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检索条件"任意字段=Conference on Parallel and Distributed Methods for Image Processing III"

共 552 条记录，以下是161-170 订阅

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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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FD-MOBILENET: IMPROVED MOBILENET WITH A FAST DOWNSAMPLING STRATEGY 25

FD-MOBILENET: IMPROVED MOBILENET WITH A FAST DOWNSAMPLING ST...

引用

25th IEEE International conference on image processing (ICIP)

作者： Qin, Zheng Zhang, Zhaoning Chen, Xiaotao Wang, Changjian Peng, Yuxing Natl Univ Def Technol Sci & Technol Parallel & Distributed Lab Changsha Hunan Peoples R China Natl Univ Def Technol Coll Comp Changsha Hunan Peoples R China

ISBN: (纸本)9781479970612

We present Fast-Downsampling MobileNet (FD-MobileNet), an efficient and accurate network for very limited computational budgets (e.g., 10-140 MFLOPs). Our key idea is applying a fast downsampling strategy to MobileNet framework. In FD-MobileNet, we perform 32x downsampling within 12 layers, only half the layers in the original MobileNet. This design brings three advantages: (i) It remarkably reduces the computational cost. (ii) It increases the information capacity and achieves significant performance improvements. (iii) It is engineering-friendly and provides fast actual inference speed. Experiments on ILSVRC 2012 and PASCAL VOC datasets demonstrate that FD-MobileNet consistently outperforms MobileNet and achieves comparable results with ShuffleNet under different computational budgets, for instance, surpassing MobileNet by 5.5% on the ILSVRC 2012 top-1 accuracy and 8.3% on the VOC 2007 mAP under a complexity of 12 MFLOPs. On an ARM-based device, FD-MobileNet achieves 1.11x inference speedup over MobileNet and 1.82x over ShuffleNet under the same complexity.

关键词： Computer vision convolutional neural network deep learning

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GPU-Accelerated Light-field image Super-resolution

GPU-Accelerated Light-field Image Super-resolution

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International conference on Advanced Computing and Applications (ACOMP)

作者： Trung-Hieu Tran Mammadov, Gasim Sun, Kaicong Simon, Sven Univ Stuttgart Inst Parallel & Distributed Syst Stuttgart Germany

ISBN: (纸本)9781538691861

Light-field imaging has become an emerging technology that brings great benefits to many fields, i.e. in photography, academia, and industry. However, these benefits come with the cost of high computation requirement that limits its applications in practice. This paper presents an accelerated solution for 4D light-field image super-resolution. The acceleration is achieved by the mean of parallel computation using graphics processing units. The selected algorithm is broken into functions which is suitable for parallel execution. Each of the functions is then transformed into GPU kernel and executed at each work-item which is associated with a pixel location in the proposed architecture. Using disparity maps extracted from input 4D light-field as an aid for super-resolution task, the proposed approach can successfully super-resolute an input 4D light-field by the factor of 4 horizontally and vertically. Two strategies, Y-RGB and RGB, are proposed to handle color images. Y-RGB is suitable for high-speed processing constraints while RGB is more preferable if output quality is the main concern. Experimental results show that the proposed approach can achieve the speed up of 203x and 71x compared to CPU implementation for Y-RGB and RGB strategy respectively. Regarding output quality, the proposed approach generates a shaper high-resolution image with more details compared to the baseline methods.

关键词： Spatial resolution Graphics processing units Imaging Estimation Computer architecture Three-dimensional displays

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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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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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Auto-Resource Provisioning for MapReduce-Based Multiple Object Tracking in Video 18

Auto-Resource Provisioning for MapReduce-Based Multiple Obje...

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19th International conference on distributed Computing and Networking (ICDCN)

作者： Singh, Gurinderbeer Majumdar, Shikharesh Rajan, Sreeraman Carleton Univ Syst & Comp Engn Dept Ottawa ON Canada

ISBN: (纸本)9781450363723

Use of complex image analysis and globally optimal techniques make the current Multiple Object Tracking (MOT) methods for video analysis computationally slow. An important issue in this context is meeting the specific latency requirement for a given application while processing large scale video data. This is especially important in emergency situations such as accidents, natural calamities, and terrorist attacks. This paper introduces a latency reducing MapReduce/Hadoop-based parallel solution for MOT. The system includes an Auto-Resource Provisioning technique for determining the number of Hadoop nodes required to process the MOT job within a user specified deadline. The estimated number of nodes are then provisioned by the system and the MOT application is executed on the Hadoop cluster comprising the desired number of nodes. A prototype is built using the AWS EC2 cloud. A performance analysis is performed using measurements made on the prototype and insights gained into system behavior and performance are presented.

关键词： Video processing Multiple Object Tracking Cloud Computing distributed Systems MapReduce Apache Hadoop

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MARGIN-EMBEDDING CANONICAL CORRELATION ANALYSIS WITH FEATURE SELECTION FOR PERSON RE-IDENTIFICATION 25

MARGIN-EMBEDDING CANONICAL CORRELATION ANALYSIS WITH FEATURE...

