FPGA-based online learning hardware architecture for kernel fuzzy c-means algorithm

被引：0

作者：

Ou, Chien-Min ^{[1
]}

Hwang, Wen-Jyi ^{[2
]}

Yang, Ssu-Min ^{[2
]}

机构：

[1] Chien Hsin Univ Sci & Technol, Dept Elect Engn, Zhongli 320, Taiwan

[2] Natl Taiwan Normal Univ, Dept Comp Sci & Informat Engn, Taipei 116, Taiwan

来源：

INDIAN JOURNAL OF ENGINEERING AND MATERIALS SCIENCES | 2013年 / 20卷 / 03期

关键词：

System-on-chip; Image segmentation; Fuzzy clustering; Reconfigurable computing; FPGA;

D O I：

暂无

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

This paper presents a novel embedded system for the online training of kernel fuzzy c-means (KFCM) algorithm. A hardware architecture capable of accelerating the KFCM training process is proposed. The architecture is used as a coprocessor in the embedded system. It consists of efficient circuits for the computation of kernel functions, membership coefficients and cluster centers. In addition, the usual iterative operations for updating the membership matrix and cluster centers are merged into one single updating process to evade the large storage requirement. Experimental results show that the Proposed solution is an effective alternative for image segmentation with low computational cost and low segmentation error rate.

引用

页码：225 / 231

页数：7

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