Utilización de GPU-CUDA en el Procesamiento Digital de Imágenes

Authors

DOI:

https://doi.org/10.20983/culcyt.2018.3.9

Keywords:

CPU, GPU, CUDA, Procesamiento Digital de Imágenes, Nvidia

Abstract

EL procesamiento de imágenes es una herramienta de gran utilidad en diversas aplicaciones como video vigilancia, reconstrucción de imágenes, información geográfica y médica. Sin embargo, estas aplicaciones requieren una gran demanda computacional para ser llevadas a cabo en el menor tiempo posible, aún y que se desarrollan nuevos algoritmos, suelen ser restrictivos para implementarse en tiempo real en sistemas que solo se basan en CPU. Afortunadamente, estos algoritmos pueden ser analizados para llevarse a cabo en plataformas de cómputo paralelo, como las GPU-CUDA. En este trabajo se analizan diferentes revistas de la IEEE desde el 2013, donde se paralelizaron algoritmos con estas aplicaciones y se implementaron con ayuda de GPU´s. Se destaca que el uso de esta herramienta ha ido en crecimiento en el ámbito científico, en diferentes ramas de la ciencia.

Downloads

Download data is not yet available.

References

Abbate, S. et al. 2016 ‘Extended Chirp Scaling on GPGPU’, IEEE Latin America Transactions, 14(6), pp. 2638–2643. doi: 10.1109/TLA.2016.7555231.

Alexiadis, D. S., Zarpalas, D. and Daras, P. 2013 ‘Real-Time , Full 3-D Reconstruction of Consumer Depth Cameras’, IEEE Transactions on Multimedia, 15(2), pp. 339–358.

Bahri, H. et al. 2017 ‘Image feature extraction algorithm based on CUDA architecture: case study GFD and GCFD’, IET Computers & Digital Techniques, 11(4), pp. 125–132. doi: 10.1049/iet-cdt.2016.0135.

Balsa Rodríguez, M. et al. 2014 ‘State-of-the-art in compressed GPU-based direct volume rendering’, Computer Graphics Forum, 33(6), pp. 77–100. doi: 10.1111/cgf.12280.

Barberis, A. et al. 2013 ‘Real-time implementation of the vertex component analysis algorithm on GPUs’, IEEE Geoscience and Remote Sensing Letters, 10(2), pp. 251–255. doi: 10.1109/LGRS.2012.2200452.

Bernabe, S. et al. 2013 ‘Hyperspectral unmixing on GPUs and multi-core processors: A comparison’, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6(3), pp. 1386–1398. doi: 10.1109/JSTARS.2013.2254470.

Bernabe, S. et al. 2016 ‘Parallel Hyperspectral Coded Aperture for Compressive Sensing on GPUs’, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(2), pp. 932–944. doi: 10.1109/JSTARS.2015.2436440.

Bista, S. et al. 2014 ‘Visualization of brain microstructure through spherical harmonics illumination of high fidelity spatio-angular fields’, IEEE Transactions on Visualization and Computer Graphics, 20(12), pp. 2516–2525. doi: 10.1109/TVCG.2014.2346411.

Chen, Z., Chen, Y. and Huang, Q. 2016 ‘Development of a Wireless and Near Real-Time 3D Ultrasound Strain Imaging System’, IEEE Transactions on Biomedical Circuits and Systems, 10(2), pp. 394–403. doi: 10.1109/TBCAS.2015.2420117.

Cui, J. et al. 2013 ‘Distributed MLEM: an iterative tomographic image reconstruction algorithm for distributed memory architectures.’, IEEE Trans Med Imaging, 32(5), pp. 957–967. doi: 10.1109/TMI.2013.2252913.

Deligiannidis, L. and Arabnia, H. R. 2014 ‘Parallel Video Processing Techniques for Surveillance Applications’, 2014 International Conference on Computational Science and Computational Intelligence, pp. 183–189. doi: 10.1109/CSCI.2014.38.

