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IEEE International Symposium on parallel and distributed processing with Applications Workshops (ISPAW)

作者： J. Hossein-Khani H. Soltanian-Zadeh M. Kamarei O. Staadt CIPCE Department of Electrical and Computer Engineering University of Tehran Tehran Iran Faculty of Computer Science University of Rostock Rostock Germany

In the present work, automatic corner detection in soccer games based on image features (e.g., object-based features) has been studied. For this purpose, a framework has been proposed that consists of five steps. This paper mainly focuses on the first three steps and specially ball detection step. Ball position on the field plays an important role in determining which event has occurred in the game. Therefore, it is necessary to detect exact position of the ball in the playfield and then track it. Ball trajectory that can be obtained via tracking is useful for identifying and detecting main events of soccer games. In these three steps, the most important processing that has been applied to the images is based on image segmentation to detect playfield, field lines, and ball. Cleaning morphological method is applied to detect the ball. This method is real-time and automatic, it yields superior results in comparison with other common methods such as Template Matching and Circular Hough Transform (CHT). By applying the proposed method, only one candidate for the ball position is obtained and non-ball candidates are removed, hence, it is more reliable than other methods. The results of the proposed method are compared with those of CHT. They illustrate that the proposed method is fast, effective, and reliable.

关键词： image color analysis Streaming media Feature extraction Shape Games Tracking Pixel

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Tracking Structure of Streaming Social Networks

Tracking Structure of Streaming Social Networks

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IEEE International Symposium on parallel and distributed processing Workshops and Phd Forum (IPDPSW)

作者： David Ediger Jason Riedy David A. Bader Henning Meyerhenke College of Computing Georgia Institute of Technology Atlanta GA USA

Current online social networks are massive and still growing. For example, Face book has over 500 million active users sharing over 30 billion items per month. The scale within these data streams has outstripped traditional graph analysis methods. Real-time monitoring for anomalies may require dynamic analysis rather than repeated static analysis. The massive state behind multiple persistent queries requires shared data structures and flexible representations. We present a framework based on the STINGER data structure that can monitor a global property, connected components, on a graph of 16 million vertices at rates of up to 240,000 updates per second on 32 processors of a Cray XMT. For very large scale-free graphs, our implementation uses novel batching techniques that exploit the scale-free nature of the data and run over three times faster than prior methods. Our framework handles, for the first time, real-world data rates, opening the door to higher-level analytics such as community and anomaly detection.

关键词： Arrays Program processors Facebook image color analysis Hardware

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Phase Map Generation for Phase Shift Moire Using CUDA

Phase Map Generation for Phase Shift Moire Using CUDA

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IEEE International Conference on parallel and distributed Computing, Applications and Technologies (PDCAT)

作者： Sudhakar Sah YoungJun Roh KyoungSeop Chang DaeHwa Jeong Center of Research in Engr Sciences and Tech-CREST KPIT Cummins Infosystems Limited Pune India FPG Productivity Research Institute LG Electronics Inc. South Korea

Phase shift Moiré is a very popular and one of the most successful techniques for shape measurement of 3-D objects such as PCB (printed circuit board), TFT (thin film transistor), LCD (Liquid crystal display) etc. Various implementations of phase shift moiré are available for improving accuracy and/or speed. Although, these methods contribute a lot in reducing the computation with some compromise in accuracy, there is a lot of scope of improving the performance of these algorithms with increased accuracy, especially when specialized hardware like GPU is available. GPU contains many core or processing elements that can process the same work concurrently resulting in dramatic increase in performance. In this paper, we propose the parallel implementation of the phase shift moiré method on CUDA. A novel method called image stacking method is proposed that can also be used for CUDA implementation of similar algorithms to improve performance. Using this technique, we are able to execute the application 180 times faster compared to the CPU implementation.

关键词： Graphics processing unit Instruction sets Kernel Three dimensional displays Computer architecture Pipeline processing Stacking

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parallel biomedical image processing with GPGPUs in cancer research

Parallel biomedical image processing with GPGPUs in cancer r...

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International Symposium on Logistics and Industrial Informatics, LINDI

作者： Attila Reményi Sándor Szénási István Bándi Zoltán Vámossy Gábor Valcz Pál Bogdanov Szabolcs Sergyán Miklos Kozlovszky John von Neumann Faculty of Informatics Óbuda University Budapest Hungary 2nd Department of Internal Medicine Semmelweiss University Budapest Hungary Laboratory of Parallel and Distributed Computing MTA SZTAKI Budapest Hungary

The main aim of this work is to show, how GPGPUs can facilitate certain type of image processing methods. The software used in this paper is used to detect special tissue part, the nuclei on (HE - hematoxilin eosin) stained colon tissue sample images. Since pathologists are working with large number of high resolution images - thus require significant storage space -, one feasible way to achieve reasonable processing time is the usage of GPGPUs. The CUDA software development kit was used to develop processing algorithms to NVIDIA type GPUs. Our work focuses on how to achieve better performance with coalesced global memory access when working with three-channel RGB tissue images, and how to use the on-die shared memory efficiently.

