An evaluation of global thresholding techniques for the automatic image segmentation of automotive aluminum sheet alloys

被引：25

作者：

Lievers, WB

Pilkey, AK ^{[1
]}

机构：

[1] Queens Univ, Dept Mech & Mat Engn, Kingston, ON K7L 3N6, Canada

[2] Ctr Automot Mat & Mfg, Kingston, ON K7L 5L9, Canada

来源：

MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING | 2004年 / 381卷 / 1-2期

基金：

加拿大自然科学与工程研究理事会;

关键词：

automotive aluminum; threshold; segmentation; image analysis;

D O I：

10.1016/j.msea.2004.04.002

中图分类号：

TB3 [工程材料学];

学科分类号：

0805 ; 080502 ;

摘要：

Digital image analysis is a valuable tool for the quantification of microstructural morphology, such as the size and clustering distributions of second-phase particles. However, the first step to such analysis is the segmentation of digital micrographs to identify the features of interest. Since the outcome of this step will have a significant influence on all subsequent processing, accurate segmentation is of the utmost importance. Grayscale images of the microstructures of three automotive aluminum alloys are used (AA5182, AA5754, and AA6111) to compare the abilities of several automatic global image thresholding techniques. The minimum cross-entropy method of Li and Lee [Pattern Recognit. 30 (1993) 617] was found to work well for all alloys when combined with the cost function of Yen et al. (C) 2004 Elsevier B.V. All rights reserved.

引用

页码：134 / 142

页数：9

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