Després, P. and Jia, X. 2017 ‘A review of GPU-based medical image reconstruction’, Physica Medica, 42, pp. 76–92. doi: 10.1016/j.ejmp.2017.07.024.

Devani, U., Nikam, V. B. and Meshram, B. B. 2015 ‘Super-fast parallel eigenface implementation on GPU for face recognition’, Proceedings of 2014 3rd International Conference on Parallel, Distributed and Grid Computing, PDGC 2014, pp. 130–136. doi: 10.1109/PDGC.2014.7030729.

Duchateau, J. et al. 2017 ‘An Out-of-Core Method for Physical Simulations on a Multi-GPU Architecture Using Lattice Boltzmann Method’, Proceedings - 13th IEEE International Conference on Ubiquitous Intelligence and Computing, 13th IEEE International Conference on Advanced and Trusted Computing, 16th IEEE International Conference on Scalable Computing and Communications, IEEE Internationa, 581. doi: 10.1109/UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld.2016.0099.

Eklund, A. et al. 2013 ‘Medical image processing on the GPU - Past, present and future’, Medical Image Analysis. Elsevier B.V., 17(8), pp. 1073–1094. doi: 10.1016/j.media.2013.05.008.

Enfedaque, P., Auli-Llinas, F. and Moure, J. C. 2015 ‘Implementation of the DWT in a GPU through a Register-based Strategy’, IEEE Transactions on Parallel and Distributed Systems, 26(12), pp. 3394–3406. doi: 10.1109/TPDS.2014.2384047.

Enfedaque, P., Auli-Llinas, F. and Moure, J. C. 2017 ‘GPU Implementation of Bitplane Coding with Parallel Coefficient Processing for High Performance Image Compression’, IEEE Transactions on Parallel and Distributed Systems, 28(8), pp. 2272–2284. doi: 10.1109/TPDS.2017.2657506.

Fang, L. et al. 2015 ‘MOC-based parallel preprocessing of ZY-3 satellite images’, IEEE Geoscience and Remote Sensing Letters, 12(2), pp. 419–423. doi: 10.1109/LGRS.2014.2345419.

Fatica, M. and Phillips, E. 2014 ‘Synthetic aperture radar imaging on a CUDA-enabled mobile platform’, in 2014 IEEE High Performance Extreme Computing Conference (HPEC). IEEE, pp. 1–5. doi: 10.1109/HPEC.2014.7040960.

Feng, C., Zhang, X. and Gao, Z. 2015 ‘An Improved Image Super Resolution and Its Parallel ImplementationBased on CUDA’, The Tenth International Conference on Digital Information Management, (Icdim), pp. 182–187.

Fortmeier, D. et al. 2016 ‘A virtual reality system for PTCD simulation using direct visuo-haptic rendering of partially segmented image data’, IEEE Journal of Biomedical and Health Informatics, 20(1), pp. 355–366. doi: 10.1109/JBHI.2014.2381772.

Garcia-Rial, F., Ubeda-Medina, L. and Grajal, J. 2017 ‘Real-time GPU-based image processing for a 3-D THz radar’, IEEE Transactions on Parallel and Distributed

Systems, 28(10), pp. 2953–2964. doi: 10.1109/TPDS.2017.2687927.

González-Albo, B. and Bordons, M. 2011 ‘Articles vs. proceedings papers: Do they differ in research relevance and impact? A case study in the Library and Information Science field’, Journal of Informetrics, 5(3), pp. 369–381. doi: 10.1016/j.joi.2011.01.011.

Green, O. 2018 ‘Efficient scalable median filtering using histogram-based operations’, IEEE Transactions on Image Processing, 27(5), pp. 2217–2228. doi: 10.1109/TIP.2017.2781375.

Grosset, A. V. P. et al. 2017 ‘TOD-tree: Task-overlapped direct send tree image compositing for hybrid MPI parallelism and GPUs’, IEEE Transactions on Visualization and Computer Graphics, 23(6), pp. 1677–1680. doi: 10.1109/TVCG.2016.2542069.

