Efficient fuzzy c-means architecture for image segmentation

Hui Ya Li, Wen Jyi Hwang, Chia Yen Chang

Research output: Contribution to journalArticle

7 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)6697-6718
Number of pages22
JournalSensors
Volume11
Issue number7
DOIs
Publication statusPublished - 2011 Jul 1

Fingerprint

Image segmentation
Costs and Cost Analysis
Networks (circuits)
very large scale integration
centroids
costs
Costs
requirements
matrices

Keywords

  • FPGA
  • Fuzzy c-means
  • Fuzzy clustering
  • Fuzzy hardware
  • Image segmentation
  • Reconfigurable computing
  • System on programmable chip

ASJC Scopus subject areas

  • Analytical Chemistry
  • Biochemistry
  • Atomic and Molecular Physics, and Optics
  • Instrumentation
  • Electrical and Electronic Engineering

Cite this

Efficient fuzzy c-means architecture for image segmentation. / Li, Hui Ya; Hwang, Wen Jyi; Chang, Chia Yen.

In: Sensors, Vol. 11, No. 7, 01.07.2011, p. 6697-6718.

Research output: Contribution to journalArticle

Li, Hui Ya ; Hwang, Wen Jyi ; Chang, Chia Yen. / Efficient fuzzy c-means architecture for image segmentation. In: Sensors. 2011 ; Vol. 11, No. 7. pp. 6697-6718.
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