Texture Image Analysis of Metallography: Automatic Estimating Grade of Spherular Pearlite Using Dempster-Shafer Theory

Author

Tian, Pei ; Zhang, Qiang ; Zhang, Shu-yong

Author_Institution

Sch. of Control Sci. & Eng., North China Electr. Power Univ., Baoding

fYear

2006

fDate

13-16 Aug. 2006

Firstpage

1985

Lastpage

1990

Abstract

An algorithm based on Dempster-Shafer evidence theory with discernment frame segmentation is proposed for texture image analysis, which applies to automatic gradation of spherular pearlite about 15CrMo. Image enhancement, segmentation and feature extraction is implemented first to form the feature space, which includes the fractal dimension, energy and entropy. The feature information is fused using the proposed algorithm. The experiment demonstrates that the algorithm applied to the case with both high accuracy and efficiency

Keywords

feature extraction; image enhancement; image segmentation; image texture; mathematical morphology; metallography; steel; uncertainty handling; Dempster-Shafer evidence theory; automatic estimation grade; discernment frame segmentation; feature extraction; image enhancement; metallography; spherular pearlite; texture image analysis; Automatic control; Bayesian methods; Cybernetics; Entropy; Estimation theory; Feature extraction; Fractals; Image enhancement; Image segmentation; Image texture analysis; Machine learning; Power engineering and energy; Power generation; Steel; Dempster-Shafer; feature extraction; information fusion; metallography; pearlite;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2006 International Conference on

Conference_Location

Dalian, China

Print_ISBN

1-4244-0061-9

Type

conf

DOI

10.1109/ICMLC.2006.259129

Filename

4028390