Guerra, R. et al. 2017 ‘On the Evaluation of Different High-Performance Computing Platforms for Hyperspectral Imaging: An OpenCL-Based Approach’, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(11), pp. 4879–4897. doi: 10.1109/JSTARS.2017.2737958.

Guo, X. et al. 2016 ‘Parallel Computation of Aerial Target Reflection of Background Infrared Radiation: Performance Comparison of OpenMP, OpenACC, and CUDA Implementations’, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(4), pp. 1653–1662. doi: 10.1109/JSTARS.2016.2516503.

Gutierrez, P. D. et al. 2014 ‘A high performance fingerprint matching system for large databases based on GPU’, IEEE Transactions on Information Forensics and Security, 9(1), pp. 62–71. doi: 10.1109/TIFS.2013.2291220.

Ha, S. et al. 2013 ‘GPU-accelerated forward and back-projections with spatially varying kernels for 3D DIRECT TOF PET reconstruction’, IEEE Transactions on Nuclear Science, 60(1), pp. 166–173. doi: 10.1109/TNS.2012.2233754.

Haythem, B. et al. 2013 ‘Contribution to the implementation of computer vision application on a GPU’, 2013 International Conference on Control, Decision and Information Technologies, CoDIT 2013, pp. 319–324. doi: 10.1109/CoDIT.2013.6689564.

Haythem, B. et al. 2014 ‘Fast Generalized Fourier Descriptor for object recognition of image using CUDA’, 2014 World Symposium on Computer Applications and Research, WSCAR 2014, (4). doi: 10.1109/WSCAR.2014.6916817.

Heidari, H. 2013 ‘Accelerating of Color Moments and Texture Features Extraction Using GPU Based Parallel Computing’, 8th Iranian Conference on Machine Vision and Image Processing MVIP, pp. 430–435. doi: 10.1109/IranianMVIP.2013.6780024.

Idzenga, T. et al. (2014) ‘Fast 2-D ultrasound strain imaging: The benefits of using a GPU’, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 61(1), pp. 207–213. doi: 10.1109/TUFFC.2014.6689790.

Ikeda, K., Ino, F. and Hagihara, K. 2014 ‘Efficient acceleration of mutual information computation for nonrigid registration using CUDA’, IEEE Journal of Biomedical and Health Informatics, 18(3), pp. 956–968. doi: 10.1109/JBHI.2014.2310745.

Images, U. H. and Martel, E. 2017 ‘A GPU-Based Processing Chain for Linearly’, 10(3), pp. 818–834.

Javier, L., Investigaci, C. S. De and Heras, D. B. 2015 ‘Efficient Classification of Hyperspectral Images on Commodity GPUs using ELM-based Techniques’, 8(6), pp. 2884–2893.

Jha, S. and Trivedi, P. 2013 ‘An automated video surveillance system using Viewpoint Feature Histogram and CUDA-enabled GPUs’, Proceedings of the 2013 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2013, pp. 1812–1816. doi: 10.1109/ICACCI.2013.6637456.

Jia, X., Ziegenhein, P. and Jiang, S. B. 2014 ‘GPU-based high-performance computing for radiation therapy’, Physics in Medicine and Biology, 59(4). doi: 10.1088/0031-9155/59/4/R151.

Jiansen Li et al. 2014 ‘Accelerating the reconstruction of magnetic resonance imaging by three-dimensional dual-dictionary learning using CUDA’, in 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, pp. 2412–2415. doi: 10.1109/EMBC.2014.6944108.

Katsigiannis, S., Zacharia, E. and Maroulis, D. 2015 ‘Grow-Cut Based Automatic cDNA Microarray Image Segmentation’, IEEE Trans Nanobioscience, 14(1), pp. 138–145. doi: 10.1109/tnb.2014.2369961 LB - Katsigiannis2015.

