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检索条件"任意字段=17th Image Processing: Algorithms and Systems Conference, IPAS 2019"

共 249 条记录，以下是111-120 订阅

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image Analysis using Convolutional Neural Networks for Modeling 2D Fracture Propagation 17

Image Analysis using Convolutional Neural Networks for Model...

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17th IEEE International conference on Data Mining (ICDMW)

作者： Miller, Robyn L. Moore, Bryan Viswanathan, Hari Srinivasan, Gowri Los Alamos Natl Lab Los Alamos NM 87545 USA

ISBN: (纸本)9781538638002

the primary failure mechanism in brittle materials such as ceramics, granite and some metal alloys is through the presence of defects which result in crack formation and propagation under the application of load. We are interested in studying this process of crack propagation, interaction and coalescence, which degrades the strength of the specimen. Traditionally, engineering applications that study these materials employ finite element mesh-based methods that require hundreds of hours of processing time on multi-core high performance clusters. We have developed a graph-based reduced order model that captures key geometric and topological features of the dynamic fracture propagation network. We report here the early stages of our study in which deep neural networks will be applied to dynamic directed weighted graphs capturing various metrics of crack-pair interaction strength with the aim of predicting crack lengths, dynamic crack growth/coalescence properties, distributions of these properties over the entire material through time, failure paths and time to failure. Our graph-based representations allow us to consider detailed topology in conjunction with metric geometry to gain insights into the dominant mechanisms that drive the physics in these systems.

关键词： Physics Feature extraction Data models Integrated circuits Data mining Geometry Heuristic algorithms

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Fine-Grain Parallel Reconstruction Algorithm for MR images 17

Fine-Grain Parallel Reconstruction Algorithm for MR Images

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17th International conference on Parallel and Distributed Computing systems, PDCS 2004

作者： Lee, Hyo Jong Potkin, Steven Div. of Electronic and Information Chonbuk National University Jeonju Korea Republic of Dept. of Psychiatry and Human Behavior University of California Irvine IrvineCA92697 United States

ISBN: (纸本)9781618398185

the role of magnetic resonance imaging (MRI) analysis is critical in medical diagnoses for performing a noninvasive diagnostic technique. the raw data of MRI consists of series of complex numbers that represent phases and amplitudes of signals. the reconstruction procedure of the MRI requires lengthy computational time and complicated procedure due to the heavy discrete Fourier transformation. A parallel algorithm is developed for the cluster of heterogeneous computers and analyzed based on its performance, focusing on communication overhead and granularity. Granularity and scalability are critical factors to determine the performance of parallel reconstruction algorithms. the performance of fine granularity is more efficient compared to the one of coarse granularity, if its input and output(I/O) processing is fast enough to handle messages from all slave processors. © (2004) by the International Society for Computers and their Applications All rights reserved.

关键词： image reconstruction

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An efficient GPU implementation technique for higher-order 3D stencils 21

An efficient GPU implementation technique for higher-order 3...

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21st IEEE International conference on High Performance Computing and Communications, 17th IEEE International conference on Smart City and 5th IEEE International conference on Data Science and systems, HPCC/SmartCity/DSS 2019

作者： Anjum, Omer Simon, Garcia De Gonzalo Hidayetoglu, Mert Hwu, Wen-Mei Department of Electrical and Computer Engineering University of Illinois Urbana-ChampaignIL United States

