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19th International Conference on methods and Models in Automation and Robotics (MMAR)

作者： Mazurek, Przemyslaw West Pomeranian Univ Technol Szczecin Dept Signal Proc & Multimedia Engn PL-71126 Szczecin Poland

ISBN: (纸本)9781479950812

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： graphics processing units image sequences message passing object detection object tracking GPGPU MPICH2 bandwidth limited network dim object tracking downsampled spatio-temporal TBD algorithm general-purpose graphics processing unit image transmission message passing interface network interface spatio-temporal track-before-detect algorithm state-space exchange Algorithm design and analysis Computers Radar tracking Signal processing algorithms Tracking Trajectory Vectors image sequences image communication systems message passing Network interfaces Algorithm design and analysis Computers Signal processing algorithms trajectory Object detection radar target tracking tracking tracking objects Graphics processing Unit

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Correntropy Supervised Non-negative Matrix Factorization

Correntropy Supervised Non-negative Matrix Factorization

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International Joint Conference on Neural Networks

作者： Wenju Zhang Naiyang Guan Dacheng Tao Bin Mao Xuhui Huang Zhigang Luo Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology Changsha Hunan China 410073 Center for Quantum Computation and Intelligent Systems FEIT University of Technology Sydney Sydney NSW 2007 Australia College of Science National University of Defense Technology Changsha Hunan China 410073 Department of Computer Science and Technology College of Computer National University of Defense Technology Changsha Hunan China 410073

ISBN: (纸本)9781479919611

Non-negative matrix factorization (NMF) is a powerful dimension reduction method and has been widely used in many pattern recognition and computer vision problems. However, conventional NMF methods are neither robust enough as their loss functions are sensitive to outliers, nor discriminative because they completely ignore labels in a dataset. In this paper, we proposed a correntropy supervised NMF (CSNMF) to simultaneously overcome aforementioned deficiencies. In particular, CSNMF maximizes the correntropy between the data matrix and its reconstruction in low-dimensional space to inhibit outliers during learning the subspace, and narrows the minimizes the distances between coefficients of any two samples with the same class labels to enhance the subsequent classification performance. To solve CSNMF, we developed a multiplicative update rules and theoretically proved its convergence. Experimental results on popular face image datasets verify the effectiveness of CSNMF comparing with NMF, its supervised variants, and its robustified variants.

关键词： NMF subsequent classification NMF protocol matrix decomposition outliers Nonnegative LOSS FUNCTION images REDUCTION methods Dataset computer vision

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Efficient data partitioning for the GPU computation of moment functions

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JOURNAL OF parallel AND distributed COMPUTING 2014年第1期74卷 1994-2004页

作者： Jesus Martin Requena, Manuel Moscato, Pablo Ujaldon, Manuel Univ Malaga Comp Architecture Dept E-29071 Malaga Spain Hunter Med Res Inst Ctr Bioinformat Biomarker Discovery & Informat Ba Newcastle NSW Australia Univ Newcastle Sch Elect Engn & Comp Sci Callaghan NSW 2308 Australia

In our previous work, we have provided tools for an efficient characterization of biomedical images using Legendre and Zernike moments, showing their relevance as biomarkers for classifying image tiles coming from bone tissue regeneration studies (Ujaldon, 2009) [24]. As part of our research quest for efficiency, we developed methods for accelerating those computations on GPUs (Martin-Requena and Ujaldon, 2011) [10,9]. This new stage of our work focuses on the efficient data partitioning to optimize the execution on many-cores and clusters of GPUs to attain gains up to three orders of magnitude when compared to the execution on multi-core CPUs of similar age and cost using 1 Mpixel images. We deploy a successive and successful chain of optimizations which exploit symmetries in trigonometric functions and access patterns to image pixels which are effectively combined with massive data parallelism on GPUs to enable (1) real-time processing for our set of input biomedical images, and (2) the use of high-resolution images in clinical practice. (C) 2013 Elsevier Inc. All rights reserved.

