Identification of instruments and implants with RFID and Data Matrix Codes for the use at the instrument table

2016 ◽  
Vol 1 (11) ◽  
pp. 227-230
Author(s):  
LUKAS B�HLER
Keyword(s):  
Data Matrix ◽  
Matrix Codes

scholarly journals Makelabels: a Bash script for generating data matrix codes for collection management

2016 ◽  
Vol 4 ◽  
pp. e9583 ◽  
Author(s):  
Theresa Dellinger ◽  
Victoria Wong ◽  
Paul Marek
Keyword(s):  
Data Matrix ◽  
Collection Management ◽  
Matrix Codes

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

2018 ◽  
Vol 69 (4) ◽  
pp. 286-292 ◽  
Author(s):  
Ladislav Karrach ◽  
Elena Pivarčiová ◽  
Yury Rafailovich Nikitin
Keyword(s):  
Outer Boundary ◽  
Industrial Applications ◽  
Image Resolution ◽  
Data Matrix ◽  
Second Step ◽  
Bar Codes ◽  
Real Scene ◽  
Timing Pattern ◽  
Matrix Codes ◽  
The Impact

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.

scholarly journals Recognition of Data Matrix Codes in Images and their Applications in Production Processes

2020 ◽  
Vol 28 (3) ◽  
pp. 154-161
Author(s):  
Ladislav Karrach ◽  
Elena Pivarčiová
Keyword(s):  
Open Source ◽  
Efficient Algorithm ◽  
Data Matrix ◽  
Connected Components ◽  
Computationally Efficient ◽  
Production Processes ◽  
Real Scene ◽  
Timing Pattern ◽  
The Difference ◽  
Matrix Codes

AbstractData Matrix codes can be a significant factor in increasing productivity and efficiency in production processes. An important point in deploying Data Matrix codes is their recognition and decoding. In this paper is presented a computationally efficient algorithm for locating Data Matrix codes in the images. Image areas that may contain the Data Matrix code are to be identified firstly. To identify these areas, the thresholding, connected components labelling and examining outer bounding-box of the continuous regions is used. Subsequently, to determine the boundaries of the Data Matrix code more precisely, we work with the difference of adjacent projections around the Finder Pattern. The dimensions of the Data Matrix code are determined by analyzing the local extremes around the Timing Pattern. We verified the proposed method on a testing set of synthetic and real scene images and compared it with the results of other open-source and commercial solutions. The proposed method has achieved better results than competitive commercial solutions.

scholarly journals Options to Use Data Matrix Codes in Production Engineering

2018 ◽  
Vol 26 (4) ◽  
pp. 231-236
Author(s):  
Ladislav Karrach ◽  
Elena Pivarčiová
Keyword(s):  
Data Matrix ◽  
Computationally Efficient ◽  
Production Engineering ◽  
Perpendicular Line ◽  
Real Time Processing ◽  
Black And White ◽  
Shift Rotation ◽  
Matrix Codes ◽  
Using Data ◽  
Candidate Regions

Abstract The paper deals with the possibilities of using Data Matrix codes in production engineering. We designed and tested the computationally efficient method for locating the Data Matrix code in the images. The location search procedure consists of identification of candidate regions using image binarization, then joining adjacent points into continuous regions and also examining outer boundaries of the regions. Afterwards we verify the presence of the Finder Pattern (as two perpendicular line segments) and Timing Pattern (as alternating sequence of black and white modules) in these candidate regions. Such procedure is invariant to shift rotation and scale change of Data Matrix codes. The method we have proposed has been verified on a set of real industrial images and compared to other commercial algorithms. We are also convinced that such technique is also suitable for real-time processing and has achieved better results than comparable commercial algorithms.

scholarly journals Comparative Study of Data Matrix Codes Localization and Recognition Methods

2021 ◽  
Vol 7 (9) ◽  
pp. 163
Author(s):  
Ladislav Karrach ◽  
Elena Pivarčiová
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Recognition Rate ◽  
Data Matrix ◽  
Adaptive Thresholding ◽  
Connected Component ◽  
Image Processing Techniques ◽  
Connected Component Labeling ◽  
Matrix Codes ◽  
Processing Techniques

We provide a comprehensive and in-depth overview of the various approaches applicable to the recognition of Data Matrix codes in arbitrary images. All presented methods use the typical “L” shaped Finder Pattern to locate the Data Matrix code in the image. Well-known image processing techniques such as edge detection, adaptive thresholding, or connected component labeling are used to identify the Finder Pattern. The recognition rate of the compared methods was tested on a set of images with Data Matrix codes, which is published together with the article. The experimental results show that methods based on adaptive thresholding achieved a better recognition rate than methods based on edge detection.

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