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2014 International conference on Reconfigurable Computing and FAGAs

作者： Skalicky, Sam Kwolek, Tyler Lopez, Sonia Lukowiak, Marcin Rochester Inst Technol Rochester NY 14623 USA

ISBN: (纸本)9781479959440

FPGAs have been shown to provide orders of magnitude improvement over CPUs and GPUs in terms of absolute performance and energy efficiency for various kernels such as Cholesky decomposition, matrix inversion, and FFT among others. Despite this, the overall performance of many applications suffer when implemented entirely in FPGAs. Combining FPGAs with CPUs and GPUs provides the range of capabilities needed to support diverse computational requirements of applications. Integrating FPGAs into these systems challenges application developers with constructing hardware kernel implementations and interfacing from the low level hardware logic in the FPGA to the high speed networks that connect processors in the system. In this work we extend the compute capabilities of MATLAB by incorporating support for FPGAs and automating the parallel code generation. We characterize the system and evaluate the performance gains that can be achieved by adding the FPGA for two compute intensive applications. We present performance results for medical imaging and fluid dynamics applications implemented in a CPU+GPU+FPGA system and achieved up to 40x improvement compared to the standard MATLAB CPU+GPU environment.

关键词： computational fluid dynamics fast Fourier transforms field programmable gate arrays graphics processing units mathematics computing matrix inversion medical image processing multiprocessing systems parallel processing parallelising compilers CPU Cholesky decomposition FFT FPGA support GPU MATLAB based heterogeneous systems absolute performance application development computational requirements compute intensive application energy efficiency fluid dynamics application hardware kernel implementation high speed network low level hardware logic medical imaging parallel code generation performance gains processor connection Field programmable gate arrays Graphics processing units Hardware Instruction sets Kernel MATLAB

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On the design of a transversal filter bank for parallel processing multiple image lines in real-time acoustic imaging

On the design of a transversal filter bank for parallel proc...

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Proceedings of the 1996 IEEE International conference on Acoustics, Speech, and Signal processing, ICASSP. Part 1 (of 6)

作者： Shen, Jian Ebbini, Emad S. Univ of Michigan Ann Arbor United States

We present a new method for designing a transversal filter bank for parallel processing echo data in real-time active acoustic imaging. The method is based on a discretized linear spatio-temporal formulation of an imaging system which uses transmitter arrays with coded-excitation. The filter coefficients are derived from a pseudo-inverse operator which not only decouples echoes from different image lines, but also provides pulse compression along the range direction. The new method, together with the proposed coded-excitation imaging modality, may find many applications in SONAR, NDE and biomedical imaging areas.

关键词： Acoustic imaging

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Multi-view Stereo Reconstruction via Voxel Clustering and Optimization of parallel Volumetric Graph Cuts

Multi-view Stereo Reconstruction via Voxel Clustering and Op...

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conference on parallel processing for imaging applications

作者： Zhu Yun-Feng Zhang Yu-Jin Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China

ISBN: (纸本)9780819484093

Traditional multi-view stereo reconstruction via volumetric graph cuts formulates the 3D reconstruction problem as a computationally tractable global optimization using graph cuts. It benefits from a volumetric scene representation and discrete photo consistency is defined on the edge cost with a weighted graph. As the independence between each discrete voxel, it is natural to do the parallel processing with multi-core CPUs or GPU, but after the photo consistency has been estimated, it still need to design a parallel optimized methods to get the optimized labeling results for each voxel. In our paper, we use the parallel volumetric graph cuts methods to solve the above problems. Our algorithm has two main steps, clustering step and parallel graph cuts optimization step. We also introduce an approach for enhancing accuracy and speeding up existing Multi-view 3D reconstruction methods, which based on volumetric graph cuts. The main idea is to decompose the collected photos into some overlapping sets, while the voxels are also be clustered. The voxels consistency estimating and surface labeling with graph cuts are processed in parallel, however, the labels of the overlapped voxels may in general have multiple label solutions. It will be constrained to be equal to obtain a unique solution in parallel graph cuts optimization step.

关键词： Multi-view stereo reconstruction Voxel clustering parallel graph cuts Dual decomposition

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parallel morphological/neural processing of hyperspectral images using heterogeneous and homogeneous platforms

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND applications 2008年第1期11卷 17-32页

作者： Plaza, Javier Perez, Rosa Plaza, Antonio Martinez, Pablo Valencia, David Univ Extremadura Neural Networks & Signal Proc Grp Dept Comp Sci Caceres 10071 Spain

The wealth spatial and spectral information available from last-generation Earth observation instruments has introduced extremely high computational requirements in many applications. Most currently available parallel techniques treat remotely sensed data not as images, but as unordered listings of spectral measurements with no spatial arrangement. In thematic classification applications, however, the integration of spatial and spectral information can be greatly beneficial. Although such integrated approaches can be efficiently mapped in homogeneous commodity clusters, low-cost heterogeneous networks of computers (HNOCs) have soon become a standard tool of choice for dealing with the massive amount of image data produced by Earth observation missions. In this paper, we develop a new morphological/neural algorithm for parallel classification of high-dimensional (hyperspectral) remotely sensed image data sets. The algorithm's accuracy and parallel performance is tested in a variety of homogeneous and heterogeneous computing platforms, using two networks of workstations distributed among different locations, and also a massively parallel Beowulf cluster at NASA's Goddard Space Flight Center in Maryland.

