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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems VI"

共 15613 条记录，以下是4331-4340 订阅

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Acceleration of calculations using block algorithms for the difference solution of the heat equation 5

Acceleration of calculations using block algorithms for the ...

引用

5th International conference on Information Technology and Nanotechnology, ITNT 2019

作者： Golovashkin, D.L. Yablokova, L.V. Reznik, I.D. Image Processing Systems Institute of RAS Branch of the FSRC Crystallography and Photonics RAS Molodogvardejskaya street 151 Samara443001 Russia Samara National Research University Moskovskoe Shosse 34 Samara443086 Russia

In this paper, we propose taking into account the architectural features of the processor at the stage of constructing the numerical method itself. This idea is illustrated by the example of the synthesis of a new difference scheme for the heat conduction equation, which has traditionally been the object of testing innovations in the theory of difference schemes. The architectural feature hierarchical structure of the computer memory chosen led to considerable communication costs even when a single hardware computational flow was used for organising the calculations. This feature is accounted for in computational linear algebra by using block algorithms, and in the theory of difference schemes, by using the technique of programming 'tiling'. However, for the two-layer difference schemes of block algorithms for solving grid equations, prior to the proposed work, it was not known because of the impossibility of organising block calculations by using the existing schemes. Here, we propose a new method of constructing two-layer difference schemes and a mixed scheme with a shift as an example of the application of this method. In the course of the experiments, a five-fold acceleration of calculations according to this scheme was demonstrated relative to the traditional explicit model, with the same computational complexity. © 2019 IOP Publishing Ltd. All rights reserved.

关键词： Numerical methods

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Q-PIM: a genetic algorithm based flexible DNN quantization method and application to processing-in-memory platform 20

Q-PIM: a genetic algorithm based flexible DNN quantization m...

引用

Proceedings of the 57th ACM/EDAC/IEEE Design Automation conference

作者： Yun Long Edward Lee Daehyun Kim Saibal Mukhopadhyay

ISBN: (纸本)9781450367257

This paper presents a genetic algorithm (GA) based training free layer-wise quantization method, named as GAQ, to reduce model complexity of arbitrary DNN architectures. The proposed algorithm formulates an optimization problem to determine the quantization level for each DNN layer under the constrain of maximum accuracy degradation and uses genetic algorithm to solve the problem at the inference stage of any pre-trained DNN models. The experimental results on various DNNs for image classification demonstrate 5x to 17x weight compression rate with insignificant (< 2%) accuracy loss, comparable with existing quantization algorithms which typically require multi-pass retraining and handcrafted tuning. To evaluate the computational benefits of GAQ, we present a SRAM based flexible precision all-digital processing-in-memory (PIM) architecture, named as Q-PIM, that leverages GAQ to optimally control precision for each DNN layer to enhance efficiency. The simulation in 28nm CMOS shows potential for significant energy and latency advantage over fixed-precision PIM architectures.

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Using Synthetic Training Data for Deep Learning-Based GBM Segmentation 41

Using Synthetic Training Data for Deep Learning-Based GBM Se...

引用

41st Annual International conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Lindner, Lydia Narnhofer, Dominik Weber, Maximilian Gsaxner, Christina Kolodziej, Malgorzata Egger, Jan Graz Univ Technol Inst Comp Graph & Vis Intfeldgasse 16 A-8010 Graz Austria Comp Algorithms Med Lab Graz Austria Med Univ Graz Dept Oral & Maxillotacial Surg Auenbruggerpl 5-1 A-8036 Graz Austria Justus Liebig Univ Dept Neurosurg Giessen Germany

ISBN: (纸本)9781538613115

In this work, fully automatic binary segmentation of GBMs (glioblastoma multiforme) in 2D magnetic resonance images is presented using a convolutional neural network trained exclusively on synthetic data. The precise segmentation of brain tumors is one of the most complex and challenging tasks in clinical practice and is usually done manually by radiologists or physicians. However, manual delineations are time-consuming, subjective and in general not reproducible. Hence, more advanced automated segmentation techniques are in great demand. After deep learning methods already successfully demonstrated their practical usefulness in other domains, they are now also attracting increasing interest in the field of medical image processing. Using fully convolutional neural networks for medical image segmentation provides considerable advantages, as it is a reliable, fast and objective technique. In the medical domain, however, only a very limited amount of data is available in the majority of cases, due to privacy issues among other things. Nevertheless, a sufficiently large training data set with ground truth annotations is required to successfully train a deep segmentation network. Therefore, a semi-automatic method for generating synthetic GBM data and the corresponding ground truth was utilized in this work. A U-Net-based segmentation network was then trained solely on this synthetically generated data set. Finally, the segmentation performance of the model was evaluated using real magnetic resonance images of GBMs.

