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16th IEEE ISPA / 17th IEEE IUCC / 8th IEEE BDCloud / 11th IEEE SocialCom / 8th IEEE SustainCom

作者： Sadafi, Ario Radolko, Martin Serafeimidis, Iosif Hadlak, Steffen Univ Rostock Rostock Germany Arivis AG Munich Germany

ISBN: (纸本)9781728111414

Red blood cell segmentation in microscopic images is the first step for various clinical studies carried out on blood samples such as cell counting, cell shape identification, etc. Conventional methods while often showing a high accuracy are heavily depending on the acquisition modality. Deep learning approaches have shown to be more robust regarding such modalities and still showing a comparable accuracy. In this paper, we first investigate necessary steps to apply a specific type of deep learning methods, namely fully convolutional networks, to red blood cell segmentation. Based on data given and constraints imposed by our partners mainly regarding a high throughput of their data we then describe an exemplary application. First results show, that even with a focus on high performance a good accuracy above 90% can be reached.

关键词： Microscopic image analysis Red blood cells Cell Segmentation Fully convolutional networks

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基于Hadoop技术的大规模数据分布式存储技术研究

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自动化与仪器仪表 2024年第10期 182-186页

作者：魏迎咸阳职业技术学院陕西咸阳712000

随着数据信息的规模不断增加,数据存储技术引来的全新的调整,为了提升大规模数据的信息存储能力,研究设计了Hadoop生态系统的核心组件,对大规模数据分布式存储的处理方法。Hadoop分布式文件系统通过其独特的存储机制和高容错性,有效地... 详细信息

随着数据信息的规模不断增加,数据存储技术引来的全新的调整,为了提升大规模数据的信息存储能力,研究设计了Hadoop生态系统的核心组件,对大规模数据分布式存储的处理方法。Hadoop分布式文件系统通过其独特的存储机制和高容错性,有效地管理了大型文件的存储问题。而MapReduce模型则简化了并行数据处理的复杂性,使得对大规模数据集的处理变得更加高效和可靠。结果显示,分布式处理方式的图片处理速度总是小于单机处理方式,二者处理速度最大相差1502230 ms。研究提出的方法,对系统的CPU占用率更低,可以使系统处理更多的数据信息,该方法对系统CPU的占用率最高仅63%左右。研究提出的数据存储技术可以减小系统面对大规模数据时的压力,为大规模数据存储提供了一种新的技术方案。

关键词： Hadoop技术分布式存储数据存储大规模数据

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基于Hadoop的景区人流密度预测模型设计与实现

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电子设计工程 2021年第18期29卷 39-43页

作者：马骞西安航空职业技术学院陕西西安710089

针对景区人流监控精细化、高效化的需求,文中对视频图像的背景建模与轮廓提取方法进行研究,构建了面向海量计算需求的人流密度预测模型。该模型需要对输入模型的图像质量进行检测,校核图像的分辨率、像素是否满足算法的输入要求。对图... 详细信息

针对景区人流监控精细化、高效化的需求,文中对视频图像的背景建模与轮廓提取方法进行研究,构建了面向海量计算需求的人流密度预测模型。该模型需要对输入模型的图像质量进行检测,校核图像的分辨率、像素是否满足算法的输入要求。对图像的背景引入混合高斯模型进行建模,并借助图像的前景边缘检测描绘人体轮廓,基于Canny算子对人体轮廓进行跟踪、提取特征,以这些轮廓为基础实现人流密度的计算。将最终的提取结果输入到神经网络中,根据时间分布完成人流的预测。在算法的实现上考虑到计算需求,文中搭建了7个计算集群的Hadoop分布式计算平台,实现了算法的分布式存储和并行化计算。计算结果表明,相较于传统算法,该算法提取的人体轮廓更精细、轮廓长度更长,漏检率由13.21%降低到5.24%,人流的预测精度达到了92.43%,提升了7.12%。

