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Massively parallel computing techniques might improve highway maintenance

IEEE CONCURRENCY 1998年第1期6卷 58-+页

作者： Huang, YG Tillotson, H Snaith, M Univ Birmingham Sch Civil Engn Birmingham B15 2TT W Midlands England

Manual methods of measuring defects in roads show poor repeatability and reproducibility. Cracking is a principle indicator of defect progression in a road pavements, and the authors' overall objective is to develop a practical automatic, repeatable, and reproducible method of determining the extent of cracking. Their research aims at using a distributed array of processors to achieve practical speeds for processing digitized images of road surfaces to detect cracks. The algorithms described here provide for two processes. The first converts a gray-scale image into a binary image that represents most of the cracks and eliminates most of the noise from the surface texture. This initial screening process might suffice for the bulk of a road having few cracks. The second process combines the crack fragments in the binary image into continous cracks and gives the highway engineer an appropriate output. The article includes results in which individual images were judged to contain cracks or not contain cracks by eight independent observers and by processing on the DAP to the end of the initial screening process. The authors have found that single images can be processed to the initial screening stage in the 40-millisecond limit for real-time processing provided by the British TV standard.

关键词： parallel processing Road transportation Velocity measurement TV Reproducibility of results Surface cracks Pixel Cameras Resource management Data engineering

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GPU Implementation of Bitplane Coding with parallel Coefficient processing for High Performance image Compression

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IEEE TRANSACTIONS ON parallel AND distributed SYSTEMS 2017年第8期28卷 2272-2284页

作者： Enfedaque, Pablo Auli-Llinas, Francesc Moure, Juan Carlos Univ Autonoma Barcelona Dept Informat & Commun Engn E-08193 Barcelona Spain Univ Autonoma Barcelona Dept Comp Architecture & Operating Syst E-08193 Barcelona Spain

The fast compression of images is a requisite in many applications like TV production, teleconferencing, or digital cinema. Many of the algorithms employed in current image compression standards are inherently sequential. High performance implementations of such algorithms often require specialized hardware like field integrated gate arrays. Graphics processing Units (GPUs) do not commonly achieve high performance on these algorithms because they do not exhibit fine-grain parallelism. Our previous work introduced a new core algorithm for wavelet-based image coding systems. It is tailored for massive parallel architectures. It is called bitplane coding with parallel coefficient processing (BPC-PaCo). This paper introduces the first high performance, GPU-based implementation of BPC-PaCo. A detailed analysis of the algorithm aids its implementation in the GPU. The main insights behind the proposed codec are an efficient thread-to-data mapping, a smart memory management, and the use of efficient cooperation mechanisms to enable inter-thread communication. Experimental results indicate that the proposed implementation matches the requirements for high resolution (4 K) digital cinema in real time, yielding speedups of 30x with respect to the fastest implementations of current compression standards. Also, a power consumption evaluation shows that our implementation consumes 40x less energy for equivalent performance than state-of-the-art methods.

关键词： image coding SIMD computing graphics processing unit (GPU) compute unified device architecture (CUDA)

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NPU-based image compositing in a distributed visualization system

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IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2007年第4期13卷 798-809页

作者： Pugmire, David Monroe, Laura Davenport, Carolyn Connor DuBois, Andrew DuBois, David Poole, Stephen Los Alamos Natl Lab Los Alamos NM 87545 USA Oak Ridge Natl Lab Oak Ridge TN 37831 USA

This paper describes the first use of a Network processing Unit (NPU) to perform hardware-based image composition in a distributed rendering system. The image composition step is a notorious bottleneck in a clustered rendering system. Furthermore, image compositing algorithms do not necessarily scale as data size and number of nodes increase. Previous researchers have addressed the composition problem via software and/or custom-built hardware. We used the heterogeneous multicore computation architecture of the Intel IXP28XX NPU, a fully programmable commercial off-the-shelf (COTS) technology, to perform the image composition step. With this design, we have attained a nearly four-times performance increase over traditional software-based compositing methods, achieving sustained compositing rates of 22-28 fps on a 1, 024 x 1, 024 image. This system is fully scalable with a negligible penalty in frame rate, is entirely COTS, and is flexible with regard to operating system, rendering software, graphics cards, and node architecture. The NPU-based compositor has the additional advantage of being a modular compositing component that is eminently suitable for integration into existing distributed software visualization packages.