关键词： image segmentation Tumors Magnetic resonance imaging Two dimensional displays Medical diagnostic imaging Training

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A method for automatic detection of acronyms in texts and building a dataset for acronym disambiguation

A method for automatic detection of acronyms in texts and bu...

引用

International conference of Signal processing and Intelligent systems (ICSPIS)

作者： Sasan Azimi Hadi Veisi Reyhaneh Amouie University of Tehran Tehran Iran

The following topics are dealt with: feature extraction; learning (artificial intelligence); medical image processing; image segmentation; convolutional neural nets; image classification; image colour analysis; video ... 详细信息

ISBN: (数字)9781728153506

ISBN: (纸本)9781728153513

关键词： Terminology Task analysis Signal processing algorithms Buildings Databases Text mining Signal processing

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A simple LED panel dection algoritum for Optical Camera Communication systems 10

A simple LED panel dection algoritum for Optical Camera Comm...

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10th International conference on Information and Communication Technology Convergence (ICTC) - ICT Convergence Leading the Autonomous Future

作者： Do, Trong-Hop Yoo, Myungsik Soongsil Univ Sch Elect Engn Seoul 06978 South Korea

ISBN: (纸本)9781728108933

In optical camera communication (OCC), LED panels are used to transmit visible light signal which will be received by cameras. The transmitted data is obtained through processing the captured images of LED panels. The first step in this process is to determine where the LED panels are in the image. Existing object detection algorithms are not originally designed for LED detection and thus is not effective for this task. In this study, based on distinct characteristics of LED, a simple and effective LED panel detection algorithm is proposed. The performance of the proposed algorithm is verified through simulations.

关键词： optical camera LED communication simulation

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A Modified CenterNet for Crack Detection of Sanitary Ceramics

A Modified CenterNet for Crack Detection of Sanitary Ceramic...

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Annual conference of Industrial Electronics Society

作者： Xiaogang Jia Xianqiang Yang Xinghu Yu Huijun Gao Research Institue of Intelligent Control and System Harbin Institute of Technology Harbin Heilongjiang China HIT-Ningbo Institute of Intelligent Equipment Technology Ningbo China

ISBN: (数字)9781728154145

ISBN: (纸本)9781728154152

In this paper, we propose a modified CenterNet to complete the defect detection of Sanitary Ceramics. Generally, visual quality inspection is rather important during the productive process of Sanitary Ceramics and it is nearly impossible to inspect the massive images by hand. Consequently, it is necessary to devise an accurate and real-time system to process the data. However, due to the varied shapes and backgrounds of ceramics, conventional computer vision methods are usually not robust to all those variables. Detectors based on Deep Learning start to be adopted in recent years, but most algorithms require some carefully devised anchor boxes and post-processing methods, which also bring more computational costs. Here we decide to take advantage of the anchor-free model, CenterNet. We change the main structure to fit our own data and introduce an extra branch with shallow layers to strengthen the feature representation. The results have shown the great power of this model. Without even any post-processing methods, our model achieves a result of 96.16 AP on the established dataset.

关键词： Feature extraction Ceramics Computational modeling Detectors Shape image edge detection Heating systems

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Data Compression Based on Coding Methods with a Controlled Level of Quality Loss

Data Compression Based on Coding Methods with a Controlled L...

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2019 IEEE International conference on Advanced Trends in Information Theory, ATIT 2019

作者： Yudin, Oleksandr Boiko, Yuliia Ziubina, Ruslana Buchyk, Serhii Tverdokhleb, vitaly Beresina, Svitlana Taras Shevchenko National University of Kyiv Department of Cyber Security and Information Protection Kyiv Ukraine Taras Shevchenko National University of Kyiv Department of Applied Information Systems Kyiv Ukraine Kharkiv National University of Radio Electronics Department of Information and Network Engineering Kharkiv Ukraine Taras Shevchenko National University of Kyiv Department of Insurance Banking and Risk Management Kyiv Ukraine

ISBN: (数字)9781728161440

ISBN: (纸本)9781728161440

This article is devoted to the actual problem-the formation of modern technologies for processing digital information messages and the development of a video data compression method with a controlled level of quality loss. In contrast to the existing algorithms, the developed method is based on standard procedures for preliminary quantization of the transformant components of the discrete cosine transformation under conditions of effective reduction of their volume and the parameter of weight coefficients. This approach of discrete cosine transformation allows to achieve conversion and redistribution of the output signal energy in the frequency plane and to obtain areas, that have a smaller amount of information representation of the digital image from the position of its visual perception by the human visual system. This redistribution of the energy of the digital video signal allows to eliminate psychovisual redundancy of the image and provides the opportunity to make quality decisions by increasing the data compression ratio. The proposed method allows to increase the compression ratio of images by 7-10% compared to similar methods using DCT. © 2019 IEEE.

