Top

Published in:

2020 | OriginalPaper | Chapter

Neural Network for Smart Adjustment of Industrial Camera - Study of Deployed Application

Authors : Petr Dolezel, Daniel Honc

Published in: AETA 2018 - Recent Advances in Electrical Engineering and Related Sciences: Theory and Application

Publisher: Springer International Publishing

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Since machine vision is gaining more and more interest lately, it is necessary to deal with correct approaches to visual data acquisition in industry. As a particular part of this complex problematics, a technique for the industrial camera exposure time and image sensor gain tuning is presented in this contribution. In comparison to other approaches, a human expert photographer is used instead of explicitly defined cost function. His knowledge is transformed into an artificial expert system represented by a feedforward neural network. The expert system then provides the suitable exposure time and image sensor gain to gather sharp and balanced images.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

previous chapter A New Approach Newton-Raphson Load Flow Analysis in Power System Networks with STATCOM

next chapter Risk Assessment Approach to Estimate Security of Cryptographic Keys in Quantum Cryptography

Data file with images (2018). https://www.researchgate.net/publication/326557728_Images_Basler

Chen, T., Wang, Y., Xiao, C., Wu, Q.M.J.: A machine vision apparatus and method for can-end inspection. IEEE Trans. Instrum. Measur. 65(9), 2055–2066 (2016)CrossRef

Hagan, M., Menhaj, M.: Training feedforward networks with the Marquardt algorithm. IEEE Trans. Neural Netw. 5(6), 989–993 (1994). https://doi.org/10.1109/72.329697CrossRef

Haykin, S.: Neural Networks: A Comprehensive Foundation. Prentice Hall, New Jersey (1999)MATH

Hornik, K., Stinchcombe, M., White, H.: Multilayer feedforward networks are universal approximators. Neural Netw. 2(5), 359–366 (1989)CrossRef

ITU-R Recommendation BT.601: Studio encoding parameters of digital television for standard 4:3 and wide screen 16:9 aspect ratios (2011)

Koik, B.T., Ibrahim, H.: A literature survey on blur detection algorithms for digital imaging. In: 2013 1st International Conference on Artificial Intelligence, Modelling and Simulation, pp. 272–277 (2013). 10.1109/AIMS.2013.50

Nandini, V., Vishal, R.D., Prakash, C.A., Aishwarya, S.: A review on applications of machine vision systems in industries. Indian J. Sci. Technol. 9(48), 1–5 (2016)CrossRef

Nguyen, D., Widrow, B.: Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights. pp. 21–26 (1990)

10.

Ning, J., Lu, T., Liu, L., Guo, L., Jin, X.: The optimization and design of the auto-exposure algorithm based on image entropy. In: 2015 8th International Congress on Image and Signal Processing (CISP), pp. 1020–1025 (2015). https://doi.org/10.1109/CISP.2015.7408029

11.

Park, S., Kim, G., Jeon, J.: The method of auto exposure control for low-end digital camera. In: 2009 11th International Conference on Advanced Communication Technology, vol. 03, pp. 1712–1714 (2009)

12.

Pourreza-Shahri, R., Kehtarnavaz, N.: Exposure bracketing via automatic exposure selection. In: 2015 IEEE International Conference on Image Processing (ICIP), pp. 320–323 (2015). https://doi.org/10.1109/ICIP.2015.7350812

13.

Shim, I., Lee, J.Y., Kweon, I.S.: Auto-adjusting camera exposure for outdoor robotics using gradient information. In: 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1011–1017 (2014). https://doi.org/10.1109/IROS.2014.6942682

14.

Wang, W.C., Chen, S.L., Chen, L.B., Chang, W.J.: A machine vision based automatic optical inspection system for measuring drilling quality of printed circuit boards. IEEE Access 5, 10817–10833 (2017)CrossRef

Title: Neural Network for Smart Adjustment of Industrial Camera - Study of Deployed Application
Authors: Petr Dolezel
Daniel Honc
Publisher: Springer International Publishing
Book: AETA 2018 - Recent Advances in Electrical Engineering and Related Sciences: Theory and Application
Print ISBN: 978-3-030-14906-2

Electronic ISBN: 978-3-030-14907-9

Copyright Year: 2020
DOI: https://doi.org/10.1007/978-3-030-14907-9_11