Abstract
In printing business, a lot of printing products have no apparent marks for registration, which cause the difficulty of printing image quality auto-detection. Aiming to this problem, a novel quality detection approach for non-mark printing image is proposed in this paper. The proposed approach mainly consists of the region feature based registration region selection and fast shape-based image matching method and an improved difference matching method to detect the printing defects. The proposed approach is realized by the well-known machine vision software HALCON. The experiment results show that the proposed approach can detect the printing defects efficiently with high accuracy, fast speed and strong robustness.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Shang, H.C.: The Study on Algorithm and System Implementation of Printing Image Online Detection. Huazhong University of Science and Technology (2008)
Li, C.P., Fan, Y.B., Hu, Q.C.: 3 recognition methods and analysis of PCB mark point based on HALCON. J. Foshan Univ. (Nat. Sci. Ed.) 28(2), 29–33 (2010)
Carsten, S., Markus, U., Christian, W.: Machine Vision Algorithms and Applications. Wiley-VCH, Weinheim (2008)
van Beusekom, J., Shafait, F., Breuel, T.M.: Image-matching for revision detection in printed historical documents. In: Hamprecht, F.A., Schnörr, C., Jähne, B. (eds.) Pattern Recognition, DAGM 2007. LNCS, vol. 4713, pp. 507–516. Springer, Heidelberg (2007)
Ding, J.H., Lu, Y., Huang, W., Qin, M.: A background subtraction method for defect detection of printed image. Appl. Mech. Mater. 462, 421–427 (2014)
Milan, S., Vaclav, H., Roger, B.: Image Processing, Analysis, and Machine Vision, 3rd edn. Tsinghua University Press, Beijing (2005)
Jin, C.: Research on Printing Image Quality Detection Technology Based on HALCON. Central South University (2013)
Acknowledgment
This work is supported by the National Natural Science Foundation of China (No. 61302049), Science and Technology planning Project of Guangdong Province (No. 2015B020233018, No. cgzhzd1105, No. 2012B050300024), Science and Technology Planning Project of Shantou and Open Fund of Guangdong Provincial Key Laboratory of Digital Signal and Image Processing Techniques.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Zhang, Q., Li, B., Shen, M., Shen, H. (2017). A Novel Quality Detection Approach for Non-mark Printing Image. In: Yuan, H., Geng, J., Bian, F. (eds) Geo-Spatial Knowledge and Intelligence. GRMSE 2016. Communications in Computer and Information Science, vol 699. Springer, Singapore. https://doi.org/10.1007/978-981-10-3969-0_20
Download citation
DOI: https://doi.org/10.1007/978-981-10-3969-0_20
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-3968-3
Online ISBN: 978-981-10-3969-0
eBook Packages: Computer ScienceComputer Science (R0)