关键词： hardware-assisted image compositing high-performance computing image compositing Network processing Unit parallel rendering PC clusters visualization volume rendering

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High-performance thresholding with adaptive equalisation

High-performance thresholding with adaptive equalisation

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SPIE Conference on parallel and distributed methods for image processing II

作者： Lam, KP Univ Keele Dept Comp Sci Keele ST5 5BG Staffs England

ISBN: (纸本)0819429074

The ability to simplify an image whilst retaining such crucial information as shapes and geometric structures is of great important for real-time image analysis applications. Here the technique of binary thresholding which reduces the image complexity has generally been regarded as one of the most valuable methods, primarily owing to its ease of design and analysis. This paper studies the state of developments in the field, and describes a radically different approach of adaptive thresholding. The latter employs the analytical technique of histogram normalisation for facilitating an optimal 'contrast level' of the image under consideration. A suitable criterion is also developed to determine the applicability of the adaptive processing procedure. In terms of performance and computational complexity, the proposed algorithm compares favourably to five established image thresholding methods selected for this study. Experimental results have shown that the new algorithm outperforms these methods in terms of a number of important errors measures, including a consistently low visual classification error performance. The simplicity of design of the algorithm also lends itself to efficient parallel implementations.

关键词： segmentation dynamic thresholding histogram equalisation image parallelism

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parallel deconvolution methods for three dimensional image restoration

Parallel deconvolution methods for three dimensional image r...

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Conference on Advanced Signal processing Algorithms Architectures and Implementations XIII

作者： Lewis, B Reichel, L LLC Rocketcalc Richfield OH 44286 USA

Restoration by deconvolution of three-dimensional images that have been contaminated by noise and spatially invariant blur is computationally demanding. We describe efficient parallel implementations of iterative meth... 详细信息

ISBN: (纸本)0819450782

关键词： image restoration ill-posed problem Krylov iterative methods parallel computation

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Efficient parallel algorithms for hierarchical clustering on arrays with reconfigurable optical buses

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JOURNAL OF parallel AND distributed COMPUTING 2000年第9期60卷 1137-1153页

作者： Wu, CH Horng, SJ Tsai, HR Natl Taiwan Univ Sci & Technol Dept Elect Engn Taipei Taiwan Chinese Naval Acad Dept Informat Management Kaohsiung Taiwan Ling Tung Coll Dept Informat Management Taichung Taiwan

Clustering is a basic operation in image processing and computer vision, and it plays an important role in unsupervised pattern recognition and image segmentation. While there are many methods for clustering, the single-link hierarchical clustering is one of the most popular techniques. In this paper, with the advantages of both optical transmission and electronic computation, we design efficient parallel hierarchical clustering algorithms on the arrays with reconfigurable optical buses (AROB). We first design three efficient basic operations which include the matrix multiplication of two N x N matrices, finding the minimum spanning tree of a graph with N vertices, and identifying the connected component containing a specified vertex. Based on these three data operations, an O(log N) time parallel hierarchical clustering algorithm is proposed using N-3 processors. Furthermore, if the connectivity of the AROB with four-port connection is allowed, two constant time clustering algorithms can be also derived using N-4 and N-3 processors, respectively. These results improve on previously known algorithms developed on various parallel computational models. (C) 2000 Academic Press.

关键词： cluster analysis hierarchical clustering image processing pattern recognition parallel algorithm arrays with reconfigurable optical buses (AROB)

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parallel DATA RESAMPLING AND FOURIER INVERSION BY THE SCAN-LINE METHOD

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IEEE TRANSACTIONS ON MEDICAL IMAGING 1995年第3期14卷 454-463页

作者： NOLL, DC WEBB, JA WARFEL, TE CARNEGIE MELLON UNIV SCH COMP SCIPITTSBURGHPA 15213 CARNEGIE MELLON UNIV DEPT ELECT & COMP ENGNPITTSBURGHPA 15213

Fourier inversion is an efficient method for image reconstruction in a variety of applications, for example, in computed tomography and magnetic resonance imaging. Fourier inversion normally consists of two steps, interpolation of data onto a rectilinear grid, if necessary, and inverse Fourier transformation, This paper presents interpolation by the scan-line method, in which the interpolation algorithm is implemented in a form consisting only of row operations and data transposes, The two-dimensional inverse Fourier transformation can also be implemented with only row operations and data transposes, Accordingly, Fourier inversion can easily be implemented on a parallel computer that supports row operations and data transposes on row distributed data The conditions under which the scan-line implementations are algorithmically equivalent to the original serial computer implementation are described and methods for improving accuracy outside of those conditions are presented, The scan-line algorithm is implemented on the iWarp parallel computer using the Adapt language for parallel image processing. This implementation is applied to magnetic resonance data acquired along radial-lines and spiral trajectories through Fourier transform space.