Katsigiannis, S., Zacharia, E. and Maroulis, Di. 2017 ‘MIGS-GPU: Microarray Image Gridding and Segmentation on the GPU’, IEEE Journal of Biomedical and Health Informatics, 21(3), pp. 867–874. doi: 10.1109/JBHI.2016.2537922.

Kau, L. J. and Chen, C. S. 2013 ‘Speeding up the runtime performance for lossless image coding on GPUs with CUDA’, Proceedings - IEEE International Symposium on Circuits and Systems, (1), pp. 2868–2871. doi: 10.1109/ISCAS.2013.6572477.

Kim, K. S. et al. 2014 ‘Ultra-fast hybrid CPU-GPU multiple scatter simulation for 3-D PET’, IEEE Journal of Biomedical and Health Informatics, 18(1), pp. 148–156. doi: 10.1109/JBHI.2013.2267016.

Kowalczuk, J., Psota, E. T. and Perez, L. C. 2013 ‘Real-Time Stereo Matching on {CUDA} Using an Iterative Refinement Method for Adaptive Support Weight Correspondences’, IEEE Trans. CSVT, 23(1), pp. 94–104.

Lee, I.-H. et al. 2013 ‘Accelerating motion-compensated adaptive color Doppler engine on CUDA-based GPU platform’, Signal Processing Systems (SiPS), 2013 IEEE Workshop on, pp. 1–6.

Lei, Z. et al. 2014 ‘Stream model-based orthorectification in a GPU cluster environment’, IEEE Geoscience and Remote Sensing Letters, 11(12), pp. 2115–2119. doi: 10.1109/LGRS.2014.2320991.

Li, X., Huang, B. and Zhao, K. 2015 ‘Massively Parallel GPU Design of Automatic Target Generation Process in Hyperspectral Imagery’, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(6), pp. 2862–2869. doi: 10.1109/JSTARS.2014.2347299.

Li, Y., Mogelmose, A. and Trivedi, M. M. 2016 ‘Pushing the “Speed Limit”: High-Accuracy US Traffic Sign Recognition With Convolutional Neural Networks’, IEEE Transactions on Intelligent Vehicles, 1(2), pp. 167–176. doi: 10.1109/TIV.2016.2615523.

Liu, J. et al. 2015 ‘Multicore Processors and Graphics Processing Unit Accelerators for Parallel Retrieval of Aerosol Optical Depth From Satellite Data: Implementation, Performance, and Energy Efficiency’, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(5), pp. 2306–2317. doi: 10.1109/JSTARS.2015.2438893.

Loock, W. Van et al. 2014 ‘Short Papers’, 35(12), pp. 1–6.

Lopez-Fandino, J. et al. 2017 ‘GPU Projection of ECAS-II Segmenter for Hyperspectral Images Based on Cellular Automata’, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(1), pp. 20–28. doi: 10.1109/JSTARS.2016.2588530.

Luo, Y. L. 2013 ‘Effectively visualizing the spatial structure of cerebral blood vessels’, Computing in Science and Engineering, 15(2), pp. 41–46. doi: 10.1109/MCSE.2013.25.

Marcellino, L. and Navarra, G. 2016 ‘A GPU-accelerated SVD algorithm , based on QR factorization and Givens Rotations , for DWI denoising .’ doi: 10.1109/SITIS.2016.117.

Method, A. I. D. et al. 2014 ‘GPU-Parallel Implementation of the Edge-Directed’, 10(9), pp. 746–753.

Moore, G. E. 1998 ‘Cramming more components onto integrated circuits’, Proceedings of the IEEE, 86(1), pp. 82–85. doi: 10.1109/JPROC.1998.658762.

Moore, G. E. 2005 ‘Excerpts from A Conversation with Gordon Moore: Moore’s Law’, Intel, pp. 1–2. Available at: http://large.stanford.edu/courses/2012/ph250/lee1/docs/Excepts_A_Conversation_with_Gordon_Moore.pdf%0Ahttp://download.intel.com/museum/Moores_law/Video-transcripts/excepts_a_Conversation_with_gordon_Moore.pdf.

