Abstract
The Canny algorithm is a well known edge detector that is widely used in the previous processing stages in several algorithms related to computer vision. An alternative, the LIP-Canny algorithm, is based on a robust mathematical model closer to the human vision system, obtaining better results in terms of edge detection. In this work we describe LIP-Canny algorithm under the perspective from its parallelization and optimization by using the NVIDIA CUDA framework. Furthermore, we present comparative results between an implementation of this algorithm using NVIDIA CUDA and the analogue using a C/C++ approach.
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References
Canny, J.: A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 8(6), 679–698 (1986)
Sobel, I., Feldman, G.: A 3x3 isotropic gradient operator for image processing. Talk at the Stanford Artificial Project (1968)
Sobel, I.: Camera Models and Machine Perception. Ph.d. thesis, Standford University (1970)
Palomares, J.M., González, J., Ros, E.: Detección de bordes en imágenes con sombras mediante LIP–Canny. In: AERFAI 2005 (2005)
Riegel, E., Indinger, T., Adams, N.: Implementation of a Lattice-Boltzmann method for numerical fluid mechanics using the nVIDIA CUDA technology (2009)
Kavanagh, G., Lewis, M., Massingill, B.: GPGPU planetary simulations with CUDA. In: Proceedings of the 2008 International Conference on Scientific Computing, CSC 2008, pp. 180–185 (2008)
Luo, Y., Duraiswami, R.: Canny edge detection on NVIDIA CUDA. In: 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops (2008)
Gómez-Luna, J., González-Linares, J.M., Benavides, J.I., Guil, N.: Parallelization of a video segmentation algorithm on CUDA–enabled graphics processing units. In: Sips, H., Epema, D., Lin, H.-X. (eds.) Euro-Par 2009. LNCS, vol. 5704, pp. 924–935. Springer, Heidelberg (2009)
Jourlin, P.: A model for logarithmic image processing. Journal of Microscopy 149, 21–35 (1988)
Pinoli, J.: The logarithmic image processing model: Connections with human brightness perception and contrast estimators. Journal of Mathematical Image Processing 7, 341–358 (1997)
NVIDIA: NVIDIA CUDA Programming Guide (2009)
NVIDIA: NVIDIA CUDA C Programming Best Practices Guide. CUDA Toolkit 2.3. (2009)
Podlozhnyuk, V.: Image Convolution with CUDA
Palomares, J.M., González, J., Ros, E., Prieto, A.: General logarithmic image processing convolution. IEEE Transactions on Image Processing 15(11), 3602–3608 (2006)
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Palomar, R., Palomares, J.M., Castillo, J.M., Olivares, J., Gómez-Luna, J. (2010). Parallelizing and Optimizing LIP-Canny Using NVIDIA CUDA. In: García-Pedrajas, N., Herrera, F., Fyfe, C., Benítez, J.M., Ali, M. (eds) Trends in Applied Intelligent Systems. IEA/AIE 2010. Lecture Notes in Computer Science(), vol 6098. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13033-5_40
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DOI: https://doi.org/10.1007/978-3-642-13033-5_40
Publisher Name: Springer, Berlin, Heidelberg
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