关键词： heterogeneous computing parallel performance neural networks hyperspectral imaging mathematical morphology

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Accelerating image recognition on mobile devices using GPGPU

Accelerating image recognition on mobile devices using GPGPU

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conference on parallel processing for imaging applications

作者： Lopez, Miguel Bordallo Nykanen, Henri Hannuksela, Jari Silven, Olli Vehvilainen, Markku Univ Oulu Machine Vis Grp Oulu Finland Visidon Ltd Oulu Finland Nokia Res Ctr Tampere Finland

ISBN: (纸本)9780819484093

The future multi-modal user interfaces of battery-powered mobile devices are expected to require computationally costly image analysis techniques. The use of Graphic processing Units for computing is very well suited for parallel processing and the addition of programmable stages and high precision arithmetic provide for opportunities to implement energy-efficient complete algorithms. At the moment the first mobile graphics accelerators with programmable pipelines are available, enabling the GPGPU implementation of several image processing algorithms. In this context, we consider a face tracking approach that uses efficient gray-scale invariant texture features and boosting. The solution is based on the Local Binary Pattern (LBP) features and makes use of the GPU on the pre-processing and feature extraction phase. We have implemented a series of image processing techniques in the shader language of OpenGL ES 2.0, compiled them for a mobile graphics processing unit and performed tests on a mobile application processor platform (OMAP3530). In our contribution, we describe the challenges of designing on a mobile platform, present the performance achieved and provide measurement results for the actual power consumption in comparison to using the CPU (ARM) on the same platform.

关键词： Computer vision cell phone image recognition graphics processing unit GPGPU

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TEXTURED AREAS DETECTION AND SEGMENTATION IN CIRCULAR HARMONIC FUNCTIONS DOMAIN

TEXTURED AREAS DETECTION AND SEGMENTATION IN CIRCULAR HARMON...

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conference on Image processing - Algorithms and Systems X and parallel processing for imaging applications II

作者： Costantini, Luca Capodiferro, Licia Carli, Marco Neri, Alessandro Fdn Ugo Bordoni Viale Policlin 147 I-00161 Rome Italy Univ Degli Studi Roma Rome Italy

ISBN: (纸本)9780819489425

In this work a novel technique for detecting and segmenting textured areas in natural images is presented. The method is based on the circular harmonic function, and, in particular, on the Laguerre Gauss functions. The detection of the textured areas is performed by analyzing the mean, the mode, and the skewness of the marginal densities of the Laguerre Gauss coefficients. By using these parameters a classification of the patch and of the pixel, is performed. The feature vectors representing the textures are built using the parameters of the Generalized Gaussian Densities that approximate the marginal densities of the Laguerre Gauss functions computed at three different resolutions. The feature vectors are clustered by using the K-means algorithm in which the symmetric Kullback-Leibler distance is adopted. The experimental results, obtained by using a set of natural images, show the effectiveness of the proposed technique.

关键词： Non linear image processing circular harmonic wavelet textured areas detection texture segmentation

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Using a commercial graphical processing unit and the CUDA programming language to accelerate scientific image processing applications

Using a commercial graphical processing unit and the CUDA pr...

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conference on parallel processing for imaging applications

作者： Broussard, Randy P. Ives, Robert W. USN Acad Weapons & Syst Engn Dept Annapolis MD 21402 USA US Naval Acad Elect & Comp Engn Dept Annapolis MD 21402 USA

ISBN: (纸本)9780819484093

In the past two years the processing power of video graphics cards has quadrupled and is approaching super computer levels. State-of-the-art graphical processing units (GPU) boast of theoretical computational performance in the range of 1.5 trillion floating point operations per second (1.5 Teraflops). This processing power is readily accessible to the scientific community at a relatively small cost. High level programming languages are now available that give access to the internal architecture of the graphics card allowing greater algorithm optimization. This research takes memory access expensive portions of an image-based iris identification algorithm and hosts it on a GPU using the C++ compatible CUDA language. The selected segmentation algorithm uses basic image processing techniques such as image inversion, value squaring, thresholding, dilation, erosion and memory/computationally intensive calculations such as the circular Hough transform. Portions of the iris segmentation algorithm were accelerated by a factor of 77 over the 2008 GPU results. Some parts of the algorithm ran at speeds that were over 1600 times faster than their CPU counterparts. Strengths and limitations of the GPU Single Instruction Multiple Data architecture are discussed. Memory access times, instruction execution times, programming details and code samples are presented as part of the research.

