Micro crack detection with Dijkstra’s shortest path algorithm

被引：0

作者：

Christina Gunkel

Alexander Stepper

Arne C. Müller

Christine H. Müller

机构：

[1] University of Kassel,Department of Mathematics

[2] Free University of Berlin,Department of Mathematics and Computer Science

[3] University of Technology Dortmund,Faculty of Statistics

来源：

Machine Vision and Applications | 2012年 / 23卷

关键词：

Image analysis; Crack detection; Crack cluster; Crack path; Dijkstra’s algorithm; Linear parts of a path;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

A package based on the free software R is presented which allows the automatic detection of micro cracks and corresponding statistical analysis of crack quantities. It uses a shortest path algorithm to detect micro cracks in situations where the cracks are surrounded by plastic deformations and where a discrimination between cracks and plastic deformations is difficult. In a first step, crack clusters are detected as connected components of pixels with values below a given threshold value. Then the crack paths are determined by Dijkstra’s algorithm as longest shortest paths through the darkest parts of the crack clusters. Linear parts of kinked paths can be identified with this. The new method was applied to over 2,000 images. Some statistical applications and a comparison with another free image tool are given.

引用

页码：589 / 601

页数：12

共 37 条

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