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

Konstantin Trambitckii, Katharina Anding, Lilli Haar, Gunther Notni

2019

Abstract

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.

Paper Citation

in Harvard Style

Trambitckii K., Anding K., Haar L. and Notni G. (2019). Estimation of Correlation between Texture Features and Surface Parameters for Milled Metal Parts.In Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-351-3, pages 421-428. DOI: 10.5220/0007344104210428

in Bibtex Style

@conference{icpram19,
author={Konstantin Trambitckii and Katharina Anding and Lilli Haar and Gunther Notni},
title={Estimation of Correlation between Texture Features and Surface Parameters for Milled Metal Parts},
booktitle={Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2019},
pages={421-428},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007344104210428},
isbn={978-989-758-351-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Estimation of Correlation between Texture Features and Surface Parameters for Milled Metal Parts
SN - 978-989-758-351-3
AU - Trambitckii K.
AU - Anding K.
AU - Haar L.
AU - Notni G.
PY - 2019
SP - 421
EP - 428
DO - 10.5220/0007344104210428