ISBN: (纸本)9781728120584

Stencils are a family of widely used computational patterns that play a critical role in various scientific and engineering applications. Stencil computations are known to be memory-bandwidth bound, thus a number of different techniques and algorithms that optimizes memory bandwidth usage have been proposed. However, existing techniques fall short in addressing the needs of large stencils, particularly more advanced stencil patterns involving non-axis aligned grid points. To handle non-axis aligned grid points, existing methods either use 3D caching or 2D caching schemes with more than one pass over the stencil per iteration, which suffers from the high intensity of memory accesses. the large number of memory accesses in these methods hinder the available performance. In this work, we present a new GPU-based implementation technique called 'SWiC' that focuses on using 2D caching to efficiently implement advanced 3D stencil patterns, involving non-axis aligned grid points, and reducing global memory transactions by increased data reuse while only requiring a single pass per iteration. In contrast to the current approaches that maintain input register queues, the proposed approach maintains and updates the output register queue instead. the analysis shows that SWiC achieves a significant reduction in memory transactions which translates to a significant application speedup, 1.6x to 5.76x, when compared to the current state-of-the-art GPU stencil implementation. 'SWiC' was evaluated across the latest three Nvidia GPU architectures as of the writing of this paper, as well as various stencil patterns and sizes. We also show that 'SWiC' does not suffer from performance penalties when applied to simpler 3D stencils without non-axis aligned grid points, covering a wide application range. When running on a multi-node setting, we study the scaling efficiency of SWiC and show that it is able to achieve a weak scaling efficiency of about 96%. © 2019 IEEE.

关键词： Graphics processing unit

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A hierarchical clustering strategy of processing class imbalance and its application in fraud detection 21

A hierarchical clustering strategy of processing class imbal...

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作者： Zhang, Youjun Liu, Guanjun Zheng, Lutao Yan, Chungang Key Laboratory of the Ministry of Education for Embedded System and Service Computing Shanghai Electronic Transactions and Information Service Collaborative Innovation Center Tongji University Shanghai China

ISBN: (纸本)9781728120584

With the Internet and mobile communications becoming an indispensable part of people's daily lives, online transactions have become one of the most common payment methods. However, transaction fraud incidents also occur frequently, causing a larger number of economic losses. therefore, transaction fraud detection is significant. the methods of machine learning are often used to detect fraudulent transactions from a larger amount of transaction data. However, the class imbalance problem reduces the performance of these methods. there are mainly four factors causing this problem: imbalanced class distribution, sample size, class separability and within-class concept. the existing improvement strategies for class imbalance mainly focus on the first factor but omit other three ones. this paper considers the four factors to propose a comprehensive model called clustering tree. Constructing a clustering tree includes two steps: 1) we first select a clustering algorithm considering the class separability;and then 2) we use this clustering algorithm to construct a tree that can be used to determine if an incoming transaction is illegal. the root node of this tree contains all samples, and we consider both the imbalanced class distribution and the within-class concept when samples are hierarchically divided into sub-nodes during the constructing process. We compare the proposed method with five state-of-the-art ones on two real transaction datasets, and the experimental results show that our method works better. © 2019 IEEE.

关键词： Clustering algorithms

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Twofold Binary image Consensus for Medical Imaging Meta-Analysis 17th

Twofold Binary Image Consensus for Medical Imaging Meta-Anal...

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17th International conference on Information processing and Management of Uncertainty in Knowledge-Based systems (IPMU)

作者： Lopez-Molina, Carlos Ruiz de Gordoa, Javier Sanchez Zelaya-Huerta, Victoria De Baets, Bernard Univ Publ Navarra Dept Automat & Comp Pamplona Spain NavarraBiomed Serv Navarro Salud Osasunbidea Pamplona Spain Univ Ghent Dept Data Anal & Math Modelling KERMIT Ghent Belgium

ISBN: (纸本)9783319914763;9783319914756

In the field of medical imaging, ground truth is often gathered from groups of experts, whose outputs are generally heterogeneous. this procedure raises questions on how to compare the results obtained by automatic algorithms to multiple ground truth items. Secondarily, it raises questions on the meaning of the divergences between experts. In this work, we focus on the case of immunohistochemistry image segmentation and analysis. We propose measures to quantify the divergence in groups of ground truth images, and we observe their behaviour. these measures are based upon fusion techniques for binary images, which is a common example of non-monotone data fusion process. Our measures can be used not only in this specific field of medical imagery, but also in any task related to meta-quality evaluation for image processing, e.g. ground truth validation or expert rating.

