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Training Subjective Perception Biased Images of Vehicle Ambient Lights with Deep Belief Networks Using Backpropagation- and Enforcing-Rules Supervised

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Artificial Intelligence and Soft Computing (ICAISC 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13588))

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Abstract

Quality measurement of vehicle ambient lighting during series production can be influenced by subjective perceptions of light homogeneity. In consequence, the labels correspond to the decisions whether the lights appear homogeneous or not. In this article we demonstrate how images of ambient lighting were trained by Deep Belief Networks using the learning rules “backpropagation” (BP) and “enforcing-rule supervised” (ERS). In addition, the effect of the contrastive divergence pre-training is analyzed on the accuracy of the trained networks. The results are promising for decision support in the production process to minimize the influence of subjectivity by human evaluators.

Supported by MENTOR GmbH & Co. Präzisions-Bauteile KG.

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Author information

Authors and Affiliations

  1. University of Applied Sciences, Düsseldorf, Germany

    Gregor Braun & Michel Brokamp

  2. University of Duisburg-Essen, Essen, Germany

    Christina Klüver

Authors
  1. Gregor Braun
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  2. Michel Brokamp
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  3. Christina Klüver
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Corresponding author

Correspondence to Christina Klüver .

Editor information

Editors and Affiliations

  1. Systems Research Institute of the Polish Academy of Sciences, Warsaw, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

  3. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  4. University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

  5. AGH University of Science and Technology, Kraków, Poland

    Ryszard Tadeusiewicz

  6. University of Louisville, Louisville, KY, USA

    Jacek M. Zurada

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Braun, G., Brokamp, M., Klüver, C. (2023). Training Subjective Perception Biased Images of Vehicle Ambient Lights with Deep Belief Networks Using Backpropagation- and Enforcing-Rules Supervised. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2022. Lecture Notes in Computer Science(), vol 13588. Springer, Cham. https://doi.org/10.1007/978-3-031-23492-7_3

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  • DOI: https://doi.org/10.1007/978-3-031-23492-7_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-23491-0

  • Online ISBN: 978-3-031-23492-7

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