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International Conference on methods and Models in Automation and Robotics (MMAR)

作者： Przemyslaw Mazurek Department of Signal Processing and Multimedia Engineering West Pomeranian University of Technology Szczecin Poland

Dim objects tracking, like asteroids or satellites, require high performance algorithm. Track-Before-Detect (TBD) algorithms could be applied for such scenario, but the computation power is very demanding. Giant amount of trajectories that could be processed is the main problem. The application of GPGPU allows the computation using optimized Downsampled Spatio-Temporal TBD (ST-TBD) algorithm for small part of image sequences. Message Passing Interface (MPICH2) is applied for the image transmission and state-space exchange between computers and GPGPUs. Analysis of the system with multiple processing computers, for bandwidth limited network, is provided. The system could be limited by the bandwidth of network interface, not by GPGPU, quite often. The derived formula for this system allows the design with best optimization of GPGPUs.

关键词： Vectors Trajectory Signal processing algorithms Computers Tracking Radar tracking Algorithm design and analysis

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parallel training and testing methods for complex image processing algorithms on distributed, heterogeneous, unreliable, and non-dedicated resources

Parallel training and testing methods for complex image proc...

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Conference on parallel processing for Imaging Applications

作者： Usamentiaga, Ruben Garcia, Daniel F. Molleda, Julio Sainz, Ignacio Bulnes, Francisco G. Univ Oviedo Dept Comp Sci Gijon 33204 Asturias Spain

ISBN: (纸本)9780819484093

Advances in the image processing field have brought new methods which are able to perform complex tasks robustly. However, in order to meet constraints on functionality and reliability, imaging application developers often design complex algorithms with many parameters which must be finely tuned for each particular environment. The best approach for tuning these algorithms is to use an automatic training method, but the computational cost of this kind of training method is prohibitive, making it inviable even in powerful machines. The same problem arises when designing testing procedures. This work presents methods to train and test complex image processing algorithms in parallel execution environments. The approach proposed in this work is to use existing resources in offices or laboratories, rather than expensive clusters. These resources are typically non-dedicated, heterogeneous and unreliable. The proposed methods have been designed to deal with all these issues. Two methods are proposed: intelligent training based on genetic algorithms and PVM, and a full factorial design based on grid computing which can be used for training or testing. These methods are capable of harnessing the available computational power resources, giving more work to more powerful machines, while taking its unreliable nature into account. Both methods have been tested using real applications.

关键词： parallelization on unreliable non-dedicated resources parallel training parallel genetic algorithms image processing based on grid computing

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A piecewise-based contrast enhancement framework for low lighting video

A piecewise-based contrast enhancement framework for low lig...

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International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)

作者： Dongsheng Wang Xin Niu Yong Dou National Laboratory for Parallel & Distributed Processing National University of Defense Technology Changsha China

In this paper, we propose an efficient automatic contrast enhancement algorithm for low lighting video. The algorithm is based on a piecewise stretch on the brightness component extracted with Retinex theory in HSV space to improve the visuality of the image. By dividing the brightness component into dark and bright part, nonlinear transformations with different distribution assumption were performed respectively. All the model parameters were estimated automatically according to the illumination conditions. We use two methods to estimate the brightness. The one is global illumination estimation and the other is local illumination estimation. In comparison with global estimation, a local illumination estimation method is proposed for the further improvement. Experiments show that the algorithm can achieve satisfactory effect for nighttime image or video enhancement by comparing with some state-of-the-art approaches.

关键词： Brightness Lighting Estimation Histograms Transforms Real-time systems image color analysis

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parallel approaches to machine learning - A comprehensive survey

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JOURNAL OF parallel AND distributed COMPUTING 2013年第3期73卷 284-292页

作者： Upadhyaya, Sujatha R. Infosys Technol Bangalore Karnataka India

Literature has always witnessed efforts that make use of parallel algorithms / parallel architecture to improve performance;machine learning space is no exception. In fact, a considerable effort has gone into this area in the past fifteen years. Our report attempts to bring together and consolidate such attempts. It tracks the development in this area since the inception of the idea in 1995, identifies different phases during the time period 1995-2011 and marks important achievements. When it comes to performance enhancement, GPU platforms have carved a special niche for themselves. The strength of these platforms comes from the capability to speed up computations exponentially by way of parallel architecture / programming methods. While it is evident that computationally complex processes like image processing, gaming etc. stand to gain much from parallel architectures;studies suggest that general purpose tasks such as machine learning, graph traversal, and finite state machines are also identified as the parallel applications of the future. Map reduce is another important technique that has evolved during this period and as the literature has it, it has been proved to be an important aid in delivering performance of machine learning algorithms on GPUs. The report summarily presents the path of developments. (C) 2012 Elsevier Inc. All rights reserved.