关键词： Data fusion Twofold Consensus Ground Truth Medical imagery Immunohistochemistry (IHC) Meta-analysis

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Video: Banner – An image sensor reconfiguration framework for seamless resolution-based tradeoffs 17

Video: Banner – An image sensor reconfiguration framework f...

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17th ACM International conference on Mobile systems, Applications, and Services, MobiSys 2019

作者： Hu, Jinhan Shearer, Alexander Rajagopalan, Saranya LiKamWa, Robert Arizona State University TempeAZ United States

ISBN: (纸本)9781450366618

the high energy consumption of visual sensing continues to impede the future of mobile vision in which devices will continuously compute visual information from sensory data, e.g,. for visual personal assistants or for augmented reality (AR). While vision algorithms continue to improve in task accuracy and speed, mobile and wearable vision systems fail to achieve sufficient battery life when vision tasks are continuously running. Continuous video capture drains the battery of Google Glass in 30 minutes [2]. It is well known that a common culprit is the energy-expensive traffic of image data [8, 9]. Transferring high resolutions at high frame rates draws substantial power consumption from the analog-digital conversion, the sensor interface transactions, and the memory usage. Simply capturing 1080p frames at 30 frames per second consumes more than 2.4 W of system power measured on a MOTO Z smartphone. However, capturing and displaying 480p frames only consumes 1.3 W of system power. image resolution can create an interesting tradeoff for visual tasks: low resolution promotes low energy consumption, while high resolution promotes high imaging fidelity for high visual task accuracy. For example, as we explore with our AR marker-based pose estimation case study, lower resolutions suffice when an AR marker is close, but high resolutions are needed when the AR marker is far away or small. this tradeoff has been explored by several visual computing system works including marker pose estimation, object detection, and face recognition [3–6, 9, 10, 13, 14, 17]. We too advocate that mobile vision systems should be able to benefit from the ability to situationally sacrifice image resolution to save system energy when imaging detail is unnecessary. Unfortunately, any change in sensor resolution leads to a substantial pause in frame delivery. this is illustrated in Figure 1a. We measure that reconfiguring sensor resolution in the Android OS prevents the application from receiving

关键词： Energy efficiency

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Collaborative Computation in Self-organizing Particle systems 1

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17th International conference on Unconventional Computation and Natural Computation (UCNC)

作者： Porter, Alexandra Richa, Andrea Stanford Univ Stanford CA 94305 USA Arizona State Univ Tempe AZ USA

ISBN: (数字)9783319924359

ISBN: (纸本)9783319924359;9783319924342

Many forms of programmable matter have been proposed for various tasks. We use an abstract model of self-organizing particle systems for programmable matter which could be used for a variety of applications, including smart paint and coating materials for engineering or programmable cells for medical uses. Previous research using this model has focused on shape formation and other spatial configuration problems (e.g., coating and compression). In this work we study foundational computational tasks that exceed the capabilities of the individual constant size memory of a particle, such as implementing a counter and matrix-vector multiplication. these tasks represent new ways to use these self-organizing systems, which, in conjunction with previous shape and configuration work, make the systems useful for a wider variety of tasks. they can also leverage the distributed and dynamic nature of the self-organizing system to be more efficient and adaptable than on traditional linear computing hardware. Finally, we demonstrate applications of similar types of computations with self-organizing systems to image processing, with implementations of image color transformation and edge detection algorithms.

关键词： image processing

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Plenoptic camera image simulation for reconstruction algorithm verification 17

Plenoptic camera image simulation for reconstruction algorit...

