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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101-110 订阅

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LASER AND MULTI-image REVERSE ENGINEERING systems FOR ACCURATE 3D MODELLING OF COMPLEX CULTURAL ARTEFACTS 2

LASER AND MULTI-IMAGE REVERSE ENGINEERING SYSTEMS FOR ACCURA...

引用

2nd International conference of Geomatics and Restoration (GEORES)

作者： Ioannidis, Ch Piniotis, G. Soile, S. Bourexis, F. Boutsi, A-M Chliverou, R. Tsakiri, M. Natl Tech Univ Athens Sch Rural & Surveying Engn Athens Greece

the recent scientific and technical developments of reverse engineering methods and tools have broadened the possibilities of applications in the field of cultural heritage conservation. In this paper, two different non-contact reverse engineering systems were utilized for 3D data acquisition of a cultural heritage artefact. the object of interest is a 17th century wooden engraved ecclesiastical sanctuary ciborium. the requirement of the 3D model is to aid the art conservators for the preservation of the wooden material and the restoration of small damages and cracks in the engraved parts, thus requiring accuracy of the model in the order of submillimetre. In this work, a Faro Vantage laser tracker was employed along with the FARO Edge Arm. In addition, image-based modelling was also implemented with a large number of overlapping images acquired with a Canon EOS 6D camera and processed using the well-known Structure from Motion (SfM) method with an auto-calibration procedure. the digital data acquisition and processing procedures of the scanned geometry are described and compared to evaluate the performance of both systems in terms of data acquisition time, processing time, reconstruction precision and final model quality. Whilst models produced with laser scanning and image-based techniques is not a novel approach, the combination of laser tracking and photogrammetric data still presents limited documentation in the field of cultural artefact documentation mainly due to the extremely high cost of the laser tracking systems.

关键词： 3D model laser tracker close range photogrammetry reverse engineering

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FPGA Design of an Automatic Target Generation Process for Hyperspectral image Analysis

FPGA Design of an Automatic Target Generation Process for Hy...

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17th IEEE International conference on Parallel and Distributed systems (ICPADS)

作者： Bernabe, Sergio Lopez, Sebastian Plaza, Antonio Sarmiento, Roberto Garcia Rodriguez, Pablo Univ Extremadura Dept Technol Comp & Commun Hyperspectral Comp Lab Avda Univ S-N E-10003 Caceres Spain Univ Las Palmas Gran Canaria Inst Appl Microelect IUMA E-35017 Las Palmas Gran Canaria Spain Univ Extremadura GIM E-10003 Badajoz Spain

ISBN: (纸本)9780769545769

Onboard processing of remotely sensed hyperspectral data is a highly desirable goal in many applications. For this purpose, compact reconfigurable hardware modules such as field programmable gate arrays (FPGAs) are widely used. In this paper, we develop a new implementation of an automatic target generation process (ATGP) for hyperspectral images. Our implementation is based on a design methodology that starts from a high-level description in Matlab (or alternative C/C++) and obtains a register transfer level (RTL) description that can be ported to FPGAs. In order to validate our new implementation, we develop a quantitative and comparative study using two different FPGA architectures: Xilinx Virtex-5 and Altera Stratix-III Altera. Experimental results have been obtained in the context of a real application focused on the detection of mineral components over the Cuprite mining district (Nevada), using hyperspectral data collected by NASA's Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS). Our experimental results indicate that the proposed implementation can achieve peak frequency designs above 200MHz in the considered FPGAs, in addition to satisfactory results in terms of target detection accuracy and parallel performance. this represents a step forward towards the design of real-time onboard implementations of hyperspectral image analysis algorithms.

关键词： Hyperspectral image analysis target detection field programmable gate arrays (FPGAs)

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17th CIRP conference on Intelligent Computation in Manufacturing Engineering, CIRP ICME 2023

17th CIRP Conference on Intelligent Computation in Manufactu...

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17th CIRP conference on Intelligent Computation in Manufacturing Engineering, CIRP ICME 2023

the proceedings contain 180 papers. the topics discussed include: analysis of medical data and machine-learning algorithms from the perspective of public-goods models of data-provision decision making;price competition for service provision with different bargaining abilities;survey on stakeholder cooperative behavior for designing voluntary medical data provision motivation mechanisms;analysis of excellent service systems from co-creation and emergent synthesis perspective;meta-heuristic scheduling auction applying distributed genetic algorithm;the impact of characteristic function of Shapley value mechanism in distributed machine learning environment for equipment diagnosis;automatic measurement of timber diameter using image processing;intelligent scheduling based on discrete-time simulation using machine learning;and a fuzzy synthesis approach for hierarchical decision analysis to select optimum repair technique.