关键词： distributed and parallel machine learning GPU Map reduce

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A rapid fluorescence lifetime image acquistion method based on time-gated fluorescence lifetime imaging microscopy

A rapid fluorescence lifetime image acquistion method based ...

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International Conference on Systems and Informatics (ICSAI)

作者： Yang Liu Yan Zhou Yuliang Liu Optoelectronics System Laboratory Institute of Semiconductors Chinese Academy of Science Beijing China

ISBN: (纸本)9781479954599

Besides the regular biological or biomedical imaging applications, time-gated fluorescence lifetime imaging microscopy (FLIM) could be used to image living cells or rapid biochemistry reactions by its advantage of wide field and obvious parallel acquisition speed. As the basis of system detection mode, a good fitting algorithm could satisfy the lifetime precision and acquisition demand at the same time. The common used fluorescence lifetime fitting methods include rapid fluorescence lifetime determination(RLD), linear least square method (LLS)and iterative nonlinear least square (NLLS)method. In this study, we compared these three methods and analyzed the lifetime results. It demonstrated the RLD algorithm could achieve a fast processing speed and a good contrast of lifetime map, however, it shows the results of the rapid fitted lifetime map exists some distributed noise caused by insufficient time-resolved fluorescence intensity images. To remove these noise spots and preserve the lifetime map precision, we chose a four-order partial differential equations (PDEs), which achieved a trade-off between noise-removing and lifetime map feature. In this paper, we proposed a rapid fluorescence lifetime acquisition method combining rapid lifetime determination and four-order PDEs algorithm based on time-gated FLIM, which could satisfy the high speed detection and lifetime precision demand of living cells or rapid biochemical reactions.

关键词： Fluorescence Noise Fitting Logic gates Biomedical imaging Least squares methods

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Using Clusters in Undergraduate Research: distributed Animation Rendering, Photo processing, and image Transcoding

Using Clusters in Undergraduate Research: Distributed Animat...

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15th IEEE International Conference on Cluster Computing (CLUSTER)

作者： Bui, Peter Boettcher, Travis Jaeger, Nicholas Westphal, Jeffrey Univ Wisconsin Dept Comp Sci Eau Claire WI 54702 USA

ISBN: (纸本)9781479908981

With distributed and parallel computing becoming increasingly important in both industrial and scientific endeavors, it is imperative that students are introduced to the challenges and methods of high performance and high throughput computing. Because these topics are often absent in standard undergraduate computer science curriculums, it is necessary to encourage and support independent research and study involving distributed and parallel computing. In this paper, we present three undergraduate research projects that utilize distributed computing clusters: animation rendering, photo processing, and image transcoding. We describe the challenges faced in each project, examine the solutions developed by the students, and then evaluate the performance and behavior of each system. At the end, we reflect on our experience using distributed clusters in undergraduate research and offer six general guidelines for mentoring and pursing distributed computing research projects with undergraduates. Overall, these projects effectively promote skills in high performance and high throughput computing while enhancing the undergraduate educational experience.

关键词： computer animation computer science education image coding parallel processing rendering (computer graphics) research and development

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distributed and hardware accelerated computing for clinical medical imaging using proton computed tomography (pCT)

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JOURNAL OF parallel AND distributed COMPUTING 2013年第12期73卷 1605-1612页

作者： Karonis, Nicholas T. Duffin, Kirk L. Ordonez, Caesar E. Erdelyi, Bela Uram, Thomas D. Olson, Eric C. Coutrakon, George Papka, Michael E. No Illinois Univ Dept Comp Sci De Kalb IL 60115 USA No Illinois Univ Dept Phys De Kalb IL 60115 USA Argonne Natl Lab Argonne Leadership Comp Facil Argonne IL 60439 USA Argonne Natl Lab Math & Comp Sci Div Argonne IL 60439 USA Argonne Natl Lab Div Phys Argonne IL 60439 USA Univ Chicago Computat Inst Chicago IL 60637 USA

Proton computed tomography (pCT) is an imaging modality that has been in development to support targeted dose delivery in proton therapy. It aims to accurately map the distribution of relative stopping power. Because protons traverse material media in non-linear paths, pCT requires individual proton processing. image reconstruction then becomes a time-consuming process. Clinical-use scenarios that require images from billions of protons in less than ten or fifteen minutes have motivated us to use distributed and hardware-accelerated computing methods to achieve fast image reconstruction. Combined use of MPI and GPUs demonstrates that clinically viable image reconstruction is possible. On a 60-node CPU/GPU computer cluster, we achieved efficient strong and weak scaling when reconstructing images from two billion histories in under seven minutes. This represents a significant improvement over the previous state-of-the-art in pCT, which took almost seventy minutes to reconstruct an image from 131 million histories on a single-CPU, single-GPU computer. (C) 2013 Elsevier Inc. All rights reserved.