Moore, N., Leeser, M. and King, L. S. 2015 ‘Kernel specialization provides adaptable GPU code for particle image velocimetry’, IEEE Transactions on Parallel and Distributed Systems, 26(4), pp. 1049–1058. doi: 10.1109/TPDS.2014.2317721.

Nguyen, V. G. and Lee, S. J. 2015 ‘Parallelizing a matched pair of ray-tracing projector and backprojector for iterative cone-beam CT reconstruction’, IEEE Transactions on Nuclear Science, 62(1), pp. 171–181. doi: 10.1109/TNS.2015.2388553.

Niederhauser, T. et al. 2015 ‘Graphics-processor-unit-based parallelization of optimized baseline wander filtering algorithms for long-term electrocardiography’, IEEE Transactions on Biomedical Engineering, 62(6), pp. 1576–1584. doi: 10.1109/TBME.2015.2395456.

NVIDIA no date Procesamiento paralelo CUDA | Qué es CUDA | NVIDIA. Available at: https://developer.nvidia.com/cuda-zone.

Nykl, S., Mourning, C. and Chelberg, D. 2014 ‘Interactive Mesostructures withVolumetric Collisions’, IEEE Transactions on Visualization and Computer Graphics, 20(7), pp. 970–982. doi: 10.1109/TVCG.2014.2317700.

Optimization, G. 2018 ‘Image Autoregressive Interpolation Model Using’, 14(2), pp. 426–436.

Ordonez, A., Arguello, F. and Heras, D. B. 2017 ‘GPU accelerated FFT-based registration of hyperspectral scenes’, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(11), pp. 4869–4878. doi: 10.1109/JSTARS.2017.2734052.

Pawar, D. 2017 ‘GPU Based Background Subtraction Using CUDA : State of the Art’, pp. 1201–1204.

Phusomsai, W. and So-in, C. 2016 ‘Brain Tumor Cell Recognition Schemes using Image Processing with Parallel ELM Classifications on GPU’.

Projection, B. et al. 2018 ‘A Look-Up Table-Based Ray Integration’, 37(2), pp. 361–371.

Punithakumar, K., Boulanger, P. and Noga, M. 2017 ‘A GPU-Accelerated Deformable Image Registration Algorithm with Applications to Right Ventricular Segmentation’, IEEE Access, 5, pp. 20374–20382. doi: 10.1109/ACCESS.2017.2755863.

Rakvic, R., Broussard, R. and Ngo, H. A. U. 2016 ‘Energy Efficient Iris Recognition With Graphics Processing Units’, 4, pp. 2831–2839. doi: 10.1109/ACCESS.2016.2571747.

Romero-Laorden, D. et al. 2016 ‘Analysis of Parallel Computing Strategies to Accelerate Ultrasound Imaging Processes’, IEEE Transactions on Parallel and Distributed Systems, 27(12), pp. 3429–3440. doi: 10.1109/TPDS.2016.2544312.

Santos, L. et al. 2013 ‘Highly-parallel gpu architecture for lossy hyperspectral image compression’, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6(2), pp. 670–681. doi: 10.1109/JSTARS.2013.2247975.

Saxena, S., Sharma, S. and Sharma, N. 2014 ‘Image registration techniques using parallel computing in multicore environment and its applications in medical imaging: An overview’, 2014 International Conference on Computer and Communication Technology (ICCCT), pp. 97–104. doi: 10.1109/ICCCT.2014.7001475.

Shuai, L. et al. 2017 ‘Motion Capture with Ellipsoidal Skeleton Using Multiple Depth Cameras’, IEEE Transactions on Visualization and Computer Graphics, 23(2), pp. 1085–1098. doi: 10.1109/TVCG.2016.2520926.

Song, J. et al. 2017 ‘The Reconnection of Contour Lines from Scanned Color Images of Topographical Maps Based on GPU Implementation’, 10(2), pp. 400–408.

