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Shallow Convolutional Neural Network and Viterbi Algorithm for Dim Line Tracking

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Book cover Computer Vision and Graphics (ICCVG 2018)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11114))

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Abstract

The estimation of line is important in numerous practical applications. The most difficult case if the line is dim, even hidden in background noise. The application of Track–Before–Detect algorithms allows the tracking of such line. Additional preprocessing using shallow neural network trained for the detection of line features is proposed in this paper. Four variant of data fusion from neural network are compared. Direct output of neural network that works as a classifier gives best results for Mean Absolute Error (MAE) metric. Similar results are obtained if output of neural network is used as a mask for input image. Monte Carlo test are used for unbiased results. Test shows improvement of MAE about two times. The application of binary output from neural network is wrong solution and the error is largest. The influence of the number of convolutional layer neurons is not significant in this test.

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Acknowledgment

This work is supported by the UE EFRR ZPORR project Z/2.32/I/1.3.1/267/05 “Szczecin University of Technology – Research and Education Center of Modern Multimedia Technologies” (Poland).

We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan X GPU used for this research.

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Authors and Affiliations

  1. West Pomeranian University of Technology Szczecin, 26. Kwietnia 10 St., 71126, Szczecin, Poland

    Przemyslaw Mazurek

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  1. Przemyslaw Mazurek
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Correspondence to Przemyslaw Mazurek .

Editor information

Editors and Affiliations

  1. Faculty of Applied Informatics and Mathematics, Warsaw University of Life Sciences, Warsaw, Poland

    Leszek J. Chmielewski

  2. Faculty of Applied Informatics and Mathematics, Warsaw University of Life Sciences, Warsaw, Poland

    Ryszard Kozera

  3. Faculty of Applied Informatics and Mathematics, Warsaw University of Life Sciences, Warsaw, Poland

    Arkadiusz Orłowski

  4. Institute of Computer Science, Silesian University of Technology, Gliwice, Poland

    Konrad Wojciechowski

  5. Technion, Israel Institute of Technology, Haifa, Israel

    Alfred M. Bruckstein

  6. University of Groningen, Groningen, The Netherlands

    Nicolai Petkov

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Mazurek, P. (2018). Shallow Convolutional Neural Network and Viterbi Algorithm for Dim Line Tracking. In: Chmielewski, L., Kozera, R., Orłowski, A., Wojciechowski, K., Bruckstein, A., Petkov, N. (eds) Computer Vision and Graphics. ICCVG 2018. Lecture Notes in Computer Science(), vol 11114. Springer, Cham. https://doi.org/10.1007/978-3-030-00692-1_33

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  • DOI: https://doi.org/10.1007/978-3-030-00692-1_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00691-4

  • Online ISBN: 978-3-030-00692-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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