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

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.

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

Journal of Electrical Engineering ◽

10.2478/jee-2018-0040 ◽

2018 ◽

Vol 69 (4) ◽

pp. 286-292 ◽

Cited By ~ 2

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.

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Options to Use Data Matrix Codes in Production Engineering

Management Systems in Production Engineering ◽

10.1515/mspe-2018-0038 ◽

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.

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Computationally efficient algorithm for multifocus image reconstruction

10.1117/12.476754 ◽

2003 ◽

Cited By ~ 48

Author(s):

Helmy A. Eltoukhy ◽

Sam Kavusi

Keyword(s):

Image Reconstruction ◽

Efficient Algorithm ◽

Computationally Efficient

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A Reliable and Computationally Efficient Algorithm for Imposing the Saddle Point Property in Dynamic Models

SSRN Electronic Journal ◽

10.2139/ssrn.1782059 ◽

2010 ◽

Cited By ~ 8

Author(s):

Gary Stanley Anderson

Keyword(s):

Saddle Point ◽

Efficient Algorithm ◽

Dynamic Models ◽

Point Property ◽

Computationally Efficient

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Efficient Algorithm For L(3,2,1)-Labeling of Cartesian Product Between Some Graphs

10.29007/x3qf ◽

2019 ◽

Author(s):

Sumonta Ghosh ◽

Prakhar Pogde ◽

Narayan C. Debnath ◽

Anita Pal

Keyword(s):

Communication Network ◽

Assignment Problem ◽

Efficient Algorithm ◽

Time Complexity ◽

Cartesian Product ◽

Frequency Assignment ◽

Frequency Assignment Problem ◽

The Difference

L(h,k) Labeling in graph came into existence as a solution to frequency assignment problem. To reduce interference a frequency in the form of non negative integers is assigned to each radio or TV transmitters located at various places. After L(h,k) labeling, L(h,k, j) labeling is introduced to reduce noise in the communication network. We investigated the graph obtained by Cartesian Product betweenCompleteBipartiteGraphwithPathandCycle,i. e.,Km,n×Pr andKm,n×Cr byapplying L(3,2,1)Labeling. The L(3,2,1) Labeling of a graph G is the difference between the highest and the lowest labels used in L(3,2,1) and is denoted by λ3,2,1(G) In this paper we have designed three suitable algorithms to label the graphs Km,n × Pr and Km,n × Cr . We have also analyzed the time complexity of each algorithm with illustration.

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