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Multi-resource fair allocation for cloud federation 21

Multi-resource fair allocation for cloud federation

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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： Zhang, Gengwei Lu, Runhao Wu, Weigang School of Data and Computer Science Sun Yet-Sen University Guangzhou China Guangdong Province Key Lab. of Big Data Analysis and Processing Sun Yet-Sen University Guangzhou China

ISBN: (纸本)9781728120584

Since the number of tasks submitted by user changes with time, the cloud data center cannot meet all resource requests in time when the load is high, and the idle resources are not effectively utilized at low load. Cloud federation can be established to share resources between different Cloud Service Providers (CSPs), enabling load balance between clouds. CSPs in the cloud federation are independent and their individual benefit need to be guaranteed when sharing resources with others. In this paper, we establish a two-level asynchronous scheduling model within and between the clouds, and introduce high priority queues in the cloud to prioritize the tasks sent by the federation scheduler. Due to the heterogeneity of resource requirements of user tasks, we design two multi-resource fair scheduling algorithms for cloud and federation. Within the cloud, we add the dynamically adjusted weighting factor to the Dominant Resource Fairness (DRF) to increase the priority of user whose task is outsourced. We define the dominant share and dominant contribution of the cloud. the priority of the cloud is the difference between the dominant share and the dominant contribution, which ensure the incentive and fairness of the cloud. Based on the open source container management system Kubernetes, we develop a cloud federation prototype system to implement our algorithm, and conduct performance evaluations through experiments. the results show that, compared with the existing fair allocation algorithm, the multi-resource fair allocation mechanism proposed in this paper can guarantee users' QoS of each CSP. © 2019 IEEE.

关键词： Scheduling

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Continuous non-negative matrix factorization for time-dependent data

Continuous non-negative matrix factorization for time-depend...

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17th European Signal processing conference, EUSIPCO 2009

作者： Omlor, Lars Slotine, Jean-Jacques Department of Cognitive Neurology University of Tübingen Wilhelmstr. 7 72074 Tübingen Germany Nonlinear Systems Laboratory MIT 77 Massachusetts Avenue Cambridge MA 02139-4307 United States

In many signal processing applications such as image analysis or spectral decomposition, non-negativity constrains are necessary to provide a physical reasonable interpretation. this constraint is exploited by non-negative matrix factorization (NMF) methods. the goal of NMF is to find low rank matrices A ≥ 0 and B ≥ 0 such that the positive data matrix X can be approximated by AB ≈ X. Most algorithms for this type of factorization are discrete-step iterative optimization procedures based on gradient descent or QuasiNewton methods. Here we propose a continuous-time version of NMF based on dynamical systems with positive solutions, which allows time-dependent cost functions, e.g. due to time-dependent data. © EURASIP, 2009.

关键词： Iterative methods

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Convolutional Neural Networks: Estimating Relations in the Ising Model on Overfiting 17

Convolutional Neural Networks: Estimating Relations in the I...

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17th IEEE International conference on Cognitive Informatics and Cognitive Computing (ICCI*CC)

作者： Gavrilov, Andrei Jordache, Alex Vasdani, Maya Deng, Jack Univ British Columbia Vancouver BC Canada

ISBN: (纸本)9781538633601

A CNN (Convolutional Neural Network) is one of actively researched and broadly applied deep machine learning methods. A CNN is composed of a feed-forward neural network that takes in images as inputs, and outputs a probability value associated to a class that best describes the image. As well, it is constructed of multiple layers, which include convolutional, max-pooling and fully connected layers. However, the training set has a large influence on the accuracy of a network, and hence it is paramount to create a network architecture that prevents overfitting (when a trained model cannot differentiate newly input data from its test data) and underfitting (the inability of a model to find relationships among inputs). this paper addresses the above deficiencies by comparing the statistics of CNN image recognition algorithms to the Ising model. the Ising model consists of magnetic dipole moments that can be in one of two states: +1 or -1. Using a two-dimensional square-lattice array once a training set of such data is complete, we determine the impact that network parameters, specifically learning rate and regularization rate, have on the adaptability of convolutional neural networks for image classification. Our results not only contribute to a better theoretical understanding of a CNN, but also provide concrete guidance on preventing model overfitting and underfitting when a CNN is applied for image recognition.

关键词： Machine Learning Convolutional Neural Network image processing Ising Model Data Overfitting Learning Rate Regularization Rate Cognitive systems

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processing of the image from infrared focal plane array using FPGA-based system

Processing of the image from infrared focal plane array usin...

