GTC2013

Tag: image processing

High Quality Real-Time Video with Scanning Electron Microscope Using Total Variation Algorithm on a GPU

High Quality Real-Time Video with Scanning Electron Microscope Using Total Variation Algorithm on a GPU

| 17 July, 2012 | 1 Comment

We showed that a limited number of iterations for Total Variation algorithm can lead to an acceptable quality of regularization. This algorithm is parallel and deployed on a Graphics Processing Unit to obtain a real-time high quality video with a SEM.

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GPU Implementation of an Automatic Target Detection and Classification Algorithm for Hyperspectral Image Analysis

GPU Implementation of an Automatic Target Detection and Classification Algorithm for Hyperspectral Image Analysis

| 13 July, 2012 | 0 Comments

In this letter, we have developed the first real-time implementation of an ATDCA, implemented with GS orthogonalization, on GPU architectures. The proposed implementation has been specifically tailored to specific aspects involved in hyperspectral data processing and makes advanced use of the GPU architecture

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GPUs are competing FPGA-based image enhancement for specialized cameras

GPUs are competing FPGA-based image enhancement for specialized cameras

| 25 May, 2012 | 0 Comments

Short lecture by Dr. Reinhard Borst, ELTEC Elektronik AG. It has been delivered at SpectroNet Collaboration Forum Konstanz, 2012.

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GPU Acceleration of Functional Neuroimaging

GPU Acceleration of Functional Neuroimaging

| 11 May, 2012 | 2 Comments

GPUs accelerate functional neuroimaging, by using three GPUs, 850 TB of data can be analyzed in 10 days, compared to 100 years with conventional software!

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Accelerating batch processing of spatial raster analysis using GPU

Accelerating batch processing of spatial raster analysis using GPU

| 28 January, 2012 | 2 Comments

The scope of this paper is to present an efficient two-level caching strategy for raster data and an acceleration of selected raster operations using the GPU, which were implemented as a plugin for the open source software GRASS.

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Processing piecewise autoregressive model image interpolation algorithm on GPU with CUDA

Processing piecewise autoregressive model image interpolation algorithm on GPU with CUDA

| 27 January, 2012 | 0 Comments

This paper presents a parallel implementation of piecewise autoregressive modeling image interpolation algorithm, using CUDA (Compute Unified Device Architecture) on GPU

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fMRI analysis on the GPU – Possibilities and challenges

fMRI analysis on the GPU – Possibilities and challenges

| 23 January, 2012 | 0 Comments

We describe how to perform preprocessing and statistical analysis of fMRI data on the GPU. Non-parametric tests of fMRI data become practically feasible by using the GPU. GPUs are required to handle the future increase in spatial and temporal resolution. GPUs enable more advanced real-time analysis.

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Improved tracking technique for visual measurements of ionic polymer–metal composites (IPMC) actuators using CUDA

Improved tracking technique for visual measurements of ionic polymer–metal composites (IPMC) actuators using CUDA

| 4 November, 2011 | 0 Comments

The implementation of a real-time measurement system based on visual measurements of displacement of an actuator–cantilever is presented in this paper.

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Achieving a single compute device image in OpenCL for multiple GPU

Achieving a single compute device image in OpenCL for multiple GPU

| 1 November, 2011 | 0 Comments

In this paper, we propose an OpenCL framework that combines multiple GPUs and treats them as a single compute device. Providing a single virtual compute device image to the user makes an OpenCL application written for a single GPU portable to the platform that has multiple GPU devices.

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Volume visualization: A technical overview with a focus on medical applications

Volume visualization: A technical overview with a focus on medical applications

| 27 October, 2011 | 0 Comments

In this paper, we review volumetric image visualization pipelines, algorithms, and medical applications. We also illustrate our algorithm implementation and evaluation results, and address the advantages and drawbacks of each algorithm in terms of image quality and efficiency.

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