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A fast Hough Transform algorithm for straight lines detection in an image using GPU parallel computing with CUDA-C

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Abstract

The Hough Transform (HT) is a digital image processing method for the detection of shapes which has multiple uses today. A disadvantage of this method is its sequential computational complexity, particularly when a single processor is used. An optimized algorithm of HT for straight lines detection in an image is presented in this article. Optimization is realized by using a decomposition of the input image recently proposed via central processing unit (CPU), and the technique known as segment decomposition. Optimized algorithms improve execution times significantly. In this paper, the optimization is implemented in parallel using graphics processing unit (GPU) programming, allowing a reduction of total run time and achieving a performance more than 20 times better than the sequential method and up to 10 times better than the implementation recently proposed. Additionally, we introduce the concept of Performance Ratio, to emphasize the outperforming of the GPU over the CPUs.

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Author information

Authors and Affiliations

  1. Facultad de Matematicas, Universidad Autonoma de Yucatan, Mérida, Mexico

    R. Yam-Uicab, J. L. Lopez-Martinez & S. Gonzalez-Segura

  2. Conacyt-Centro de Investigacion en Matematicas, Mérida, Mexico

    J. A. Trejo-Sanchez

  3. Basic Sciences Department, Universidad del Caribe, Cancún, Mexico

    J. A. Trejo-Sanchez

  4. Centro de Investigacion Cientifica y Educacion Superior de Ensenada, Ensenada, Mexico

    H. Hidalgo-Silva

Authors
  1. R. Yam-Uicab
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  2. J. L. Lopez-Martinez
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  3. J. A. Trejo-Sanchez
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  4. H. Hidalgo-Silva
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  5. S. Gonzalez-Segura
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Corresponding author

Correspondence to J. L. Lopez-Martinez.

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Yam-Uicab, R., Lopez-Martinez, J.L., Trejo-Sanchez, J.A. et al. A fast Hough Transform algorithm for straight lines detection in an image using GPU parallel computing with CUDA-C. J Supercomput 73, 4823–4842 (2017). https://doi.org/10.1007/s11227-017-2051-5

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  • DOI: https://doi.org/10.1007/s11227-017-2051-5

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