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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4351-4360 订阅

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A Comparative Analysis of Implementation Performances for image processing Applications Used to Control Robotic Arms

A Comparative Analysis of Implementation Performances for Im...

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IEEE International conference on Environment and Electrical Engineering (EEEIC) / IEEE Industrial and Commercial Power systems Europe (ICPS Europe)

作者： Ricman, Radu-Stefan Szabo, Roland Gontean, Aurel Politehn Univ Timisoara Appl Elect Dept ETcTI Timisoara Romania

ISBN: (纸本)9781538651865

This paper presents an analysis of code implementation performance for image processing algorithms. The test is made for image processing algorithms for robotic arms, but it is suitable for any type of image processing software. image processing software can use quite big amount of resources, so this way it can be tested which platform, which operating system or which programming language is the most suitable for usage. The image processing task is a color detection task with some line and circle overlays for robotic arm's guidance. The implementations were tested base on code line numbers, code size on disk, binary file size on disk, used memory during execution and used CPU during execution.

关键词： code execution code size image processing algorithms performance comparison resource utilization robotic arm control software implementation

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14th International conference on Hybrid Artificial Intelligence systems, HAIS 2019

14th International Conference on Hybrid Artificial Intellige...

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14th International conference on Hybrid Artificial Intelligence systems, HAIS 2019

ISBN: (纸本)9783030298586

The proceedings contain 64 papers. The special focus in this conference is on Hybrid Artificial Intelligence systems. The topics include: Particle Swarm Optimization-Based CNN-LSTM Networks for Anomalous Query Access Control in RBAC-Administered Model;d(Tree)-by-dx: Automatic and Exact Differentiation of Genetic Programming Trees;genetic Algorithm-Based Deep Learning Ensemble for Detecting Database Intrusion via Insider Attack;an Efficient Hybrid Genetic Algorithm for Solving a Particular Two-Stage Fixed-Charge Transportation Problem;Analysis of MOEA/D Approaches for Inferring Ancestral Relationships;parsimonious Modeling for Estimating Hospital Cooling Demand to Reduce Maintenance Costs and Power Consumption;haploid Versus Diploid Genetic algorithms. A Comparative Study;entropy and Organizational Performance;a Semi-supervised Method to Classify Educational videos;generating a Question Answering System from Text Causal Relations;new Approach for the Aesthetic Improvement of images Through the Combination of Convolutional Neural Networks and Evolutionary algorithms;evaluating Strategies for Selecting Test Datasets in Recommender systems;use of Natural Language processing to Identify Inappropriate Content in Text;classification of Human Driving Behaviour images Using Convolutional Neural Network Architecture;building a Classification Model Using Affinity Propagation;clustering-Based Ensemble Pruning and Multistage Organization Using Diversity;towards a Custom Designed Mechanism for Indexing and Retrieving video Transcripts;active image Data Augmentation;a Novel Density-Based Clustering Approach for Outlier Detection in High-Dimensional Data;Convolutional CARMEN: Tomographic Reconstruction for Night Observation;Creation of a Distributed NoSQL Database with Distributed Hash Tables;A Proof of Concept in Multivariate Time Series Clustering Using Recurrent Neural Networks and SP-Lines;On the Influence of Admissible Orders in IVOVO.

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Reducing dimension of parametric space based on the approximation of components for interpolation of multidimensional signals 5

Reducing dimension of parametric space based on the approxim...

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

作者： Gashnikov, M.V. Samara National Research University Moskovskoe Shosse 34A Samara443086 Russia Image Processing Systems Institute of RAS-Branch of the FSRC Crystallography and Photonics RAS Molodogvardejskaya street 151 Samara443001 Russia

In this paper, we address the problem of parametric space dimension reduction in the interpolation of multidimensional signals task. We develop adaptive parameterized interpolation algorithms for multidimensional signals. We perform a dimension reduction of the parameter space to reduce the complexity of optimizing such algorithms. The dependences of the samples inside the signal sections and between the signal sections are taken into account in various ways to reduce the dimension. We consider the dependencies between the signal sections through the approximation algorithm for the sections. We take into account the sample dependencies inside sections due to an adaptive parameterized interpolation algorithm. As a result, we solve the optimization problem of an adaptive interpolator in the parameter space of lower dimension for each signal section separately. To study the effectiveness of adaptive interpolators, we perform computational experiments using real-world multidimensional signals. Experimental results showed that the proposed interpolator improves the efficiency of the compression method up to 10% compared with the prototype algorithm. © Published under licence by IOP Publishing Ltd.

关键词： Interpolation

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Optimizing Schedule Length for DAG Type Applications with Energy Consumption Constraint in Heterogeneous Computing systems

Optimizing Schedule Length for DAG Type Applications with En...

