Adaptive fuzzy edge detector for image enhancement

Chang Shing Lee, Yau Hwang Kuo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

A novel adaptive fuzzy edge detector for image enhancement, which can work well in full range of random impulse noise probability and perform efficiently in the environment of mixed Gaussian impulse noise, is proposed. It is an extended adaptive weighted fuzzy mean (EAWFM) filter, which combines adaptive weighted fuzzy mean filter and fuzzy normed inference system to efficiently perform edge detection in smeared images. The membership functions of all fuzzy sets used in EAWFM can be adaptively determined for different images, and EAWFM filter is capable of converting blurred edges to clear ones and suppressing noise at the same time. The important properties of EAWFM filter are analyzed and some experimental results are presented to show its excellent performance.

Original languageEnglish
Title of host publication1998 IEEE International Conference on Fuzzy Systems Proceedings - IEEE World Congress on Computational Intelligence
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1542-1547
Number of pages6
ISBN (Print)078034863X, 9780780348639
DOIs
Publication statusPublished - 1998 Jan 1
Event1998 IEEE International Conference on Fuzzy Systems, FUZZY 1998 - Anchorage, United States
Duration: 1998 May 41998 May 9

Publication series

Name1998 IEEE International Conference on Fuzzy Systems Proceedings - IEEE World Congress on Computational Intelligence
Volume2

Other

Other1998 IEEE International Conference on Fuzzy Systems, FUZZY 1998
CountryUnited States
CityAnchorage
Period98-05-0498-05-09

All Science Journal Classification (ASJC) codes

  • Logic
  • Control and Optimization
  • Modelling and Simulation
  • Chemical Health and Safety
  • Software
  • Safety, Risk, Reliability and Quality

Fingerprint Dive into the research topics of 'Adaptive fuzzy edge detector for image enhancement'. Together they form a unique fingerprint.

Cite this