关键词： Zernike moments image features High performance computing GPU Data partitioning

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Real-time reconstruction solution for positron emission mammography imaging-guided intervention

Real-time reconstruction solution for positron emission mamm...

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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： Xiaoyue Gu Lin Li Long Wei Mingkai Yun Wei Zhou Zhenrui Lu Leiming Shang Pengfei Ying Chinese Academy of Sciences Beijing Engineering Research Center of Radiographic Techniques and Equipment Beijing China

Positron Emission Mammography (PEM) imaging systems with the ability in detection of millimeter-sized tumors were developed in recent years. And some of them have been well used in clinical applications. In consideration of biopsy application, a double-plane PET is more practical than a ring detector for the convenience of breast immobilization. In this study, the possibility of the imaging-guided intervention under a double-plane PET was studied. The distance between the planes is changeable with different breast size. And the system matrix is computed in real-time for every plane spacing. The biopsy motivation under the double-plane PET requires a high sensitivity, great resolution performance and a quickly reconstruction speed. The sensitivity result shows the central value is 13.28% with the 6cm spacing and 200-750keV energy window. The resolution modeling method shows the resolution recovery achieved in the cross-plane. The 3D-OSEM algorithm is applied. The reconstruction is supported with GPU (GeForce GTX 660). For the GPU acceleration, both projection and back-projection procedures use the tube model. Each tube is distributed with a thread and calculated in parallel in the GPU. A double plane PET is simulated above Geant4 Application for Emission Tomography (GATE) software based on MC methods. With all the span tubes calculated, the GPU takes 19 seconds to reconstruct the image while the CPU takes 1037 seconds. The image quality was tested with different acquisition times. The series of biopsy images are showed. The point and rod source both could be imaged in the simulated biopsy process.

关键词： Graphics processing units image reconstruction Positron emission tomography Biopsy Energy resolution Real-time systems Breast

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基于集群的遥感并行处理技术浅析

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电光系统 2015年第3期 11-14页

作者：秦臻中国电子科技集团公司第二十七研究所郑州450047

为解决遥感影像处理过程中存在的计算密集型以及数据密集型问题，深入研究GPU集群的体系结构，遥感平台类型和遥感并行处理方法等。采用了基于CUDA的GPU并行运算平台和基于Hadoop的分布式并行运算平台，重点分析了几种遥感数据处理并行... 详细信息

为解决遥感影像处理过程中存在的计算密集型以及数据密集型问题，深入研究GPU集群的体系结构，遥感平台类型和遥感并行处理方法等。采用了基于CUDA的GPU并行运算平台和基于Hadoop的分布式并行运算平台，重点分析了几种遥感数据处理并行方法。最终得出基于集群的遥感并行方法较传统方法有着明显优势，并对遥感影像处理系统并行计算前景和研究热点问题进行了展望。

关键词：遥感并行集群大数据

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Proposal of Eye-gaze Recognize Method for Input Interface without Infra-red Ray Equipment 15

Proposal of Eye-gaze Recognize Method for Input Interface wi...

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15th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and parallel/distributed Computing (SNPD)

作者： Fukushima, Kazuki Shirahama, Naruki Kitakyushu Natl Coll Technol Dept Control Engn Adv Engn Kitakyushu Fukuoka Japan Kitakyushu Natl Coll Technol Dept Elect & Control Kitakyushu Fukuoka Japan