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Symposium of image, Signal processing, and Artificial vision (STSIVA)

作者： Yu He Jinchao Chen Chenglie Du Pengcheng Han Northwestern Polytechnical University Xi’an China

ISBN: (数字)9781728170862

ISBN: (纸本)9781728170879

Schedule length minimization of a parallel application with precedence constrained tasks in heterogenous computing systems is always a research hotspot of distributed computing. Unfortunately, accelerating the execution of tasks requires faster processors and consequently produces more energy. Users are faced to a time-energy tradeoff in selecting processors and execution frequencies for tasks. A common way to address this trade-off is to minimize schedule length under energy constraints. Up to now only a few works have been done to address this problem. Nevertheless, the energy preassignment methods of these works are unfair for low priority tasks in various degrees. Furthermore, existing works pay little attention to the priority assignment strategy, which can be critical for task scheduling. Hence, we proposed a novel approach named DAG Scheduling with Energy Consumption Constraint (DSECC) to solve this problem. DSECC combines several priority assignment strategies to assign priorities for tasks, and preassigns energy for tasks based on the average energy consumption of tasks on different processors. A large number of experiments based on randomly generated DAG show that compared against state-of-the-art algorithms, our proposed algorithm can obtain smaller schedule length and generate less energy consumption.

关键词： Schedules Energy consumption Time-frequency analysis Program processors Scheduling algorithms Heterogeneous networks Task analysis

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Optimized algorithm for processing hyperspectral push-broom data from multiple sources 25

Optimized algorithm for processing hyperspectral push-broom ...

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25th SPIE conference on algorithms, Technologies, and Applications for Multispectral and Hyperspectral imagery

作者： Illmann, Raik Rosenberger, Maik Notni, Gunther Tech Univ Ilmenau Fac Mech Engn Dept Qual Assurance & Ind Image Proc Ilmenau Germany

ISBN: (纸本)9781510626386

Handling big measurement data is increasingly developing into a task with high requirements regarding efficiency and ensuring unaltered accuracy. Especially in the framework of spectral measurement, there is a rapidly increasing demand caused by rising availability of constantly improved sensor systems with better resolution and sensitivity. By using spectral imaging, an object can be measured including its spectral and spatial information. Respecting the claims on information given by a measurement result, for example from the field of quality assurance, the experimental setup that is used in this work should reach a spectral range from 220 nm up to 1700 nm. Because of technical limitations, more than one push-broom imaging system is necessary for measuring in such a wide spectral range. This paper deals about the evaluation and the further work concerning the techniques for matching spectral cubes acquired by different sources. It ties in with previous work, which laid down the fundamental ideas for handling those special kinds of big data sets. The developed algorithm is able to handle hyperspectral data from a multiple push-broom imaging system and the integrated calibration strategy ensures the correction with respect to geometric and chromatic aberrations. Further on, the experimental data will be compared to find the promising approach, depending on the case of application. A short survey of the analysis is also included and a simple idea for decreasing the effect of motion blur based on wavelet transformation was realized as well. The paper closes a chapter of investigations for merging spectral cubes, acquired by a multiple imaging prototype system with an efficient result.

关键词： Hyperspectral data spectral cube push-broom imaging calibration procedures multiple imaging systems

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Neural Learning Based Blind Source Separation for Detection of Material Defects in Pulsed Thermography images

Neural Learning Based Blind Source Separation for Detection ...

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IEEE International conference on Electro-Information Technology

作者： Xin Zhang Jafar Saniie Alexander Heifetz ECASP Research Laboratory Illinois Institute of Technology Chicago IL U.S.A. Nuclear Science and Engineering Division Argonne National Laboratory Lemont IL U.S.A.

ISBN: (数字)9781728153179

ISBN: (纸本)9781728153186

In this paper, we introduce a neural learning-based approach to blind source separation for detection of material flaws in pulsed thermography (PT) images. This approach can be used to detect internal defects (pores) in metallic Additively Manufactured (AM) materials. Such defects occur in high-strength alloys produced with direct laser sintering AM method for nuclear energy applications. Pulsed thermal imaging system utilizes a high intensity flash lamp to rapidly heat surface of sample, a high sensitivity infrared camera to capture data of surface temperature variations. The data cube obtained with PT (stack of surface temperature images at different times) can be analyzed with image processing algorithms to detect material defects. Compared with conventional nondestructive evaluation (NDE) methods, such as digital radiography and ultrasonic testing, the PT pulsed infrared thermal detection technique has advantages of one-sided noncontact measurements, fast processing of large sample areas captured in one image. Detection of small material defects requires finding features in thermal images, which have signal contrast levels approaching sensitivity limit of IR camera. In this study, an optimized Neural Learning based Blind Source Separation (NLBSS) algorithm, including Principal Component Analysis (PCA), and Independent Component Analysis (ICA) is demonstrated to automatically extract principal temporal and spatial features of thermography frames to enhance flaw detection. By using the NLBSS algorithm, material internal defects can be automatically detected. Furthermore, this processing approach compensates for experimental thermal imaging artifacts, such as noise and uneven heating. By merging artificial intelligence with the photothermic, the NDE system detects internal calibrated defects of various sizes and depths in AM nuclear-grade metallic alloys.

关键词： Surface treatment Principal component analysis Cameras Feature extraction Heating systems Blind source separation Matrix decomposition

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Design and Implementation of a Learning Emotion Recognition System 6th

Design and Implementation of a Learning Emotion Recognition ...