关键词： Computed tomography Proton computed tomography image reconstruction Iterative reconstruction Relative stopping power RSP MLP GPU PCT CUDA MPI Scaling

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Rapid processing of remote sensing images based on cloud computing

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF GRID COMPUTING AND ESCIENCE 2013年第8期29卷 1963-1968页

作者： Wang, Pengyao Wang, Jianqin Chen, Ying Ni, Guangyuan China Agr Univ Beijing 100083 Peoples R China Beijing SINOPERM Technol Co Ltd Beijing 100083 Peoples R China

The rapid processing of remote sensing (RS) images is essential in many large-scale real-time monitoring, such as meteorological monitoring and natural disaster warning. However, the computation cost of RS is often expensive, traditional RS processing methods cannot satisfy the time requirement of dynamic monitoring. Fortunately, cloud computing not only provides an effective service for data management, but also offers a convenient way to execute RS computing, It is necessary to integrate the rapid RS processing services in a unified cloud computing architecture. The architecture can provide users with integrated rapid RS image processing service through effective huge data management and distributed parallel processing. This paper explores rapid processing methods and strategies for RS images based on cloud computing. In order to compare with other computing paradigms, we choose the maximum likelihood classification (MLC) as our experimental algorithm and Mahalanobis distance clustering (MDC) as our verifying algorithm to execute comparing. In these experiments, we compare the computation cost of RS processing in three computing paradigms (stand-alone, MPI, and MapReduce). From the intensive experimental results, we find that the RS processing based on cloud computing performs best from the aspects of programming convenience, data management and computational efficiency simultaneously, especially when processing huge amount of data. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Remote sensing image processing Cloud computing Timeliness

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Correntropy induced metric based graph regularized non-negative matrix factorization

Correntropy induced metric based graph regularized non-negat...

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International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)

作者： Bin Mao Naiyang Guan Dacheng Tao Xuhui Huang Zhigang Luo College of Science National University of Defense Technology Changsha China College of Computer National University of Defense Technology Science and Technology on Parallel and Distributed Processing Laboratory Changsha China Department of Computer Science and Technology College of Computer National University of Defense Technology Changsha China Faculty of Engineering and Information Technology University of Technology Sydney Sydney NSW Australia

Non-negative matrix factorization (NMF) is an efficient dimension reduction method and plays an important role in many pattern recognition and computer vision tasks. However, conventional NMF methods are not robust since the objective functions are sensitive to outliers and do not consider the geometric structure in datasets. In this paper, we proposed a correntropy graph regularized NMF (CGNMF) to overcome the aforementioned problems. CGNMF maximizes the correntropy between data matrix and its reconstruction to filter out the noises of large magnitudes, and expects the coefficients to preserve the intrinsic geometric structure of data. We also proposed a modified version of our CGNMF which construct the adjacent graph by using sparse representation to enhance its reliability. Experimental results on popular image datasets confirm the effectiveness of CGNMF.

关键词： Robustness Vectors Clustering algorithms Mutual information Measurement Computer integrated manufacturing

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Computing resultants on Graphics processing Units: Towards GPU-accelerated computer algebra

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JOURNAL OF parallel AND distributed COMPUTING 2013年第11期73卷 1494-1505页

作者： Emeliyanenko, Pavel Max Planck Inst Informat D-66123 Saarbrucken Germany

In this article we report on our experience in computing resultants of bivariate polynomials on Graphics processing Units (GPU). Following the outline of Collins' modular approach [6], our algorithm starts by mapping the input polynomials to a finite field for sufficiently many primes m. Next, the GPU algorithm evaluates the polynomials at a number of fixed points x is an element of Z(m), and computes a set of univariate resultants for each modular image. Afterwards, the resultant is reconstructed using polynomial interpolation and Chinese remaindering. The GPU returns resultant coefficients in the form of Mixed Radix (MR) digits. Finally, large integer coefficients are recovered from the MR representation on the CPU. All computations performed by the algorithm (except for, partly, Chinese remaindering) are outsourced to the graphics processor thereby minimizing the amount of work to be done on the host machine. The main theoretical contribution of this work is the modification of Collins' modular algorithm using the methods of matrix algebra to make an efficient realization on the GPU feasible. According to the benchmarks, our algorithm outperforms a CPU-based resultant algorithm from 64-bit Maple 14 by a factor of 100. (C) 2012 Elsevier Inc. All rights reserved.

关键词： Symbolic algorithms Resultants Modular computations GPU CUDA parallel computing

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