Sun, L. C. et al. 2013 ‘Acceleration algorithm for CUDA-based face detection’, 2013 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2013. doi: 10.1109/ICSPCC.2013.6664139.

Sun, X. and Wang, R. 2015 ‘Fast Smoke Detection for video surveillance Using CUDA’, 14(2), pp. 725–733. doi: 10.1109/BigMM.2015.86.

Tagliavini, G., Cesarini, D. and Marongiu, A. 2018 ‘Unleashing Fine-Grained Parallelism on Embedded Many-Core Accelerators with Lightweight OpenMP Tasking’, IEEE Transactions on Parallel and Distributed Systems, 9219(X), pp. 1–1. doi: 10.1109/TPDS.2018.2814602.

Tan, K. et al. 2015 ‘GPU Parallel Implementation of Support Vector Machines for Hyperspectral Image Classification’, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(10), pp. 4647–4656. doi: 10.1109/JSTARS.2015.2453411.

Topa, T. 2017 ‘Load-Balanced Fortran-Based Out-of-GPU Memory Implementation of the Method of Moments’, IEEE Antennas and Wireless Propagation Letters, 16, pp. 813–816. doi: 10.1109/LAWP.2016.2605042.

Torti, E. et al. 2014 ‘Real-Time Identi fi cation of Hyperspectral Subspaces’, 7(6), pp. 2680–2687.

Torti, E., Danese, G. and Leporati, F. 2015 ‘A Hybrid CPU – GPU Real-Time Hyperspectral’, 9(2), pp. 1–7.

Tung, C. T. 2009 ‘Computing 2D Delaunay Triangulation using GPU’, Comp.Nus.Edu.Sg, 19(5), pp. 736–748. Available at: http://www.comp.nus.edu.sg/~tants/delaunay2DDownload_files/cao_hyp_2009.pdf.

Vogel, T. 2013 ‘All the way to CUDA’, Computing in Science and Engineering, 15(5), pp. 6–8. doi: 10.1109/MCSE.2013.101.

Vokorokos, L. et al. 2014 ‘Increasing efficiency of the sequential algorithms programs execution using CUDA’, SAMI 2014 - IEEE 12th International Symposium on Applied Machine Intelligence and Informatics, Proceedings, pp. 281–284. doi: 10.1109/SAMI.2014.6822422.

Wang, X. Y., Li, M. and Abubakar, A. 2015 ‘Acceleration of multiplicative regularized contrast source inversion algorithm using paralleled computing device’, Microwave Conference (APMC), 2015 Asia-Pacific, 3(2), pp. 1–3.

Weinlich, A. et al. 2013 ‘Volumetric deformation compensation in CUDA for coding of dynamic cardiac images’, in 2013 Picture Coding Symposium (PCS). IEEE, pp. 189–192. doi: 10.1109/PCS.2013.6737715.

Won, J. H. et al. 2013 ‘Uncluttered single-image visualization of vascular structures using GPU and integer programming’, IEEE Transactions on Visualization and Computer Graphics, 19(1), pp. 81–93. doi: 10.1109/TVCG.2012.25.

Wu, X. et al. 2016 ‘GPU-Based Parallel Design of the Hyperspectral Signal Subspace Identification by Minimum Error (HySime)’, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(9), pp. 4400–4406. doi: 10.1109/JSTARS.2016.2574876.

Wu, Z. et al. 2014 ‘Sparse non-negative matrix factorization on GPUs for hyperspectral unmixing’, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(8), pp. 3640–3649. doi: 10.1109/JSTARS.2014.2315045.

Wu, Z., Wang, Q., Plaza, A., Li, J., et al. 2015 ‘Parallel Implementation of Sparse Representation Classifiers for Hyperspectral Imagery on GPUs’, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(6), pp. 2912–2925. doi: 10.1109/JSTARS.2015.2413831.