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17th conference of Novel Optical systems Design and Optimization

作者： Schwiegerling, Jim Univ Arizona Coll Opt Sci Tucson AZ 85721 USA Univ Arizona Dept Ophthalmol & Vis Sci Tucson AZ 85721 USA

ISBN: (纸本)9781628412208

Plenoptic cameras have emerged in recent years as a technology for capturing light field data in a single snapshot. A conventional digital camera can be modified with the addition of a lenslet array to create a plenoptic camera. Two distinct camera forms have been proposed in the literature. the first has the camera image focused onto the lenslet array. the lenslet array is placed over the camera sensor such that each lenslet forms an image of the exit pupil onto the sensor. the second plenoptic form has the lenslet array relaying the image formed by the camera lens to the sensor. We have developed a raytracing package that can simulate images formed by a generalized version of the plenoptic camera. Several rays from each sensor pixel are traced backwards through the system to define a cone of rays emanating from the entrance pupil of the camera lens. Objects that lie within this cone are integrated to lead to a color and exposure level for that pixel. To speed processing three-dimensional objects are approximated as a series of planes at different depths. Repeating this process for each pixel in the sensor leads to a simulated plenoptic image on which different reconstruction algorithms can be tested.

关键词： Plenoptic Light field Depth of field image simulation

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Pattern Recognition Framework for Histological Slide Segmentation 17th

Pattern Recognition Framework for Histological Slide Segment...

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17th International conference on Computer Information systems and Industrial Management (CISIM)

作者： Jelen, Lukasz Kulus, Michal Jurek, Tomasz Wroclaw Univ Sci & Technol Dept Comp Engn Wybrzeze Wyspianskiego 27 PL-50370 Wroclaw Poland Wroclaw Med Univ Dept Human Morphol & Embryol Histol & Embryol Div Ul Chalubinskiego 6a PL-50368 Wroclaw Poland Wroclaw Med Univ Dept Forens Med Ul J Mikulicza Radeckiego 4 PL-50345 Wroclaw Poland

ISBN: (纸本)9783319999548;9783319999531

the venous system is similar in all people, but there are some individual variations. the system is well described and compared in the literature but it seems that there is a lack of comparison on the microscopic level. In this paper we present a segmentation framework for histological image segmentation that uses a clustering approach. the described framework can be used for further comparison of the venous system at the microscopic level. For that purpose we adopted a k-means and fuzzy c-means algorithms to classify image pixels to obtain vein segmentation. the presented results are promising and achieved partitioning can further be utilized for quantitative vein comparison.

关键词： Movat FCM Segmentation KNN segmentation Fuzzy c-means k-means image processing Computer aided diagnosis Pattern Recognition Histological slide

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Enhancing Efficiency and Quality of image Caption Generation with CARU 17th

Enhancing Efficiency and Quality of Image Caption Generation...

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17th International conference on Wireless algorithms, systems, and Applications (WASA)

作者： Huang, Xuefei Ke, Wei Sheng, Hao Macao Polytechn Univ Fac Sci Appl Macau Peoples R China Macao Polytechn Univ Engn Res Ctr Appl Technol Machine Translat & Arti Minist Educ Macau Peoples R China Beihang Univ Sch Comp Sci & Engn State Key Lab Virtual Real Technol & Syst Beijing 100191 Peoples R China Beihang Hangzhou Innovat Inst Yuhang Hangzhou 310023 Peoples R China

ISBN: (纸本)9783031192135;9783031192142

image caption is textual explanation automatically generated by a computer according to the content in an image. It involves both image and natural language processing, and thus becomes an important research topic in pattern recognition. Deep learning has been successful in accomplishing this task, and the quality of captions generated by existing methods is already high. However, due to the broadness and variety of image caption applications, the current generated captions are still not sufficiently detailed, and the training efficiency can also be improved. therefore, under the encoder-decoder framework of deep learning, how to use fewer parameters to improve the training efficiency and retain the quality of the generated image descriptions is a huge challenge. In this work, we introduce an improved method based on the encoder-decoder structure, adding an attention mechanism, and applying the content adaptive recurrent unit (CARU), as the decoder, to generate image captions. Inspired by GRU, CARU is designed to have comparable performance with fewer parameters, and is sensitive to the features in hidden layers. the experimental results show, based on MsCOCO dataset, the proposed method achieved better performance than that using GRU as the decoder, and took less training time, effectively improves the training efficiency.

关键词： image caption generation Content adaptive recurrent unit Feature extraction Deep learning Training efficiency

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