关键词：边缘监测背景建模图像处理 Hadoop 人流预测

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image processing and Machine Learning to investigate fibre distribution on fibre-reinforced shotcrete Round Determinate Panels

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CONSTRUCTION AND BUILDING MATERIALS 2018年 190卷 870-880页

作者： Manca, Mirko Karrech, Ali Dight, Phil Ciancio, Daniela Univ Western Australia Sch Civil Environm & Min Engn 35 Stirling Highway Crawley WA 6009 Australia

Fibres in fibre reinforced concrete (FRC) and fibre reinforced shotcrete (FRS) are known to increase the toughness of the composite material thanks to their ability to transfer tensile stresses even after cracks have opened. However, fibres are deemed responsible for uncertainty in mechanical performance of FRC and FRS. This uncertainty is ascribed to how the fibres are physically located with respect to the crack. In this study a novel approach is presented to measure the distribution of fibres from digital images. A computer code that features image processing and Machine Learning algorithms has been developed to extract: i) the 2D location, ii) the mode of failure, iii) the orientation, iv) the pull-out length for each single fibre and v) the total number of fibres bridging the crack. The Machine Learning Algorithm is trained with a database that is attached to this article. This methodology, apart from giving an understanding of how fibres are distributed over the crack, provides the main input of several numerical models that simulate the behaviour of FRC/FRS taking into account the position and orientation of each single fibre. The main advantage of this approach over existing methods is that the hardware required to carry out the analysis consists of a simple smartphone camera while an output with errors within a few thousands parts of the actual measures. The algorithm is employed to analyse the fibres' distribution for 9 Round Determinate Panels (RDP) made of wet-mix shotcrete. The spatial 2D-location of fibres in the crack is tested for randomness with a Monte Carlo procedure and the fibres' distribution is found to follow an Independent Random Process. For fibres that pulled-out from one side of the crack, a further 3D investigation has been carried out. The analysis on the fibres' orientation confirmed the conclusion found by other authors that fibres tend to align perpendicular to the direction of spraying. Furthermore, it is unlikely to find fibres per

关键词： image processing Machine Learning Fibre reinforced shotcrete Statistical distribution of fibres Monte Carlo test

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Indigo: A Domain-Specific Language for Fast, Portable image Reconstruction 32

Indigo: A Domain-Specific Language for Fast, Portable Image ...

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27th International Heterogeneity in Computing Workshop in conjunction with 32nd IEEE International parallel and distributed processing Symposium (IPDPS)

作者： Driscoll, Michael Brock, Benjamin Ong, Frank Tamir, Jonathan Liu, Hsiou-Yuan Lustig, Michael Fox, Armando Yelick, Katherine Univ Calif Berkeley Dept Elect Engn & Comp Sci Berkeley CA 94720 USA Lawrence Berkeley Natl Lab Computat Res Div Berkeley CA 94720 USA

ISBN: (纸本)9781538643686

Linear operators used in iterative methods like conjugate gradient have typically been implemented either as "matrix-driven" subroutines backed by explicit sparse or dense matrices, or as "matrix-free" subroutines that implement specific linear operations directly (e.g. FFTs). The matrix-driven approach is generally more portable because it can target widely-available BLAS libraries, but it can be inefficient in terms of time and space complexity. In contrast, the matrix-free approach is more performant because it leverages structure in operations, but it requires each operator be re-implemented on each new platform. To increase performance and portability, we propose a hybrid approach that represents linear operators as expression trees. Leaf nodes in the tree are either matrix-free or matrix-driven operators, and interior nodes represent mathematical compositions (sums, products, transposes) or structural compositions (stacks, block diagonals, etc.) of the leaf operators. This representation enables expert-guided reordering and fusion transformations that can improve performance or reduce memory pressure. We implement our approach in a domain-specific language called Indigo. We assess Indigo on image reconstruction problems arising in four application areas: magnetic resonance imaging, ptychography, magnetic particle imaging, and fluorescent microscopy. We give performance results from vendor BLAS libraries, and we introduce specializations to Sparse BLAS routines that achieve near-Roofline performance on multi-core, many-core, and GPU systems.