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6th International conference on Frontier Computing, FC 2018

作者： Lin, Kuan-Cheng Sue, Li-Chun Hung, Jason C. Department of Management Information Systems National Chung-Hsing University Taichung Taiwan Department of Information Technology Overseas Chinese University Taichung Taiwan

ISBN: (纸本)9789811336478

The purpose of this study was to construct an instant and short-term immediate learning emotion prediction model based on the facial expression that automatically evaluates learners’ learning activities. First, the "Learning Emotional Facial image Database" was built as the training data to build the emotion classification model for the teaching and learning activity. The "Learning Emotional Facial image Database" now has 3211 facial images and labels learning emotions. Then facial features were captured by the image processing technique and compute the action units. Two kinds of input features, feature value and action units, are used to build prediction model. Finally, three supervised machine learning algorithms were used: Support Vector Machine, Multi-layer Perceptron, and Random Forest to link the relation between facial features and learning emotions. In this study, the Random Forest algorithm has the highest learning emotion recognition rate, 88.20%, with the feature value as the input vector. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Emotion Recognition

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Unsupervised Detection of Sub-pixel Objects in Hyper-spectral images via Diffusion Bases 11

Unsupervised Detection of Sub-pixel Objects in Hyper-spectra...

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11th International Joint conference on Computational Intelligence (IJCCI)

作者： Schclar, Alon Averbuch, Amir Acad Coll Tel Aviv Yaffo Sch Comp Sci POB 8401 IL-61083 Tel Aviv Israel Tel Aviv Univ Sch Comp Sci POB 39040 IL-69978 Tel Aviv Israel

ISBN: (纸本)9789897583841

Sub-pixel objects are defined as objects which due to their size and due to the resolution of the camera occupy a fraction of a pixel or partially span adjacent pixels. Unsupervised detection of sub-pixel objects can be highly useful in areas such as medical imaging, and surveillance, to name a few. Hyper-spectral images offer extensive intensity information by describing a scene at hundreds and even thousands of wavelengths. This information can be utilized to obtain better sub-pixel detection results compared to those that are obtained using RGB images. Usually, only a small number of wavelengths contain the information that is required for the detection. Furthermore, the intensity images of many wavelengths are noisy and contain very little information. Accordingly, hyper-spectral images must be pre-processed first in order to extract the information that is needed for the sub-pixel detection. This extraction process produces an image where each pixel is represented by a small number of features which allows the application of fast and efficient detection algorithms. In this paper we propose the Diffusion Bases (DB) dimensionality reduction algorithm in order to derive the essential features for the sub-pixel detection. The effectiveness of the DB algorithm facilitates the application of a very simple algorithm for the detection of sub-pixel objects in the feature space. The proposed approach does not assume any distribution of the background pixels. We demonstrate the proposed framework for the detection of cardboard objects in airborne hyper-spectral images of a desert terrain.

关键词： image processing Subpixel Segmentation Anomaly Detection Unsupervised Sub-pixel Detection Diffusion Bases Dimensionality Reduction Hyper-spectral Sensing

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2nd Computational Methods in systems and Software, CoMeSySo 2018

2nd Computational Methods in Systems and Software, CoMeSySo ...

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2nd Computational Methods in systems and Software, CoMeSySo 2018

ISBN: (纸本)9783030002107

The proceedings contain 64 papers. The special focus in this conference is on Methods in systems and Software. The topics include: Influence analysis of selected factors in the function point work effort estimation;The performance evaluation for the efficiency of coastal regional innovation network based on DEA;models and algorithms of vector optimization in selecting security measures for higher education institution’s information learning environment;implementing DevOps in legacy systems;development of sectoral intellectualized expert systems and decision making support systems in cybersecurity;optimal multi-robot path finding algorithm based on A*;an agent-architecture for automated decision-making on the semantic web;An efficient hardware realization of DCT based color image mosaicing system on FPGA;semantic history: Ontology-based modeling of users’ web browsing behaviors for improved web page revisitation;a novel template - Based data structurization scheme for normalizing and analyzing medical data;a recovery technique for the fog-computing-based information and control systems;an examination of message ferry movement for delay tolerant networks;Comparison of ERP systems with blockchain platform;image content protection using hybrid approach of blocking and embedding algorithm in the context of social network;Calculation of robustly stabilizing PI controllers for linear time-invariant systems with multiplicative uncertainty;Data extraction from the distorted DCF77 signal captured using low-cost receivers;a mobile terrestrial surveillance robot using the wall-following technique and a derivative integrative proportional controller;BrainMRI enhancement as a pre-processing: An evaluation framework using optimal gamma, homographic and DWT based methods;efficiency comparison of modern computer languages: Sorting benchmark.

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2nd Computational Methods in systems and Software, CoMeSySo 2018

2nd Computational Methods in Systems and Software, CoMeSySo ...

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2nd Computational Methods in systems and Software, CoMeSySo 2018

ISBN: (纸本)9783030001834

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