ISBN: (纸本)9781479956043

The main purpose of this study is to develop the inexpensive eye-gaze input interface for disabled people. The eye-gaze inputting can suit many situations, and it has little load for user because it is non-contact input interface. Many projects about the eye-gaze have been studied recently. Most of the productions of eye-gaze inputting use infra-red rays (IR) to detect the iris in the eyes. However the harm of IR for human eyes has been pointed out. In addition, if the interface requires the camera which has IR, the users must purchase the specific devices. Therefore we adopted a PC and the camera doesn't have the IR function. We propose the system by using the motion-template that is one of the functions of OpenCV library for tracking motion. However this function can't recognize the point where the user watches on monitor. It can recognize only the motion. That's why we made calibrate function to relate the eye-gaze with the monitor. We propose two methods to recognize eye-gaze. Both methods require the iris binary image. This image shows the iris shape and we expect that user's visual point can be calculated from this iris image. One method uses gravity point to calculate the point. Other method uses the rectangular approximation to calculate it. We did experiments for some subjects by both methods and compared the results to validate which method is proper and how much the calibrate function is accurate. In the experiment, the function randomly spotted a blue target on the monitor. The target position changes on a regular basis. In this experiment, the user stares at the target and we check the accuracy. If this function or methods are proper, the function correctly recognizes the user stare at target or near. For more accuracy, we will consider about how to detect the iris correctly in the future.

关键词： assistive technology eye-gaze web-camera image processing welfare support-equipment OpenCV

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image Classification Optimization of High Resolution Tissue images

Image Classification Optimization of High Resolution Tissue ...

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9th International Conference on Large-Scale Scientific Computations (LSSC)

作者： Kozlovszky, M. Hegedus, K. Windisch, G. Kovacs, L. Pinter, G. Obuda Univ John von Neumann Fac Informat Budapest Hungary

ISBN: (纸本)9783662438800;9783662438794

Generic image classification methods are not performing well on tissue images. Such software solutions are producing high number of false negative and positive results, which prevents their clinical usage. We have created the MorphCeck high resolution tissue image processing framework, which enables us to collect morphological and morphometrical parameter values of the examined tissues. Size of such tissue images can easily reach the order of 100 MB-1 GB. Therefore, the image processing speed and effectiveness is an important factor. Our main goal is to accurately evaluate high resolution H-E (hematoxilin-eozin) stained colon tissue sample images, and based on the parameters classify the images into differentiated sets according to the structure and the surface manifestation of the tissues. We have interfaced our MorphCheck tissue image measurement software framework with the WND-CHARM general purpose image classifier and tried to classify high resolution tissue images with this combined software solution. The classification is by default initiated with a large training set and three main classes (healthy, adenoma, carcinoma), however the new image classification process' wall-clock time was intolerably high on single core PC. The processing time is depending on the size/resolution of the image and the size of the training set. Due to the tissue specific image parameters the classification effectiveness was promising. So we have started a development process to decrease the processing time and further increase the accuracy of the classification. We have developed a workflow based parallel version of the MorphCheck and WND-CHARM classifier software. In collaboration with the MTA SZTAKI Application Porting Centre the WND-CHARM has been ported to some distributed computing infrastructure (DCI). The paper introduces the steps that were taken to optimize WND-CHARM applications running faster using DCIs and some performance results of the tissue image classification proce

关键词： Application porting Medical image processing workflow HP-SEE gUSE Scalability MorphCheck WND-CHARM

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parallel Computing of Particle Filtering Algorithms for Target Tracking Applications

Parallel Computing of Particle Filtering Algorithms for Targ...

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作者： Wu, Jiande University of New Orleans

学位级别：Ph.D.

Particle filtering has been a very popular method to solve nonlinear/non-Gaussian state estimation problems for more than twenty years. Particle filters (PFs) have found lots of applications in areas that include nonlinear filtering of noisy signals and data, especially in target tracking. However, implementation of high dimensional PFs in real-time for large-scale problems is a very challenging computational task. parallel & distributed (P&D) computing is a promising way to deal with the computational challenges of PF methods. The main goal of this dissertation is to develop, implement and evaluate computationally efficient PF algorithms for target tracking, and thereby bring them closer to practical applications. To reach this goal, a number of parallel PF algorithms is designed and implemented using different parallel hardware architectures such as Computer Cluster, Graphics processing Unit (GPU), and Field-Programmable Gate Array (FPGA). Proposed is an improved PF implementation for computer cluster - the Particle Transfer Algorithm (PTA), which takes advantage of the cluster architecture and outperforms significantly existing algorithms. Also, a novel GPU PF algorithm implementation is designed which is highly efficient for GPU architectures. The proposed algorithm implementations on different parallel computing environments are applied and tested for target tracking problems, such as space object tracking, ground multitarget tracking using image sensor, UAV-multisensor tracking. Comprehensive performance evaluation and comparison of the algorithms for both tracking and computational capabilities is performed. It is demonstrated by the obtained simulation results that the proposed implementations help greatly overcome the computational issues of particle filtering for realistic practical problems.

