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Motion estimation using learning automata

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Abstract

Block-matching algorithms (BMAs) are widely employed for motion estimation. BMAs divide input frames into several blocks and minimize an error function for each block to calculate motion vectors. Afterward, each motion vector is applicable for all of the pixels within the block. Since computing the error functions is resource intensive, many fast-search motion estimation algorithms have been suggested to reduce the computational cost. These fast algorithms provide a significant reduction in computation but often converge to a local minimum. A learning automaton is an adaptive decision-making unit that learns the optimal action through repeated interactions with its environment. Learning automata (LA) have been applied successfully to a wide range of applications including pattern recognition, dynamic channel assignment, and social network analysis. In this paper, we apply LA to motion estimation problem, which is one of the basic problems in computer vision. We compare the accuracy and performance of the suggested algorithms with other well-known BMAs. Interestingly, the obtained results indicate high efficiency and accuracy of the proposed methods. The results suggest that simplicity, efficiency, parallel nature, and accuracy of LA-based methods make them a good candidate to solve computer vision problems.

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Authors and Affiliations

  1. Department of Computer, Amirkabir University of Technology, Tehran, Iran

    Bahman Damerchilu & Mohammad Reza Meybodi

  2. Department of Computer Science, University of Wyoming, Laramie, WY, USA

    Mohammad Sadegh Norouzzadeh

Authors
  1. Bahman Damerchilu
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  2. Mohammad Sadegh Norouzzadeh
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  3. Mohammad Reza Meybodi
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Correspondence to Mohammad Sadegh Norouzzadeh.

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Damerchilu, B., Norouzzadeh, M.S. & Meybodi, M.R. Motion estimation using learning automata. Machine Vision and Applications 27, 1047–1061 (2016). https://doi.org/10.1007/s00138-016-0788-0

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  • DOI: https://doi.org/10.1007/s00138-016-0788-0

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