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A New Distributed Architecture Based on Reinforcement Learning for Parameter Estimation in Image Processing

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International Conference on Advanced Intelligent Systems for Sustainable Development (AI2SD 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 637))

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Abstract

This paper comes to solve the problem of parameter tuning in image processing. This task is mostly done manually by users, but the multitude of possible values makes it tedious and time consuming. A distributed reinforcement learning using the Q-learning algorithm combined with Kalman filters is proposed to help users to find the optimal values of a combination of image processing operators. This combination is used to extract an object of interest from an image. The obtained results show how the proposed method behaves well to reach good results.

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Author information

Authors and Affiliations

  1. LISI Laboratory, Computer Science Department, Faculty of Science Semlalia, Cadi Ayyad University, Marrakech, Morocco

    Issam Qaffou

Authors
  1. Issam Qaffou
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Correspondence to Issam Qaffou .

Editor information

Editors and Affiliations

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

    Janusz Kacprzyk

  2. Abdelmalek Essaâdi University, Tangier, Morocco

    Mostafa Ezziyyani

  3. Department of Automatics and Applied Software, Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

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Qaffou, I. (2023). A New Distributed Architecture Based on Reinforcement Learning for Parameter Estimation in Image Processing. In: Kacprzyk, J., Ezziyyani, M., Balas, V.E. (eds) International Conference on Advanced Intelligent Systems for Sustainable Development. AI2SD 2022. Lecture Notes in Networks and Systems, vol 637. Springer, Cham. https://doi.org/10.1007/978-3-031-26384-2_82

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