuzzy systems; Graphics processing unit; Image segmentation; Program processors, Compute Unified Device Architecture(CUDA); CUDA; Fuzzy C mean; Fuzzy C-means algorithms; Numerical results; Parallel implementations; Sequential implementation; Technological progress, Computer hardware
Subjects: Computer Science
Divisions: SCIENTIFIC PRODUCTION > Computer Science
Depositing User: Administrateur Eprints Administrateur Eprints
Last Modified: 31 Jan 2020 15:46
URI: http://eprints.umi.ac.ma/id/eprint/2592

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