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作者： Sosnowski, Tomasz Bieszczad, Grzegorz Kastek, Mariusz Madura, Henryk Institute of Optoelectronics Military University of Technology Warsaw Poland

ISBN: (纸本)9788392875635

the paper presents the image processing and analysis system for a thermal camera. Presented device controls the microbolometer FPA, performs non-uniformity correction and bad pixel mapping and controls the process of displaying the thermal image. the system was designed in such a way, that signal processing algorithms, needed for specific tasks, can be implemented in it without hardware modifications. It was achieved by the application of a FPGA device and microprocessor unit, which both can be re-programmed inside the system. © 2010 by Department of Microelectronics & Computer Science, Technical University of Lodz.

关键词： Infrared imaging

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RGB image Guided Depth Hole-Filling Using Bidirectional Attention Mechanism 1

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17th International conference on Intelligent Information Hiding and Multimedia Signal processing (IIH-MSP) / 14th International conference on Frontiers of Information Technology, Applications and Tools (FITAT)

作者： Chen, Anran Zhao, Yao Lin, Chunyu Beijing Jiaotong Univ Inst Informat Sci Beijing Key Lab Adv Informat Sci & Network Beijing 100044 Peoples R China

ISBN: (数字)9789811910531

ISBN: (纸本)9789811910531;9789811910524

Depth maps obtained by commercial depth sensors are more likely to have missing values due to the occlusion effect, low-reflection objects, etc. Filling holes of depth maps is an important way to meet the demands of depth related computer vision tasks. In this paper, we propose an efficient end-to-end network that takes RGB image and mask of the hole to jointly guide the depth hole-filling. Previous algorithms indistinguishably treat valid pixels and holes, resulting in inaccurate depth values prediction and blurred boundaries. Nevertheless, the proposed algorithm uses the bidirectional attention mechanism which takes the surrounding valid values as the auxiliary information to focus on the process of depth hole-filling from edge to center. the proposed method achieves competitive performance on existing public datasets.

关键词： Bidirectional attention mechanism Depth map Hole-filling RGB guided

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A Parallel Framework for Object Detection and Recognition for Secure Vehicle Parking 17

A Parallel Framework for Object Detection and Recognition fo...

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2015 IEEE 17th International conference on High Performance Computing and Communications (HPCC)

作者： Mahmood, Zahid Khan, Muhammad Usman Shahid Jawad, Muhammad Khan, Samee U. Yang, Laurence T. North Dakota State Univ Dept Elect & Comp Engn Fargo ND 58102 USA St Francis Xavier Univ Dept Comp Sci Antigonish NS B2G 2W5 Canada

ISBN: (纸本)9781479989379

image analytics, biometrics access control, security, and surveillance applications utilize complex machine learning and computer vision algorithms, such as face detection and recognition. Speedup and accuracy are two important factors that need to be addressed in all such complex applications. Parallel computing breaks down the complex tasks into discrete fragments to be solved concurrently on multiple processors. the parallel computing procedure significantly reduces the execution time with improved speedup. this paper presents a parallel framework for object detection and recognition for a secure vehicle parking. the proposed framework is divided in to three steps: (1) vehicle detection at the parking entry junction, (2) driver's face detection, and (3) identification of driver's face from the huge database of stored facial images. On successful identification of authorized person, vehicle is allowed to enter in the parking-lot. the adaptive boosting algorithm is used for vehicle and face detection, while Eigenfaces based approach is employed for face recognition. Moreover, the scalability comparison of parallely executed driver face recognition algorithm indicates high speedup compared to serial execution. Furthermore, the results of the proposed framework reveal promising performance and encourage outcomes to be deployed in real-time at entrance/exits of the public/private vehicle parking areas.

关键词： Eigenfaces Face Recognition Haar Classifiers Parallel processing

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Fast three step search algorithm with minimum checking points

Fast three step search algorithm with minimum checking point...

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Proceedings of the 1998 17th conference on Consumer Electronics

作者： Kim, Jong-Nam Choi, Tae-Sun Kwangju Inst of Science and Technology Kwangju Korea Republic of

the three step search (TSS) has been used for real time video processing and low bit-rate video communications because of reduced computations, simplicity, and reasonable performance in the motion estimation. Other modified algorithms of TSS have been studied for speed of computation and the performance of motion vector estimation. this paper proposes a new algorithm (Fast three Step Search) for more efficient reduction of computational complexity. It is shown that the proposed algorithm (FTSS) is very efficient in computation while keeping the reasonable performance.

关键词： image communication systems

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