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Clustering Method for Isolate Dynamic Points in Image Sequences

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Advances in Computer Vision and Computational Biology

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Abstract

In this chapter, we propose an optimization of the a-contrario clustering method using the probabilistic Guillaume Khenchaff Measure (MGK) quality technique. A-contrario is used for tracking salient objects in the scene in real time. This method analyzes the data contained in a motion vector, which contains the scattered optical flow accumulated points of interest. The aim of our study is to improve the first results obtained from the Number of False Alarm (NFA) criterion by using MGK to bring together the group of points endowed with a coherent movement of the binary tree. The idea is to isolate dynamic points so that we can use static points in the future.

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Author information

Authors and Affiliations

  1. Department of Mathematics, Computer Science and Applications, University of Toamasina, Toamasina, Madagascar

    Paula Niels Spinoza & Andriamasinoro Rahajaniaina

  2. IRIT, REVA, Paul Sabatier University, Toulouse, France

    Jean-Pierre Jessel

Authors
  1. Paula Niels Spinoza
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  2. Andriamasinoro Rahajaniaina
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  3. Jean-Pierre Jessel
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Georgia, Athens, GA, USA

    Hamid R. Arabnia

  2. School of Computing and Data Sciences, Wentworth Institute of Technology, Boston, MA, USA

    Leonidas Deligiannidis

  3. Graduate School of Information Science & Engineering, University of Electro-Communications, Chofu, Japan

    Hayaru Shouno

  4. Facultad de Informática - CIC PBA, Universidad Nacional de La Plata, La Plata, Argentina

    Fernando G. Tinetti

  5. Department of Computer Science, Southeastern Louisiana University, Hammond, LA, USA

    Quoc-Nam Tran

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Spinoza, P.N., Rahajaniaina, A., Jessel, JP. (2021). Clustering Method for Isolate Dynamic Points in Image Sequences. In: Arabnia, H.R., Deligiannidis, L., Shouno, H., Tinetti, F.G., Tran, QN. (eds) Advances in Computer Vision and Computational Biology. Transactions on Computational Science and Computational Intelligence. Springer, Cham. https://doi.org/10.1007/978-3-030-71051-4_19

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  • DOI: https://doi.org/10.1007/978-3-030-71051-4_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-71050-7

  • Online ISBN: 978-3-030-71051-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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