关键词： Image processing GPGPU GPU graphics card CUDA iris identification biometrics

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A GPU-based Architecture for Improved Online Rebinning Performance in Clinical 3-D PET

A GPU-based Architecture for Improved Online Rebinning Perfo...

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IEEE Nuclear Science Symposium conference 2009

作者： Patlolla, Dilip R. Breeding, Eric Jones, William F. Everman, Jimmy Siemens Med Solut USA Inc Knoxville TN 37932 USA

ISBN: (纸本)9781424439614

Online rebinning is an important and well-established technique for reducing the time required to process PET data. However, the need for efficient data processing in a clinical setting is growing rapidly and is beginning to exceed the capability of traditional online processing methods. High-count rate applications such as Rubidium 3-D PET studies can easily saturate current online rebinning technology. Real-time processing at these high count rates is essential to avoid significant data loss. In addition, the emergence of time-of-flight (TOF) scanners is producing very large data sets for processing. TOF applications require efficient online rebinning methods so as to maintain high patient throughput. Currently, several new hardware architectures like Graphics processing Units (GPUs) are available to speed up the data parallel and number crunching algorithms. In comparison to the usual parallel systems, such as multiprocessor or clustered machines, CPU hardware can be much faster and above all, it is significantly cheaper. The GPUs have been primarily delivered for graphics for video games but are now being used for High Performance computing across many domains. In this study, we investigate the suitability of the GPU for PET Rebinning algorithms and also implement them on the same.

关键词： Graphics processing unit

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Robust Detection and Estimation in Dynamic Systems and Statistical Signal processing: Intersections, parallel Paths and applications

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EUROPEAN JOURNAL OF CONTROL 2009年第3-4期15卷 348-369页

作者： Mangoubi, Rami Desai, Mukund Edelmayer, Andras Sammak, Paul Charles Stark Draper Lab Inc Cambridge MA 02139 USA Hungarian Acad Sci Comp & Automat Res Inst Syst & Control Lab H-1111 Budapest Hungary Univ Pittsburgh Magee Womens Res Inst Pittsburgh PA 15213 USA

Algorithms for Robust Fault Detection and Identification (RFDI) in dynamic systems strive to be sensitive to faults in plant components, while being insensitive to plant and noise model uncertain ties. It? the field of signal detection with applications such as radar and medical images, sensitivity to signals of interest is also an objective, while robustness is concerned with undesirable interferences residing in unknown or partially known subspaces, and uncertain ties in the signal and learned interference models. In this review article, we present the similarities and differences in the game theoretic, stochastic, and geometric subspace formulations and solution approaches to robust detection in dynamic systems and in signal processing. We illustrate how innovative formulations are successfully addressing challenging problems by briefly describing results from two examples, one in functional Magnetic Resonance imaging (fMRI) of the brain, and the other in non-invasive image based classification of stem cells.

关键词： Filtering Geometric system theory Matched filters Robust Gausian and non-Gaussian detection and estimation Statistical signal processing Stem cell image analysis

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parallelism for imaging applications

Parallelism for imaging applications

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WESCON 1993 conference

作者： Battaglia, Michael P. MetaBasics Incorporated 7025 Corte Nina PleasantonCA94566 United States

ISBN: (纸本)0780399706

Numerous image processing functions involve repetitive operations and therefore can benefit from parallel processing, where performance may be significantly improved as a function of the number of processors applied to the task. One such application that requires processing to be as near to real-time as possible is vision processing and, in particular, low level vision processing. A system developed by the National Bureau of Standards (now known as the National Institute of Standards and Technology) and commercialized by Aspex Incorporated typifies the performance improvements which may be realized in vision processing through the use of parallelism. The Pipelined Image processing Engine (PIPE) is a coarse-grained parallel system ranging from three to eight stages. Using an innovative implementation of parallel hardware and software, the efficiency in executing complex imaging algorithms may be greatly improved. As will be seen, it is extremely useful in accelerating image processing functions in the areas of point processing, spatial processing, sequence processing and Boolean processing. These functions may be used for real-time or near real-time edge extraction, motion detection and disparity map generation. applications include inspection in food processing and electronics and textiles manufacture;robot and autonomous vehicle guidance;and research into image understanding, image processing and vision processing. © 1993 IEEE.

关键词： Pipeline processing systems

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