GpuCV

Allusse, Yannick; Horain, Patrick; Agarwal, Ankit; Saipriyadarshan, Cindula

doi:10.1145/1459359.1459578

Cited by 34 publications

(2 citation statements)

References 1 publication

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“…We use SugoiTracer [20] to collect the statistics (such as average processing time, standard deviation, total time...). The mechanism leaves benchmarking mode to go to switch mode when the standard deviation time shows stable and coherent values.…”

Section: Switch Implementationmentioning

confidence: 99%

“…GpuCV integrates some embedded benchmarking tools [20] that are used to record data transfer times and processing time for all GPU and CPU implementations. It can be used to benchmark a native OpenCV application and return statistics about all the OpenCV calls depending on input parameters such as data size, format and operators options such as filter size of filter mode.…”

Section: Benchmarking Toolsmentioning

confidence: 99%

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GpuCV: A GPU-Accelerated Framework for Image Processing and Computer Vision

Allusse

Horain

Agarwal

et al. 2008

Advances in Visual Computing

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Abstract. This paper presents briefly describes the state of the art of accelerating image processing with graphics hardware (GPU) and discusses some of its caveats. Then it describes GpuCV, an open source multi-platform library for GPU-accelerated image processing and Computer Vision operators and applications. It is meant for computer vision scientist not familiar with GPU technologies. GpuCV is designed to be compatible with the popular OpenCV library by offering GPUaccelerated operators that can be integrated into native OpenCV applications. The GpuCV framework transparently manages hardware capabilities, data synchronization, activation of low level GLSL and CUDA programs, on-the-fly benchmarking and switching to the most efficient implementation and finally offers a set of image processing operators with GPU acceleration available.

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Section: Switch Implementationmentioning

confidence: 99%

Section: Benchmarking Toolsmentioning

confidence: 99%

GpuCV: A GPU-Accelerated Framework for Image Processing and Computer Vision

Allusse

Horain

Agarwal

et al. 2008

Advances in Visual Computing

Self Cite

View full text Add to dashboard Cite

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Web‐based 3D medical image visualization framework for biomedical engineering education

Settapat

Achalakul

Ohkura

2011

Comp Applic In Engineering

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Medical imaging is one of the major fields in the multi-disciplinary curriculum of biomedical engineering (BME). Biomedical engineers from different backgrounds need to understand biology in order to be able to develop effective equipment to improve healthcare diagnosis through medical images. Visualization tools are important in the learning process to improve biomedical engineer's understanding of medical imagery. In this work, we design a web-based 3D medical image visualization framework that can be used to improve medical image understanding in biology and anatomy. Our proposed framework provides not only 3D visualization, but also 3D reconstruction for medical images. This paper describes the design framework and the technology integration, as well as the implementation details. The developed system has been used as an educational tool prototype in a BME department. In order to evaluate the tool's usability, we tested it with BME students as well as doctors. We compared a conventional 2D visualization application and our proposed method with regard to system efficiency and user satisfaction. The 3D system generally demonstrated better performance and a higher level of satisfaction. The students were able to use our 3D tool to study 2D images effectively without prior background knowledge in anatomy. ß

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Stream processing on GPUs using distributed multimedia middleware

Repplinger

Slusallek

2010

Concurrency and Computation

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SUMMARYAvailable GPUs provide increasingly more processing power especially for multimedia and digital signal processing. Despite the tremendous progress in hardware and thus processing power, there are and always will be applications that require using multiple GPUs either running inside the same machine or distributed in the network due to computationally intensive processing algorithms. Existing solutions for developing applications for GPUs still require a lot of hand-optimization when using multiple GPUs inside the same machine and provide in general no or only limited support for using remote GPUs distributed in the network. In this paper we address this problem and show that an open distributed multimedia middleware, like the Network-Integrated Multimedia Middleware (NMM), is able (1) to seamlessly integrate processing components using GPUs, while completely hiding GPU-specific issues from the application developer, (2) to transparently combine processing components using GPUs or CPUs, and (3) to transparently use local and remote GPUs for distributed processing. Furthermore, we present a generic distribution framework to simplify the development of complex application scenarios.

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GpuCV

Cited by 34 publications

References 1 publication

GpuCV: A GPU-Accelerated Framework for Image Processing and Computer Vision

GpuCV: A GPU-Accelerated Framework for Image Processing and Computer Vision

Web‐based 3D medical image visualization framework for biomedical engineering education

Stream processing on GPUs using distributed multimedia middleware

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