关键词： Quality control

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Foreign Objects Intrusion Detection Using Millimeter Wave Radar on Railway Crossings

Foreign Objects Intrusion Detection Using Millimeter Wave Ra...

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IEEE International conference on systems, Man and Cybernetics

作者： Huiling Cai Fei Li Dianzhu Gao Yingze Yang Shuo Li Kai Gao Aina Qin Chao Hu Zhiwu Huang School of Computer Science and Engineering Central South University Changsha China School of Automation Central South University Changsha China School of Electrical and Information Engineering Changsha University of Science and Technology Changsha China College of Automotive and Mechanical Engineering Changsha University of Science and Technology Changsha China Big Date Institute Central South University Changsha China Key Laboratory of Medical Information Research of Hunan Province Central South University Changsha China

ISBN: (数字)9781728185262

ISBN: (纸本)9781728185279

The safety of railway crossings are of great important for rail and road transportation, because serious accidents occur in this area. Therefore, it is necessary to carry out foreign objects detection on railway crossings in order to improve the safety. Traditionally, video surveillance is one such solution, but it suffer from weather and illumination conditions. Under the hard environment conditions, the image of railway crossings is failed to capture by the camera. We propose a foreign objects detection system based on millimeter wave radar which has a higher detection accuracy, without the limitation of weather and light. Unlike vision-based approach, it can operate in darkness, high or low light intensity environment. With a millimeter wave radar, we first obtain the reflected signal from objects or ground and perform signal processing algorithm to extract the targets and suppress the clutter from received signal. We evaluate the detection capabilities of the millimeter wave radar in level crossings of railway.

关键词： Radar detection Signal processing algorithms Radar Object detection Millimeter wave radar Rail transportation Safety

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Problems of processing and Recognition of Digital images in Technical vision systems

Problems of Processing and Recognition of Digital Images in ...

引用

International Multi-conference on Industrial Engineering and Modern Technologies (FarEastCon)

作者： T.R. Zmyzgova E.M. Kuznetsova Y.K. Karpov PhD (Tech) associate professor Head of the Department – Computer Software for Automated Systems Polytechnic Institute of «Kurgan State University» (KSU) Kurgan Russian Federation PhD (Tech) associate professor Department of Automatization of Industrial Operation Polytechnic Institute of «Kurgan State University» (KSU) Kurgan Russian Federation

The basic aspects of using algorithms of image processing and analysis, focused on the use in systems of technical (machine) vision for industry and production, are considered. The problems of image filtration and possible probabilistic models of noise effects are presented. A number of algorithmic approaches are described, which allow to develop an original technology of segmentation and marking of informative *** is shown that the tasks of image processing for the purpose of their classification, recognition and analysis in the systems of technical vision are accompanied by a number of problems, among which we can highlight the lack of mathematical models that adequately describe the observed process or phenomenon, imperfection of image sensors, insufficient performance of computer systems, etc. The considered methods and algorithms of identification are based on geometric transformations, which ensure the speed of the system of identification of images, provided that the recognition reliability is maintained.

关键词： Filtration image recognition Filtering algorithms Machine vision image segmentation Task analysis

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Enhanced Machine Learning Approaches for Diagnosing Building systems

Enhanced Machine Learning Approaches for Diagnosing Building...

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2019 International conference on Internet of Things, Embedded systems and Communications, IINTEC 2019

作者： Gharsellaoui, Sondes Mansouri, Majdi Refaat, Shady S. Abu-Rub, Haitham Messaoud, Hassani National Higher Engineering School of Tunis Electrical Engineering Department Monfleury Tunisia Electrical and Computer Engineering Program Texas AM University at Qatar Doha Qatar National Engineering School of Monastir Laboratory of Automatic Signal and Image Processing Monastir Tunisia

ISBN: (纸本)9781728151847

Fault Detection and Classification (FDC) in Heating, Ventilation, and Air Conditioning (HVAC) systems is an important approach to guarantee the human safety of these systems. Therefore, the implementation of a FDC framework is the focus in this paper. The developed approach aims at reducing the energy needs for buildings and improving indoor environment quality. It merges the benefits of multiscale representation, Principal Component Analysis (PCA), and Machine Learning (ML) classifiers in order to improve the efficiency of FDC in heating systems. Firstly, a multiscale decomposition is used to extract the dynamics of the systems at different scales. The multiscale representation gives several advantages for monitoring heating systems generally driven by events in different time and frequency responses. Secondly, the multiscaled data-sets are then introduced into the PCA model to extract more efficient characteristics. Thirdly, the ML algorithms are applied to the extracted and selected characteristics to deal with the problem of fault diagnosis. The FDC efficiency of the developed technique is evaluated using a simulated data extracted from heating systems. © 2019 IEEE.

关键词： Fault detection

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