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5th International Conference in image processing and Communications, IP&C 2013

5th International Conference in Image Processing and Communi...

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5th International Conference in image processing and Communications, IP&C 2013

ISBN: (纸本)9783319016214

The proceedings contain 52 papers. The special focus in this conference is on image processing and Communications. The topics include: Adaptive windowed threshold for box counting algorithm in cytoscreening applications;corneal endothelial grid structure factor based on coefficient of variation of the cell sides lengths;a distributed approach for development of deformable model-based segmentation methods;enhancement of low-dose CT brain scans using graph-based anisotropic interpolation;using of EM algorithm to image reconstruction problem with tomography noises;the perception of humanoid robot by human;a fast histogram estimation based on the Monte Carlo method for image binarization;adaptation of the combined image similarity index for video sequences;time-frequency analysis of image based on stockwell transform;2DKLT-based color image watermarking;knowledge transformations applied in image classification task;problems of infrared and visible-light images automatic registration;semantics driven table understanding in born-digital documents;contextual possibilistic knowledge diffusion for images classification;image feature extraction using compressive sensing;a modification of the parallel spline interpolation algorithms;local Eigen background substraction;polarization imaging in 3D shape reconstrucion;layer image components geometry optimization;extraction of data from limnigraf chart images;a simplified visual cortex model for efficient image codding and object recognition;fuzzy rule-based systems for optimizing power consumption in data centers;an algorithm for finding shortest path tree using ant colony optimization metaheuristic and a sensor network gateway for IP network and SQL database.

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The challenges of image segmentation in big remotely sensed imagery data

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Annals of GIS 2014年第4期20卷 233-244页

作者： Xing, Jin Sieber, Renée Kalacska, Margaret Department of Geogrpahy McGill University Burnside Hall Building 805 Sherbrooke Street West Montreal H3A 0B9 QC Canada

With the increase in spatial, spectral and temporal resolutions of Earth observing systems, geospatial and remote sensing (RS) image research is shifting towards a big data paradigm. One of the most important challenges in RS big data is image segmentation, which is defined as a process to group pixels together by a predefined criteria. image segmentation allows for the extraction of features such as roads or habitats or buildings. image segmentation is rendered more difficult with big data because the computing power on single platforms cannot keep pace with the size and velocity of new data. Big data sets must be decomposed for the analysis in distributed and parallel computing platforms. Decomposition through techniques like slicing by spatial extent obscures the geometric and topological information in geospatial data, for example generating fake artefacts. To address these challenges, we propose a geospatial cyberinfrastructure (GCI) that coordinates cloud computing, MapReduce framework, image segmentation algorithms, a spatial extent splitting method and a recomposing technique using moving window. This GCI is evaluated on cloud computing to identify features in a 312.07 GB high-resolution colour aerial photo with Hadoop. K-means-based image segmentation is selected as the case study. We deploy the architecture in private cloud and public cloud implementation, respectively. The results demonstrate the benefits of the decomposing and recomposing methods in segmenting images, removing fake artefacts and reducing information distortion. More general problems in big data are revealed, among them I/O problems, particularly in the amount of preprocessing and post-processing that will be required in any analysis of big imagery data. We conclude with implications for scalability and suggestions to speed up decomposition and recomposition. © 2014, © 2014 Taylor & Francis.

关键词： big data cloud computing decomposition geospatial cyberinfrastructure image segmentation MapReduce recomposition spatial feature extraction

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