A fast Hough Transform algorithm for straight lines detection in an image using GPU parallel computing with CUDA-C
| Autor: | Joel Antonio Trejo-Sánchez, S. Gonzalez-Segura, Hugo Hidalgo-Silva, R. Yam-Uicab, J. L. López-Martínez |
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| Rok vydání: | 2017 |
| Předmět: |
020203 distributed computing
Computer science Graphics processing unit 02 engineering and technology Parallel computing Theoretical Computer Science Hough transform law.invention Reduction (complexity) CUDA Hardware and Architecture law Digital image processing 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing General-purpose computing on graphics processing units Algorithm Software Information Systems |
| Zdroj: | The Journal of Supercomputing. 73:4823-4842 |
| ISSN: | 1573-0484 0920-8542 |
| DOI: | 10.1007/s11227-017-2051-5 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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