关键词： Sparse matrices image reconstruction Algorithms Vegetation Production facilities Imaging Integrated circuits"

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distributed parallel Simulation of Primary Sample Space Metropolis Light Transport 18th

Distributed Parallel Simulation of Primary Sample Space Metr...

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18th International Conference on Algorithms and Architectures for parallel processing (ICA3PP)

作者： Wu, Changmao Zhang, Changyou Sun, Qiao Chinese Acad Sci Inst Software Lab Parallel Software & Computat Sci Beijing Peoples R China

ISBN: (纸本)9783030050511;9783030050504

Monte-Carlo rendering algorithms are known for producing highly realistic images, but at a significant computational cost, because they rely on tracing up to trillions of light paths through a scene to simulate physically based light transport. For this reason, a large body of research exists on various techniques for accelerating these costly algorithms. As one of the Monte-Carlo rendering algorithms, PSSMLT (Primary Sample Space Metropolis Light Transport) is widely used nowadays for photorealistic rendering. Unfortunately, the computational cost of PSSMLT is still very high since the space of light paths in high-dimension and up to trillions of paths are typically required in such path space. Recent research on PSSMLT has proposed a variety of optimized methods for single node rendering, however, multi-node rendering for PSSMLT is rarely mentioned due in large part to the complicated mathematical model, complicated physical processes and the irregular memory access patterns, and the imbalanced workload of light-carrying paths. In this paper, we present a highly scalable distributed parallel simulation framework for PSSMLT. Firstly, based on light transport equation, we propose the notion of sub-image with certain property for multi-node rendering and theoretically prove that the whole set of sub-images can be combined to produce the final image;Then we further propose a sub-image based assignment partitioning algorithm for multi-node rendering since the traditional demand-driven assignment partitioning algorithm doesn't work well. Secondly, we propose a physically based parallel simulation for the PSSMLT algorithm, which is revealed on a parallel computer system in master-worker paradigm. Finally, we discuss the issue of granularity of the assignment partitioning and some optimization strategies for improving overall performance, and then a static/dynamic hybrid scheduling strategy is described. Experiments show that framework has a nearly linear speedup along wi

关键词： Primary sample space metropolis light transport Physically based ray tracing Assignment partitioning Hybrid scheduling distributed computing Performance optimization

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DEEP image CLUSTERING USING CONVOLUTIONAL AUTOENCODER EMBEDDING WITH INCEPTION-LIKE BLOCK 25

DEEP IMAGE CLUSTERING USING CONVOLUTIONAL AUTOENCODER EMBEDD...

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25th IEEE International Conference on image processing (ICIP)

作者： Wang, Qiang Xu, Jiaqing Li, Rongchun Qiao, Peng Yang, Ke Li, Shijie Dou, Yong Natl Univ Def Technol Natl Lab Parallel & Distributed Proc Changsha Hunan Peoples R China Natl Univ Def Technol Sch Comp Changsha Hunan Peoples R China

ISBN: (纸本)9781479970612

image clustering is one of the challenging tasks in machine learning, and has been extensively used in various applications. Recently, various deep clustering methods has been proposed. These methods take a two-stage approach, feature learning and clustering, sequentially or jointly. We observe that these works usually focus on the combination of reconstruction loss and clustering loss, relatively little work has focused on improving the learning representation of the neural network for clustering. In this paper, we propose a deep convolutional embedded clustering algorithm with inception-like block (DCECI). Specifically, an inception-like block with different type of convolution filters are introduced in the symmetric deep convolutional network to preserve the local structure of convolution layers. We simultaneously minimize the reconstruction loss of the convolutional autoencoders with inception-like block and the clustering loss. Experimental results on multiple image datasets exhibit the promising performance of our proposed algorithm compared with other competitive methods.

