Estimation of Correlation between Texture Features and Surface Parameters for Milled Metal Parts

被引：0

作者：

Trambitckii, Konstantin ^{[1
]}

Anding, Katharina ^{[1
]}

Haar, Lilli ^{[1
]}

Notni, Gunther ^{[1
]}

机构：

[1] Ilmenau Univ Technol, Inst Mech Engn, Dept Qual Assurance & Ind Image Proc, Gustav Kirchhoff Pl 2, Ilmenau, Germany

来源：

ICPRAM: PROCEEDINGS OF THE 8TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION APPLICATIONS AND METHODS | 2019年

关键词：

Quality Assurance; Image Processing; Texture Features; Roughness Parameters; Metal Parts; PREDICTION; FOCUS;

D O I：

10.5220/0007344104210428

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Fast developing of computer technologies led to vast improvements of image processing systems and algorithms. Nowadays these algorithms are widely used in different areas of computer and machine vision systems. In this research texture features were used to analyse metal surfaces using a set of images obtained with industrial camera with macro lens. This kind of contactless surface roughness estimation is cheaper and quicker in comparison with traditional methods. A set of 27 texture features were calculated for a set of surface images. Correlation coefficients between the texture features and 10 roughness parameters for the sample surfaces were estimated. Obtained results showed that texture features can be successfully used for quick surface quality estimation.

引用

页码：421 / 428

页数：8

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