<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Data matrix codes are two-dimensional (2D) matrix bar codes, which are the descendants of the well known 1D bar codes. However, compared to 1D bar codes, they allow to store much more information in the same area. Comparing data matrix codes with QR codes, for example, we find them much more effective in marking small objects or in the case that you have only a very small area for placing a code in. Their capacity and ability of decoding also a code that is partly damaged make them an appropriate solution for industrial applications. In the following paper we compare the impact of various cameras on the detection and decoding of data matrix codes in real scene images. The location of the code is based on the fact that typical bordering of a data matrix code forms a region of connected points which create “L”, the so-called finder pattern, and the parallel dotting, the so-called timing pattern. In the first step, we try to locate the finder pattern using adaptive thresholding and connecting neighbouring points to continuous regions. Then we search for the regions where 3 outer boundary points form a isosceles right triangle that could represent the finder pattern. In the second step, we have to verify the timing pattern. We look for an even number of crossings between the background and foreground. Experimental results show that the algorithm we have proposed provides better results than competitive solutions.</p></abstract>

Data matrix codes are two-dimensional (2D) matrix bar codes, which are the descendants of the well known 1D bar codes. However, compared to 1D bar codes, they allow to store much more information in the same area. Comparing data matrix codes with QR codes, for example, we find them much more effective in marking small objects or in the case that you have only a very small area for placing a code in. Their capacity and ability of decoding also a code that is partly damaged make them an appropriate solution for industrial applications. In the following paper we compare the impact of various cameras on the detection and decoding of data matrix codes in real scene images. The location of the code is based on the fact that typical bordering of a data matrix code forms a region of connected points which create “L”, the so-called finder pattern, and the parallel dotting, the so-called timing pattern. In the first step, we try to locate the finder pattern using adaptive thresholding and connecting neighbouring points to continuous regions. Then we search for the regions where 3 outer boundary points form a isosceles right triangle that could represent the finder pattern. In the second step, we have to verify the timing pattern. We look for an even number of crossings between the background and foreground. Experimental results show that the algorithm we have proposed provides better results than competitive solutions.

Comparing the impact of different cameras and image resolution to recognize the data matrix codes

Department of Manufacturing and Automation Technology, Faculty of Environmental and Manufacturing Technology

Department of Mechatronic Systems, Institute of Modern Technologies in Mechanical and Automotive Engineering and Metallurgy

Kalashnikov Izhevsk State Technical University

Journal of Electrical Engineering

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Data matrix codes are two-dimensional (2D) matrix bar codes, which are the descendants of the well known 1D bar codes. However, compared to 1D bar codes, they...

Comparing the impact of different cameras and image resolution to recognize the data matrix codes

Publicado en línea: 19 sept 2018

Páginas: 286 - 292

Recibido: 06 mar 2018

DOI: https://doi.org/10.2478/jee-2018-0040

Palabras clave
data matrix code location, adaptive thresholding, connected components labelling

© 2018 Ladislav Karrach et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Comparing the impact of different cameras and image resolution to recognize the data matrix codes

Publicado en línea: 19 sept 2018

Páginas: 286 - 292

Recibido: 06 mar 2018

DOI: https://doi.org/10.2478/jee-2018-0040

Palabras clavedata matrix code location, adaptive thresholding, connected components labelling

© 2018 Ladislav Karrach et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Palabras clave
data matrix code location, adaptive thresholding, connected components labelling