Results 251 to 260 of about 55,999 (288)
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CUDA accelerated iris template matching on Graphics Processing Units (GPUs)
2010 Fourth IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS), 2010In this paper we develop a parallelized iris template matching implementation on inexpensive Graphics Processing Units (GPUs) with Nvidia's CUDA programming model to achieve matching rates of 44 million iris template comparisons per second without rotation invariance.
Nicholas A. Vanda, Marios Savvides
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Acceleration of hough transform algorithm using Graphics Processing Unit (GPU)
2016 International Conference on Communication and Signal Processing (ICCSP), 2016The Hough Transform is a popular tool for feature detection in digital image processing due to its robustness to noise and missing data. However, the computational cost associated to its voting scheme has prevented software implementation to achieve real time performance. This paper demonstrates the parallel computing power of Graphics Processing Unit (
Parag Ram Patil +2 more
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Towards using the Graphics Processing Unit (GPU) for embedded systems
Proceedings of 2012 IEEE 17th International Conference on Emerging Technologies & Factory Automation (ETFA 2012), 2012The Graphics Processing Unit (GPU) is becoming a very powerful platform to accelerate graphics and dataparallel compute-intensive applications. It gives a high performance and at the same time it has a low power consumption. This combination is of high performance and low power consumption is useful when it comes to building an embedded system. In this
Daniel Hallmans +2 more
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Leveraging graphics processing units (GPUs) for real-time seismic interpretation
The Leading Edge, 2010This article discusses the emergence of general computation on the graphics processing unit (GPGPU) as a powerful tool for enabling true interactive 3D seismic interpretation such that geologic features are computed, mapped, and visualized in real time.
Benjamin J. Kadlec, Geoffrey A. Dorn
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Utilizing Graphics Processing Units (GPUs) for Numerical Computations
Initially designed for graphics processing, GPUs (graphics processing unit) only contained fixed rendering functions. As they are parallel processors with high computational power for arithmetic calculations, GPUs have been evolving rapidly with the inclusion of programmable pipelines.Alnoman Mundher Tayyeh +3 more
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Fast motion estimation for HEVC on graphics processing unit (GPU)
Journal of Real-Time Image Processing, 2015The recent video compression standard, HEVC (high efficiency video coding), will most likely be used in various applications in the near future. However, the encoding process is far too slow for real-time applications. At the same time, computing capabilities of GPUs (graphics processing units) have become more powerful in these days. In this paper, we
Dongkyu Lee +3 more
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Fast evaluation of Helmholtz potential on graphics processing units (GPUs)
Journal of Computational Physics, 2010zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Li, Shaojing +2 more
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Computing resultants on Graphics Processing Units: Towards GPU-accelerated computer algebra
Journal of Parallel and Distributed Computing, 2013In this article we report on our experience in computing resultants of bivariate polynomials on Graphics Processing Units (GPU). Following the outline of Collins' modular approach [6], our algorithm starts by mapping the input polynomials to a finite field for sufficiently many primes m.
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Feature‐Preserving Displacement Mapping With Graphics Processing Unit (GPU) Tessellation
Computer Graphics Forum, 2012AbstractDisplacement mapping reconstructs a high‐frequency surface by adding geometric details encoded in the displacement map to the coarse base surface. In the context of hardware tessellation supported by GPUs, this paper aims at feature‐preserving surface reconstruction, and proposes the generation of a displacement map that displaces more vertices
Hanyoung Jang, JungHyun Han
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Graphical Processing Units (GPUs): Opportunities and Challenges
2011GPUs are more and more used as low cost high performance computing platforms. While new parallel computing architectures and languages such as OpenCL and CUDA, as well as some new libraries ease up their programming, it is still relatively difficult to design code for them in an efficient way and it gives us a taste of what pioneers experimented in the
El-Ghazawi, Tarek +4 more
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