TOPOLOGICAL IMAGE PROCESSING FOR COMPREHENSIVE DEFECT AND DEVIATION ANALYSIS USING ADAPTIVE BINARISATION


Abstract views: 46 / PDF downloads: 26

Authors

  • Igor NEVLIUDOV Department of Сomputer-Integrated Technologies, Automation and Mechatronics; Kharkiv National University of Radio Electronics, Kharkiv, Ukraine
  • Igor BADANYUK Department of Сomputer-Integrated Technologies, Automation and Mechatronics; Kharkiv National University of Radio Electronics, Kharkiv, Ukraine
  • Dmytro NIKITIN Department of Сomputer-Integrated Technologies, Automation and Mechatronics; Kharkiv National University of Radio Electronics, Kharkiv, Ukraine

DOI:

https://doi.org/10.5281/zenodo.8098602

Keywords:

Process image processing, Adaptive binarization, Otsu method, GP topology, Finding

Abstract

PCB topology image processing is an important component of Industry 4.0, as images can be used for automated quality control and visual inspection of manufacturing processes related to PCB production. Image processing can be used to control the quality of printed circuit boards, for example, to detect defects that may be invisible to the human eye.

The main objective of the study is to improve the method of adaptive binarization for images obtained by technical vision systems by developing an automatic algorithm for detecting the required value of the image binarization window. To achieve this goal, it was decided to develop an algorithm for automatically finding the size of the scanning area in adaptive binarization for processing technological images of the SOE topology.

References

Arianna Martinelli, Andrea Mina, Massimo Moggi. (2021). The enabling technologies of industry 4.0: examining the seeds of the fourth industrial revolution. Industrial and Corporate Change, Volume 30, Issue 1, P. 161–188. DOI: https://doi.org/10.1093/icc/dtaa060

Núbia Carvalho, Omar Chaim, Edson Cazarini, Mateus Gerolamo. (2018). Manufacturing in the fourth industrial revolution: A positive prospect in Sustainable Manufacturing, Procedia Manufacturing, Volume 21, P. 671–678. DOI: https://doi.org/10.1016/j.promfg.2018.02.170

Mohammad Fakhar Manesh; Massimiliano Matteo Pellegrini; Giacomo Marzi; Marina Dabic. (2020). Knowledge Management in the Fourth Industrial Revolution: Mapping the Literature and Scoping Future Avenues, IEEE Transactions on Engineering Management, Volume: 68, Issue: 1, P. 289–300. DOI: 10.1109/TEM.2019.2963489

Andronie, Mihai, George Lăzăroiu, Mariana Iatagan, Iulian Hurloiu, and Irina Dijmărescu. (2021). "Sustainable Cyber-Physical Production Systems in Big Data-Driven Smart Urban Economy: A Systematic Literature Review" Sustainability 13, no. 2: 751 р. DOI: https://doi.org/10.3390/su13020751

Nevliudov, I., & et al. (2021). Development of a cyber design modeling declarative Language for cyber-physical production systems, J. Math. Comput. Sci., 11(1), Р. 520–542.

Theo Lins, Ricardo Augusto Rabelo Oliveira. (2020). Cyber-physical production systems retrofitting in the context of industry 4.0. Computers & Industrial Engineering. Volume 139. DOI: https://doi.org/10.1016/j.cie.2019.106193

Igor Gruzman. (2013). Threshold binarization of images based on the skewness and kurtosis of truncated distributions. Optoelectronics Instrumentation and Data Processing 49(3). Р. 215–220. DOI: 10.3103/S8756699013030011

B. Gatos, K. Ntirogiannis, and I. Pratikakis. ICDAR 2009 document image binarization contest (DIBCO 2009). ICDAR, 2009. Р. 1375–1382. DOI:10.1109/ICDAR.2009.246

N. Stamatopoulos, B. Gatos, G. Louloudis, U. Pal, and A. Alaei. ICDAR 2013 Handwriting Segmentation Contest. 12th International Conference on Document Analysis and Recognition (ICDAR). 2013. Р. 1402-1406. URL: https://www.academia.edu/19693205/ICDAR_2013_Handwriting_Segmentation_Contest

Downloads

Published

2023-06-30

How to Cite

NEVLIUDOV, I., BADANYUK, I., & NIKITIN, D. (2023). TOPOLOGICAL IMAGE PROCESSING FOR COMPREHENSIVE DEFECT AND DEVIATION ANALYSIS USING ADAPTIVE BINARISATION. Journal of Natural Sciences and Technologies, 2(1), 183–186. https://doi.org/10.5281/zenodo.8098602

Issue

Section

Articles