关键词： Deep Clustering Convolutional Autoencoders Kullback-Leibler divergence

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Brain Tumor Segmentation by Texture Feature Extraction with the parallel Implementation of Fuzzy C-Means using CUDA on GPU 5

Brain Tumor Segmentation by Texture Feature Extraction with ...

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5th IEEE International Conference on parallel, distributed and Grid Computing (PDGC)

作者： Saxena, Sanjay Shama, Suraj Int Inst Informat Technol Bhubaneswar IIIT Bhuban Dept Comp Sci & Engn Bhubaneswar India

ISBN: (纸本)9781728106465

Exact segmentation of the brain tumor is one of the imperative tasks in medical image processing and its analysis as it deals with extracting the information of the tumorous region from the brain MRI sequences. Automated segmentation and detection of brain tumors from the brain MRI is an exigent issue caused by the texture, size, shape, and location. In this paper, a significant method of brain tumor segmentation from the FLAIR MRI sequences is by classifying local window followed by parallel fuzzy c means clustering. Fuzzy c- means methods have shown their efficiency in extracting a variety of objects in several medical image processing applications. However, one of the major issues of these algorithms is high computational requirements at the time of dealing with large data set. Nowadays, NVIDIA's GPU plays an extremely essential role in implementing such time-consuming algorithms to reduce the time complexity. Our experiments based on NCI-MICCAI BRATS 2017 FLAIR MRI of HGG (High-Grade Glioma) demonstrate the efficiency of the implemented parallel algorithm. For the segmentation of tumorous region, a mechanism of sliding window is implemented on CPU (host) in which a 45 X 45 sized window is taken to classify whether that particular window is having tumor region or not. For perfect segmentation at the GPU (device) side, fuzzy c means technique is used to get the exact location of the tumor. Approx 17.6 speed up obtained, for the BRATS data sets over the implementation of the algorithm on CPU. Apart from speed up significant dice similarity coefficients are obtained which shows the efficient segmentation in the reasonable time.

关键词： Brain Tumor Segmentation Computed Unified Device Architecture Graphics processing Unit High Performance Computing Texture Feature Extraction Fuzzy C-means

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Investigation of image processing techniques for glaucoma detection in human eyes 5

Investigation of image processing techniques for glaucoma de...

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5th International Conference on parallel, distributed and Grid Computing, PDGC 2018

作者： Kaushal, Shilpa Datt Sharma, Sunil Jain, Shruti Dept. of Electronics Comm. Engg. Jaypee University of Information Technology Distt. SolanHimachal Pradesh India

ISBN: (纸本)9781538660263

Glaucoma is a disease associated with retina of eye. Presently, millions of human being is suffering from this disease. Early detection of these diseases can save the people from blindness. Therefore, various methods have been developed for its detection. In this paper, we have studied the reported methods and summarized their performance in terms of accuracy of detection. © 2018 IEEE.

关键词： Ophthalmology

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An improved distributed storage and query for remote sensing data

An improved distributed storage and query for remote sensing...

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6th International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI)

作者： Jing, Weipeng Tian, Dongxue Northeast Forestry Univ Coll Informat & Comp Engn Harbin 150040 Heilongjiang Peoples R China

With the rapid development of information technology, the amount of remote sensing data is increasing at an unprecedented scale. In the presence of massive remote sensing data, the traditional processing methods have the problems of low efficiency and lack of scalability, so this paper uses open source big data technology to improve it. Firstly, the storage model of remote sensing image data is designed by using the distributed storage database HBase. Then, the grid index and the Hibert curve are combined to establish the index for the image data. Finally, the method of MapReduce parallel processing is used to write and query remote sensing images. The experimental results show that the method can effectively improve the data writing and query speed, and has good scalability. Copyright (C) 2018 Elsevier Ltd. All rights reserved.

关键词： remote data distribute storage data query HBase mapreduce

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