GPU Accelerated Fuzzy C-Means (FCM) Color Image Segmentation


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Title GPU Accelerated Fuzzy C-Means (FCM) Color Image Segmentation
Creator Akbar, Mutaqin
Witanti, Arita
Susilawati, Indah
Subject Computer Vision; Parallel Processing; Graphical Processing Unit
computational acceleration, FCM, GPU, image segmentation
Description In this paper, computational acceleration of color image segmentation using fuzzy c-means (FCM) algorithm has been presented. The color image is first converted from the Red Green Blue (RGB) color space to the YUV color space. Then, the luma (Y) information values are grouped according to the desired number of clusters using the FCM algorithm. The FCM algorithm is implemented on a Graphical Processing Unit (GPU) using the Compute Unified Device Library (CUDA) library which is developed by NVidia to speed up the computing time. Images used in this research are red blood cell images, geometry images and leaf images. The results of segmented images processed using GPU were seen identic to the results of segmented images processed using the Central Processing Unit (CPU). The computational time of the FCM algorithm can be accelerated by speed-up to 5,628 times faster and the average speed-up of all simulations done is 5,517 times faster.
Publisher Sekolah Tinggi Teknologi Adisutjipto Yogyakarta
Contributor Universitas Mercu Buana Yogyakarta
Date 2019-11-01
Type info:eu-repo/semantics/article

Format application/pdf
Source Compiler; Vol 8, No 2 (2019): November; 165-174
Language ind
Rights Copyright (c) 2019 Compiler

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