关键词： Graphics processing unit Instruction sets image processing Memory management Optimization Kernel Colon

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An ethological motion capture system

An ethological motion capture system

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International Symposium on Computational Intelligence and Informatics

作者： Viktor Devecseri Antal Dóka József Molnár Péter Tamás Department of Mechatronics Optics and Information Engineering Budapest University of Technology and Economics Budapest Hungary Faculty of Sciences Biological Departments / Department of Ethology Eötvös Loránd University Budapest Hungary

The motion of living beings can be recorded with motion capture systems. The recorded data can be used to animate virtual characters. There are many methods used by motion capture systems. The paper presents the development of a motion capture system for more than one performer with active optical markers. The performers wear homogenous, illuminating markers attached to each relevant joint. Movements are captured by 6 high speed cameras in order to determine the markers' position in the cameras' image. The cameras operate at 60 fps. The image processing is distributed among multiple computers. Luckily most of the image processing tasks are highly parallel, so the high parallel computational capacity of modern programmable GPUs can be exploited. The main computer calculates the markers' 3D positions after it receives the 2D data from image processing. Each marker needs to be identified, so the markers' 2D and 3D positions are tracked. A hierarchy can be defined between the points in the point cloud, which can be used to determine the bones' hierarchical transformation chain in the characters' skeleton. To achieve satisfying results precise calibration is required.

关键词： Cameras Three dimensional displays Computers Calibration Tracking image processing Laboratories

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Acceleration of Two Point Correlation Function Calculation for Pathology image Segmentation

Acceleration of Two Point Correlation Function Calculation f...

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IEEE International Conference on Healthcare Informatics, Imaging and Systems Biology (HISB)

作者： Lee A.D. Cooper Joel H. Saltz Umit Catalyurek Kun Huang Center of Comprehensive Informatics Emory University Atlanta GA USA Department of Biomedical Informatics Ohio State University Columbus OH USA

The segmentation of tissue regions in high-resolution microscopy is a challenging problem due to both the size and appearance of digitized pathology sections. The two point correlation function (TPCF) has proved to be an effective feature to address the textural appearance of tissues. However the calculation of the TPCF functions is computationally burdensome and often intractable in the gigapixel images produced by slide scanning devices for pathology application. In this paper we present several approaches for accelerating deterministic calculation of point correlation functions using theory to reduce computation, parallelization on distributed systems, and parallelization on graphics processors. Previously we show that the correlation updating method of calculation offers an 8-35x speedup over frequency domain methods and decouples efficient computation from the select scales of Fourier methods. In this paper, using distributed computation on 64 compute nodes provides a further 42x speedup. Finally, parallelization on graphics processors (GPU) results in an additional 11-16x speedup using an implementation capable of running on a single desktop machine.

关键词： Correlation Graphics processing unit parallel processing Instruction sets image segmentation Interpolation

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Biomedical image processing with GPGPU using CUDA

Biomedical image processing with GPGPU using CUDA

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Proceedings of the International Convention MIPRO

作者： A. R. Firstauthor I. B. Secondauthor G. V. Thirdauthor P. B. Fourthauthor M. K. Fifthauthor Óbuda University/John von Neumann Faculty of Informatics Budapest Hungary Semmelweiss University/Znd Department of Internal Medicine Budapest Hungary MTA SZTAKI/Laboratory of Parallel and Distributed Computing Budapest Hungary

The main aim of this work is to show, how the GPGPUs can be used to speed up certain image processing methods. The algorithm explained in this paper is used to detect nuclei on (HE - hematoxilin eosin) stained colon tissue sample images, and includes a Gauss blurring, an RGB-HSV color space conversion, a fixed binarization, an ultimate erode procedure and a local maximum search. Since the images retrieved from the digital slides require significant storage space (up to few hundred megapixels), the usage of GPGPUs to speed up image processing operations is necessary in the interest of achieving reasonable processing time. The CUDA software development kit was used to develop algorithms to GPUs made by NVIDIA. This work focuses on how to achieve coalesced global memory access when working with three-channel RGB images, and how to use the on-die shared memory efficiently. The exact test algorithm also included a linear connected component labeling, which was running on the CPU, and with iterative optimization of the GPU code, we managed to achieve significant speed up in well defined test environment.

