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Complex Object Recognition Based on Multi-shape Invariant Radon Transform

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Intelligent Decision Technologies 2017 (IDT 2017)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 73))

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Abstract

Based on the properties of Template Matching and Radon Transform, a new Multi-Shape Invariant Radon Transform (MSIRT) is proposed in this paper. Unlike Radon Transform, integrating projections across lines, the MSIRT uses arbitrary given curves, which are derived from primitives contours. MSIRT leads to peaks once similar shapes are met in the projected image. For seek of genericity and invariance with respect to geometric transformations, we consider different primitives derived from MPEG7 dataset. Each object undergoes a series of preprocessing steps, segmentation and contour extraction, for generating the corresponding primitive. For each query object, the MSIRT is applied with respect to the different primitives and a vote approach will be used for object recognition. Validation of the proposed approach is done on the MPEG7 dataset, giving an accuracy of 94%. Comparison with some known approaches demonstrates the effectiveness of the proposed approach in detecting complex objects, even under geometric transformations.

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Author information

Authors and Affiliations

  1. National Engineering School of Sfax, University of Sfax, Sfax, Tunisia

    Ghassen Hammouda & Dorra Sellami

  2. The Sciences Institute of Tunis, University of Tunis, Tunis, Tunisia

    Atef Hammouda

Authors
  1. Ghassen Hammouda
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  2. Atef Hammouda
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  3. Dorra Sellami
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Corresponding author

Correspondence to Ghassen Hammouda .

Editor information

Editors and Affiliations

  1. Maritime University , Gdynia, Poland

    Ireneusz Czarnowski

  2. Bournemouth University and KES International, Poole, Dorset, United Kingdom

    Robert J. Howlett

  3. University of Canberra, Canberra, Aust Capital Terr, Australia

    Lakhmi C. Jain

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Hammouda, G., Hammouda, A., Sellami, D. (2018). Complex Object Recognition Based on Multi-shape Invariant Radon Transform. In: Czarnowski, I., Howlett, R., Jain, L. (eds) Intelligent Decision Technologies 2017. IDT 2017. Smart Innovation, Systems and Technologies, vol 73. Springer, Cham. https://doi.org/10.1007/978-3-319-59424-8_2

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  • DOI: https://doi.org/10.1007/978-3-319-59424-8_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59423-1

  • Online ISBN: 978-3-319-59424-8

  • eBook Packages: EngineeringEngineering (R0)

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