Image segmentation via fuzzy additive hybrid networks

Author

Keller, James M. ; Chen, Zhihong

Author_Institution

Dept. of Electr. & Comput. Eng., Missouri Univ., Columbia, MO, USA

fYear

1994

fDate

18-21 Dec 1994

Firstpage

60

Lastpage

64

Abstract

We have recently introduced a class of additive hybrid fuzzy set theoretic operators which can be inserted into neural network structures and trained to learn multicriteria decision making functions through a backpropagation-type algorithm. In this paper, we examine the use of such networks to aggregate evidence and perform supervised image segmentation. Such algorithms not only group like pixels together, but also provide class labels due to the training. The resultant network is used to segment a color image of a natural scene

Keywords

backpropagation; fuzzy logic; image segmentation; backpropagation-type algorithm; class labels; evidence; fuzzy additive hybrid networks; fuzzy set theoretic operators; image segmentation; multicriteria decision making functions; neural network structures; pixels; Backpropagation algorithms; Computer networks; Computer vision; Decision making; Fuzzy neural networks; Fuzzy set theory; Fuzzy sets; Image segmentation; Layout; Neural networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Information Processing Society Biannual Conference, 1994. Industrial Fuzzy Control and Intelligent Systems Conference, and the NASA Joint Technology Workshop on Neural Networks and Fuzzy Logic,

Conference_Location

San Antonio, TX

Print_ISBN

0-7803-2125-1

Type

conf

DOI

10.1109/IJCF.1994.375150

Filename

375150