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25th IEEE International conference on image processing (ICIP)

作者： Ma, Linfei Zhang, Xiang Lan, Long Huang, Xuhui Luo, Zhigang NUDT Sci & Technol Parallel & Distributed Lab Changsha 410073 Hunan Peoples R China NUDT Coll Comp Changsha 410073 Hunan Peoples R China NUDT State Key Lab High Performance Comp Changsha 410073 Hunan Peoples R China NUDT Dept Comp Sci & Technol Changsha 410073 Hunan Peoples R China

ISBN: (纸本)9781479970612

Canonical correlation analysis (CCA) is a classical subspace learning method of capturing the common semantic information underlying multi-view data. It has been used in person re-identification (re-ID) task by treating the task of matching identical individuals across non-overlapping multi-cameras as a multi-view learning problem. However, CCA-based re-ID methods still achieve unsatisfactory results because few jointly consider discriminative margin information and selecting importantly relevant features. To address this issue, we propose a novel l(2,1)-norm regularized margin-embedding CCA (l(2,1)-MCCA), which learns a generalized discriminative subspace by employing more discriminative margin information. Moreover, the new method enforces the l(2,1)-norm regularization term over the learned subspace to identify the relevant features. Both lightweight and effective schemes can benefit from each other and endeavor to enlarge the interclass variations whilst reducing the intra-class variations. Experiments on three popular datasets show the efficacy of l(2,1)-MCCA as compared with recently representative re-ID methods.

关键词： Canonical correlation analysis l(2,1)-norm regularization margin embedding person re-identification

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Efficient Training of Convolutional Neural Nets on Large distributed Systems

Efficient Training of Convolutional Neural Nets on Large Dis...

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IEEE International conference on Cluster Computing (CLUSTER)

作者： Sreedhar, Dheeraj Saxena, Vaibhav Sabharwal, Yogish Verma, Ashish Kumar, Sameer IBM Res Bangalore Karnataka India Google Inc Sunnyvale CA 94089 USA

ISBN: (纸本)9781538683194

Deep Neural Networks (DNNs) have achieved impressive accuracy in many application domains including image classification. Training of DNNs is an extremely compute-intensive process and is solved using variants of the stochastic gradient descent (SGD) algorithm. A lot of recent research has focused on improving the performance of DNN training. In this paper, we present optimization techniques to improve the performance of the data parallel synchronous SGD algorithm using the Torch framework: (i) we maintain data in-memory to avoid file I/O overheads, (ii) we propose optimizations to the Torch data parallel table framework that handles multithreading, and (iii) we present MPI optimization to minimize communication overheads. We evaluate the performance of our optimizations on a Power 8 Minsky cluster with 64 nodes and 256 NVidia Pascal P100 GPUs. With our optimizations, we are able to train 90 epochs of the ResNet-50 model on the imagenet-1k dataset using 256 GPUs in just 48 minutes. This significantly improves on the previously best known performance of training 90 epochs of the ResNet-50 model on the same dataset using the same number of GPUs in 65 minutes. To the best of our knowledge, this is the best known training performance demonstrated for the imagenet-1k dataset using 256 GPUs.

关键词： Training Optimization Computational modeling Graphics processing units distributed databases Neural networks Peer-to-peer computing

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Scaling handwritten student assessments with a document image workflow system 31

Scaling handwritten student assessments with a document imag...

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31st Meeting of the IEEE/CVF conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018

作者： Rowtula, Vijay Bhargavan, Varun Kumar, Mohan Jawahar, C.V. IIIT Hyderabad India

With the increase in the number of students enrolled in the university system, regular assessment of student performance has become challenging. This is specially true in case of summative assessments, where one expects the student to write down an answer on paper, rather than selecting a correct answer from multiple choices. In this paper, we present a document image workflow system that helps in scaling the handwritten student assessments in a typical university setting. We argue that this improves the efficiency since the book keeping time as well as physical paper movement is minimized. An electronic workflow can make the anonymization easy, alleviating the fear of biases in many cases. Also, parallel and distributed assessment by multiple instructors is straightforward in an electronic workflow system. At the heart of our solution, we have (i) a distributed image capture module with a mobile phone (ii) image processing algorithms that improve the quality and readability (iii) image annotation module that process the evaluations/feedbacks as a separate layer. Our system also acts as a platform for modern image analysis which can be adapted to the domain of student assessments. This include (i) Handwriting recognition and word spotting [5] (ii) Measure of document similarity [6] (iii) Aesthetic analysis of handwriting [7] (iv) Identity of the writer [4] etc. With the handwriting assessment workflow system, all these recent advances in computer vision can become practical and applicable in evaluating student assessments. © 2019 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Students

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