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Discretisation of the Hough parameter space for fitting and recognising geometric primitives in 3D point clouds

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  • Romanengo, Chiara
  • Falcidieno, Bianca
  • Biasotti, Silvia

Abstract

Research in recognising and fitting simple geometric shapes has been ongoing since the 1970s, with various approaches proposed, including stochastic methods, parameter methods, primitive-based registration techniques, and more recently, deep learning. The Hough transform is a method of interest due to its demonstrated robustness to noise and outliers, ability to handle missing data, and support for multiple model instances. Unfortunately, one of the main limitations of the Hough transform is how to properly discretise its parameter space, as increasing their number or decreasing the sampling frequency can make it computationally expensive.

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  • Romanengo, Chiara & Falcidieno, Bianca & Biasotti, Silvia, 2025. "Discretisation of the Hough parameter space for fitting and recognising geometric primitives in 3D point clouds," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 228(C), pages 73-86.
    • Handle: RePEc:eee:matcom:v:228:y:2025:i:c:p:73-86
    DOI: 10.1016/j.matcom.2024.08.033
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    References listed on IDEAS

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    1. Boudraa, Omar & Hidouci, Walid Khaled & Michelucci, Dominique, 2020. "Using skeleton and Hough transform variant to correct skew in historical documents," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 167(C), pages 389-403.
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