Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 225-231 |
| Number of pages | 7 |
| Journal | Indian Journal of Engineering and Materials Sciences |
| Volume | 20 |
| Issue number | 3 |
| Publication status | Published - 2013 Jun 1 |
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Keywords
- FPGA
- Fuzzy clustering
- Image segmentation
- Reconfigurable computing
- System-on-chip
ASJC Scopus subject areas
- Materials Science(all)
- Engineering(all)
Cite this
FPGA-based online learning hardware architecture for kernel fuzzy c-means algorithm. / Ou, Chien Min; Hwang, Wen-Jyi; Yang, Ssu Min.
In: Indian Journal of Engineering and Materials Sciences, Vol. 20, No. 3, 01.06.2013, p. 225-231.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - FPGA-based online learning hardware architecture for kernel fuzzy c-means algorithm
AU - Ou, Chien Min
AU - Hwang, Wen-Jyi
AU - Yang, Ssu Min
PY - 2013/6/1
Y1 - 2013/6/1
N2 - 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.
AB - 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.
KW - FPGA
KW - Fuzzy clustering
KW - Image segmentation
KW - Reconfigurable computing
KW - System-on-chip
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UR - http://www.scopus.com/inward/citedby.url?scp=84879038358&partnerID=8YFLogxK
M3 - Article
AN - SCOPUS:84879038358
VL - 20
SP - 225
EP - 231
JO - Indian Journal of Engineering and Materials Sciences
JF - Indian Journal of Engineering and Materials Sciences
SN - 0971-4588
IS - 3
ER -