Fuzzy relations for feature-model correspondence in 3D object recognition

Author

Walker, Ellen L.

Author_Institution

Dept. of Comput. Sci., Rensselaer Polytech. Inst., Troy, NY, USA

fYear

1996

fDate

19-22 Jun 1996

Firstpage

28

Lastpage

32

Abstract

This paper presents a new mechanism for determining feature correspondences for object recognition, based on fuzzy set theory. The new method applies unary and binary constraints from the model, taking uncertainty characteristics of the measurement process into consideration. Experiments with both unoccluded and occluded images show that the method selects an appropriate set of correspondences, especially when integrated with fuzzy perceptual grouping

Keywords

feature extraction; fuzzy set theory; image matching; object recognition; uncertainty handling; 3D object recognition; binary constraints; feature-model correspondence; fuzzy perceptual grouping; fuzzy relations; fuzzy set theory; measurement process; occluded images; three dimensional object recognition; unary constraints; uncertainty; unoccluded images; Computer science; Feature extraction; Fuzzy reasoning; Fuzzy set theory; Fuzzy sets; Intelligent sensors; Intelligent systems; Object recognition; Sensor systems; Solid modeling;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Information Processing Society, 1996. NAFIPS., 1996 Biennial Conference of the North American

Conference_Location

Berkeley, CA

Print_ISBN

0-7803-3225-3

Type

conf

DOI

10.1109/NAFIPS.1996.534698

Filename

534698