关键词： Resampling Interpolation parallel computers image reconstruction Fourier Interpolation Arithmetic Fourier Transform method of lines Inversion

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parallel robust relaxation algorithm for automatic stereo analysis

Parallel robust relaxation algorithm for automatic stereo an...

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SPIE Conference on parallel and distributed methods for image processing II

作者： Palaniappan, K Vass, J Zhuang, XH Univ Missouri Dept Comp Engn & Comp Sci Multimedia Commun & Visualizat Lab Columbia MO 65211 USA

ISBN: (纸本)0819429074

A parallel robust relaxation algorithm is proposed to improve the detection and correction of illegal disparities encountered in the automatic stereo analysis (ASA) algorithm. Outliers and noisy matches from correlation-based BSA matching are improved by relaxation labeling and robust statistical methods at each stage of the multiresolution coarse-to-fine analysis. A parallel version of the relaxation labeling algorithm has been implemented for the MasPar supercomputer. The performance scales quite linearly with the number of processing elements and scales better than linear with increasing work load. The algorithm is highly scalable both as the number of processors are increased for solving a fixed size problem and also as the size of the problem increases.

关键词： parallel stereo correlation matching multiresolution algorithms parallel relaxation labeling robust estimation

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Implementation of the morphological shared-weight neural network (MSNN) for target recognition on the parallel Algebraic Logic (PAL) computer

Implementation of the morphological shared-weight neural net...

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SPIE Conference on parallel and distributed methods for image processing II

作者： Li, HZ Shi, HC Gader, P Keller, J Univ Missouri Dept Comp Engn & Comp Sci Columbia MO 65211 USA

ISBN: (纸本)0819429074

The morphological shared-weight neural network (MSNN) is an effective approach to automatic target recognition. Implementation of the network in parallel is critical for real-time target recognition systems. Although there is significant parallelism inherent in the MSNN, it is a challenge to implement it on an SIMD parallel computer consisting of a large array of simple processing elements. This paper discusses issues related to detection accuracy and throughput in implementing the MSNN on the parallel Algebraic Logic (PAL) computer.

关键词： target recognition neural network morphology SIMD processor array

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distributed computing in image analysis using open source frameworks and application to image sharpness assessment of histological whole slide images

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DIAGNOSTIC PATHOLOGY 2011年第Suppl_sup期6卷 S16-S16页

作者： Zerbe, Norman Hufnagl, Peter Schluens, Karsten Charite Inst Pathol D-10117 Berlin Germany Univ Appl Sci Berlin D-12459 Berlin Germany

Background: Automated image analysis on virtual slides is evolving rapidly and will play an important role in the future of digital pathology. Due to the image size, the computational cost of processing whole slide images (WSIs) in full resolution is immense. Moreover, image analysis requires well focused images in high magnification. methods: We present a system that merges virtual microscopy techniques, open source image analysis software, and distributed parallel processing. We have integrated the parallel processing framework JPPF, so batch processing can be performed distributed and in parallel. All resulting meta data and image data are collected and merged. As an example the system is applied to the specific task of image sharpness assessment. imageJ is an open source image editing and processing framework developed at the NIH having a large user community that contributes image processing algorithms wrapped as plug-ins in a wide field of life science applications. We developed an imageJ plug-in that supports both basic interactive virtual microscope and batch processing functionality. For the application of sharpness inspection we employ an approach with non-overlapping tiles. Compute nodes retrieve image tiles of moderate size from the streaming server and compute the focus measure. Each tile is divided into small sub images to calculate an edge based sharpness criterion which is used for classification. The results are aggregated in a sharpness map. Results: Based on the system we calculate a sharpness measure and classify virtual slides into one of the following categories - excellent, okay, review and defective. Generating a scaled sharpness map enables the user to evaluate sharpness of WSIs and shows overall quality at a glance thus reducing tedious assessment work. Conclusions: Using sharpness assessment as an example, the introduced system can be used to process, analyze and parallelize analysis of whole slide images based on open source software.

关键词： Virtual Slide Focus Measure Virtual Microscopy Stream Server Slide image

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