关键词： Graphics processing unit Instruction sets image processing Kernel Optimization Memory management Pixel

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First On-Site Data Analysis System for Subaru/Suprime-Cam

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PUBLICATIONS OF THE ASTRONOMICAL SOCIETY OF JAPAN 2011年第sp2期63卷 S585-S603页

作者： Furusawa, Hisanori Okura, Yuki Mineo, Sogo Takata, Tadafumi Nakata, Fumiaki Tanaka, Manobu Katayama, Nobuhiko Itoh, Ryosuke Yasuda, Naoki Miyazaki, Satoshi Komiyama, Yutaka Utsumi, Yousuke Uchida, Tomohisa Aihara, Hiroaki Natl Astron Observ Japan Tokyo 1818858 Japan Univ Tokyo Sch Sci Dept Phys Bunkyo Ku Tokyo 1330033 Japan Natl Astron Observ Japan Subaru Telescope Hilo HI 96720 USA High Energy Accelerator Res Org KEK Inst Particle & Nucl Studies Ibaraki 3050801 Japan Univ Tokyo Univ IPMU Inst Phys & Math Kashiwa Chiba 2778583 Japan

We developed an automated on-site quick analysis system for mosaic CCD data of Suprime-Cam, which is a wide-field camera mounted at the prime focus of the Subaru Telescope, Mauna Kea, Hawaii. The first version of the data-analysis system was constructed;and started to operate in general observations. This system is a new function of observing support at the Subaru Telescope to provide the Subaru user community with an automated on-site data evaluation, aiming at improvements of observers' productivity, especially in large imaging surveys. The new system assists the data evaluation tasks in observations by the continuous monitoring of the characteristics of every data frame during observations. The evaluation results and data frames processed by this system are also useful for reducing the data-processing time in a full analysis after an observation. The primary analysis functions implemented in the data-analysis system are composed of automated realtime analysis for data evaluation and on-demand analysis, which is executed upon request, including mosaicing analysis and flat making analysis. In data evaluation, which is controlled by the organizing software, the database keeps track of the analysis histories, as well as the evaluated values of data frames, including seeing and sky background levels;it also helps in the selection of frames for mosaicing and flat making analysis. We examined the system performance and confirmed an improvement in the data-processing time by a factor of 9 with the aid of distributed parallel data processing and on-memory data processing, which makes the automated data evaluation effective.

关键词： Astronomical data bases: miscellaneous methods: data analysis Techniques: image processing

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Exploring parallel capabilities of an innovative numerical method for recovering image velocity vectors field

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MATHEMATICAL AND COMPUTER MODELLING 2010年第3-4期51卷 138-143页

作者： Francomano, E. Macaluso, C. Tortorici, A. Toscano, E. Univ Palermo Dipartimento Ingn Informat I-90128 Palermo Italy

In this paper an efficient method devoted to estimate the velocity vectors field is investigated. The method is based on a quasi-interpolant operator and involves a large amount of computation. The operations characterizing the computational scheme are ideal for parallel processing because they are local, regular and repetitive. Therefore, the spatial parallelism of the process is studied to rapidly proceed in the computation on distributed multiprocessor systems. The process has shown to be synchoronous, with good task balancing and requiring a small amount of data transfer. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： image velocity vectors field Quasi-interpolant operator B-spline functions distributed multiprocessor systems

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Large-scale grid computing for content-based image retrieval

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ASLIB PROCEEDINGS 2010年第4-5期62卷 438-446页

作者： Town, Chris Harrison, Karl Univ Cambridge Comp Lab Cambridge CB2 3QG England Imense Ltd Cambridge England Univ Birmingham Sch Phys & Astron Birmingham W Midlands England

Purpose - Content-based image retrieval (CBIR) technologies offer many advantages over purely text-based image search. However, one of the drawbacks associated with CBIR is the increased computational cost arising from tasks such as image processing, feature extraction, image classification, and object detection and recognition. Consequently CBIR systems have suffered from a lack of scalability, which has greatly hampered their adoption for real-world public and commercial image search. At the same time, paradigms for large-scale heterogeneous distributed computing such as grid computing, cloud computing, and utility-based computing are gaining traction as a way of providing more scalable and efficient solutions to large-scale computing tasks. Design/methodology/approach - This paper presents an approach in which a large distributed processing grid has been used to apply a range of CBIR methods to a substantial number of images. By massively distributing the required computational task across thousands of grid nodes, very high through-put has been achieved at relatively low overheads. Findings - This has allowed one to analyse and index about 25 million high resolution images thus far, while using just two servers for storage and job submission. The CBIR system was developed by Imense Ltd and is based on automated analysis and recognition of image content using a semantic ontology. It features a range of image-processing and analysis modules, including image segmentation, region classification, scene analysis, object detection, and face recognition methods. Originality/value - In the case of content-based image analysis, the primary performance criterion is the overall through-put achieved by the system in terms of the number of images that can be processed over a given time frame, irrespective of the time taken to process any given image. As such, grid processing has great potential for massively parallel content-based image retrieval and other tasks with similar p

关键词： Data handling Pattern recognition Data analysis Virtual work

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