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Sensors 2011, 11(7), 6697-6718; doi:10.3390/s110706697

Efficient Fuzzy C-Means Architecture for Image Segmentation

Department of Computer Science and Information Engineering, National Taiwan Normal University,Taipei 116, Taiwan
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Received: 2 June 2011 / Revised: 20 June 2011 / Accepted: 24 June 2011 / Published: 27 June 2011
(This article belongs to the Section Physical Sensors)
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Abstract

This paper presents a novel VLSI architecture for image segmentation. The architecture is based on the fuzzy c-means algorithm with spatial constraint for reducing the misclassification rate. In the architecture, the usual iterative operations for updating the membership matrix and cluster centroid are merged into one single updating process to evade the large storage requirement. In addition, an efficient pipelined circuit is used for the updating process for accelerating the computational speed. Experimental results show that the the proposed circuit is an effective alternative for real-time image segmentation with low area cost and low misclassification rate. View Full-Text
Keywords: fuzzy c-means; image segmentation; fuzzy clustering; fuzzy hardware; FPGA; reconfigurable computing; system on programmable chip fuzzy c-means; image segmentation; fuzzy clustering; fuzzy hardware; FPGA; reconfigurable computing; system on programmable chip
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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Li, H.-Y.; Hwang, W.-J.; Chang, C.-Y. Efficient Fuzzy C-Means Architecture for Image Segmentation. Sensors 2011, 11, 6697-6718.

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