Wu, Z., Wang, Q., Plaza, A. and Li, J. 2015 ‘Parallel Spatial – Spectral Hyperspectral Image Classification With Sparse Representation and Markov Random Fields on GPUs’, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(6), pp. 2926–2938. doi: 10.1109/JSTARS.2015.2413931.

Xanthis, C. G. et al. 2014 ‘MRISIMUL: A GPU-based parallel approach to MRI simulations’, IEEE Transactions on Medical Imaging, 33(3), pp. 607–617. doi: 10.1109/TMI.2013.2292119.

Xu, F., Dinavahi, V. and Xu, X. 2016 ‘Parallel Computation of Wrench Model for Commutated Magnetically Levitated Planar Actuator’, IEEE Transactions on Industrial Electronics, 63(12), pp. 7621–7631. doi: 10.1109/TIE.2016.2592866.

Yazdanpanah, A. P. et al. 2014 ‘A CUDA based implementation of locally-and feature-adaptive diffusion based image denoising algorithm’, ITNG 2014 - Proceedings of the 11th International Conference on Information Technology: New Generations, pp. 388–393. doi: 10.1109/ITNG.2014.113.

Yu, D. et al. 2015 ‘Fast Rotation-Free Feature Based Image Registration Using Improved N-SIFT and GMM Based Parallel Optimization’, IEEE Transactions on Biomedical Engineering, 9294(c), pp. 1–1. doi: 10.1109/TBME.2015.2465855.

Zhang, Z. et al. 2013 ‘GPU-Accelerated Real-Time Tracking of Full-Body Motion With Multi-Layer

Search’, IEEE Transactions on Multimedia, 15(1), pp. 106–119. doi: 10.1109/TMM.2012.2225040.

Zhao, M. et al. 2015 ‘Real-time and temporal-coherent foreground extraction with commodity RGBD camera’, IEEE Journal on Selected Topics in Signal Processing, 9(3), pp. 449–461. doi: 10.1109/JSTSP.2014.2382476.

Zhao, Z., Zhang, X. and Fang, Y. 2015 ‘Stacked multilayer self-organizing map for background modeling’, IEEE Transactions on Image Processing, 24(9), pp. 2841–2850. doi: 10.1109/TIP.2015.2427519.

Zhou, X., Li, X. and Hu, W. 2016 ‘Learning A Superpixel-Driven Speed Function for Level Set Tracking’, IEEE Transactions on Cybernetics, 46(7), pp. 1498–1510. doi: 10.1109/TCYB.2015.2451100.

Zhu, Z. et al. 2013 ‘Research on CUDA-based image parallel dense matching’, Proceedings - 2013 Chinese Automation Congress, CAC 2013, pp. 482–486. doi: 10.1109/CAC.2013.6775782.

Zwan, M. Van Der, Codreanu, V. and Telea, A. 2016 ‘CUBu : Universal real-time bundling for large’, 22(12), pp. 1–14.

Published

2018-12-11

How to Cite

[1]
F. J. Enríquez Aguilera, J. M. Silva Aceves, S. V. Torres Argüelles, E. A. Martínez Gómez, and G. Bravo Martínez, “Utilización de GPU-CUDA en el Procesamiento Digital de Imágenes”, Cult. Científ. y Tecnol., no. 66, Dec. 2018.

Issue

No. 66 (15): Septiembre–Diciembre, 2018. Año 15, Nº 66

Section

Artículos de revisión

License

Todos los contenidos de CULCYT se distribuyen bajo una licencia de uso y distribución “Creative Commons Reconocimiento-No Comercial 4.0 Internacional” (CC-BY-NC). Puede consultar desde aquí la versión informativa de la licencia.

Los autores/as que soliciten publicar en esta revista, aceptan los términos siguientes: a) los/las autores/as conservarán sus derechos de autor y garantizarán a la revista el derecho de primera publicación de su obra; y b) se permite y recomienda a los/las autores/as agregar enlaces de sus artículos en CULCYT en la página web de su institución o en la personal, debido a que ello puede generar intercambios interesantes y aumentar las citas de su obra publicada.