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Speeding up LIP-Canny with CUDA programming

Rafael Palomar, Jose M. Palomares, Joaquin Olivares, Jose M. Castillo, Juan Gomez-Luna
Department of Computer Architecture, Electronics and Electronic Technology, University of Cordoba
XXIII Jornadas de paralelismo, 2012

@article{palomar2012speeding,

   title={Speeding up LIP-Canny with CUDA programming},

   author={Palomar, R. and Palomares, J.M. and Olivares, J. and Castillo, J.M. and G{‘o}mez-Luna, J.},

   year={2012}

}

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The LIP-Canny algorithm outperforms traditional Canny edge detection in terms of edge detection under varying illumination. This method is based on a robust mathematical model (LIP paradigm), which is closer to the human vision system. However, this model requires more computations and more complex operations than the traditional paradigm. Non-parallel implementations of LIP-Canny do not fit Real-Time requirements because of the large amount of operations required. NVIDIA CUDA is a platform which enables the parallelization of this algorithm, obtaining very high performance. In this work, a comparison results between the non-parallel implementation (written in C/C++) and the NVIDIA CUDA one.
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