Edge Detection by Adaptive Neuro-Fuzzy Inference System

Author

Zhang, Lei ; Xiao, Mei ; Ma, Jian ; Song, HongXun

Author_Institution

Sch. of Automobile, Chang´´an Univ., Sian, China

fYear

2009

fDate

17-19 Oct. 2009

Firstpage

1

Lastpage

4

Abstract

Neuro-fuzzy (NF) systems are very suitable tools to deal with uncertainty encountered in the process of extracting useful information from images. We present a novel adaptive neuro-fuzzy inference system (ANFIS) for edge detection in digital images. The internal parameters of the proposed ANFIS edge detector are optimized by training using very simple artificial images. The edges are directly determined by ANFIS network. The proposed ANFIS edge detector is tested on popular images having different image properties and also compared with popular edge detectors from the literature. Experimental results show that the proposed ANFIS edge detector exhibits much better performance than the competing operators and may efficiently be used for the detection of edges in digital images.

Keywords

computer vision; edge detection; feature extraction; fuzzy neural nets; fuzzy reasoning; ANFIS; adaptive neuro-fuzzy inference system; artificial image; edge detection; image information extraction; machine vision; Adaptive systems; Data mining; Detectors; Digital images; Fuzzy logic; Fuzzy systems; Image edge detection; Image processing; Noise measurement; Uncertainty;

fLanguage

English

Publisher

ieee

Conference_Titel

Image and Signal Processing, 2009. CISP '09. 2nd International Congress on

Conference_Location

Tianjin

Print_ISBN

978-1-4244-4129-7

Electronic_ISBN

978-1-4244-4131-0

Type

conf

DOI

10.1109/CISP.2009.5304595

Filename

5304595