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

共 12675 条记录，以下是4281-4290 订阅

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Multiple Kernel Fuzzy Discriminant Analysis for Hyperspectral Imaging Classification

Multiple Kernel Fuzzy Discriminant Analysis for Hyperspectra...

引用

IEEE International conference on Fuzzy systems (FUZZ-IEEE)

作者： Zeng, Shan Bai, Jun Jiang, Liang Kang, Zhen Wuhan Polytech Univ Coll Math & Comp Sci Wuhan Hubei Peoples R China Wuhan Text Univ Coll Math & Comp Sci Wuhan Hubei Peoples R China

ISBN: (纸本)9781509060344

The classical fuzzy discriminant analysis with kernel methods (KFDA) is an effective method of solving nonlinearity pattern analysis problem. In some complicated cases, the kernel machine constituted by a single kernel function is not able to meet some practical application requirements, such as hetero-geneous information or unnormalised data, non-flat distribution of samples, etc. By searching for an appropriate linear combination of base kernel functions or matrices, multiple kernel learning (MKL) is able to improve the performance in some extent. So it is a necessary choice to introduce multiple kernel learning into KFDA in order to get better results. In this study, multiple kernel fuzzy discriminant analysis (MKFDA) is proposed. Our method obtains the projection matrix from fuzzy discriminant analysis with multiple kernel, and then feature extraction and classification are made based on the projection matrix. The experiment on the AVIRIS image was performed, and the results showed that the performance of fuzzy discriminant analysis with multiple kernels is better than that of fuzzy discriminant with single kernel for the Hyperspectral images' feature extraction and classification.

关键词： Estimation Field programmable gate arrays OFDM Modulation Filtration Clocks Signal processing algorithms

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Diagnostic mechanism and robustness of safety relevant automotive deep convolutional networks 30

Diagnostic mechanism and robustness of safety relevant autom...

引用

30th IEEE/CVF conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Robert, Krutsch Schlagenhaft, Rolf Intel Munich Germany NXP Munich Germany

ISBN: (纸本)9781538607336

Convolution Neural Networks today provide the best results for many image detection and image recognition problems. The computational complexity and the amount of parameters learned has increased, yet there is little to no research on the topic of functional safety for systems incorporating CNNs. The analysis on false detections due to random hardware faults concentrates on human made adversarial examples obtained by adding unrealistic noise sources over carefully selected images. Redundant execution of these networks is prohibitive in application domains where power and price constraints dominate, pushing for alternate approaches. In this paper we investigate functional safety aspects for a road labeling application, a common task in the advance driver assistance systems. We introduce computationally light safety checks that reduce the error space significantly, train a CNN on the Cityscape dataset that reaches 93% mean IU (intersection over union) and use Monte Carlo simulations to assess the impact of single event upset random hardware faults. The results show that the networks based on convolution and ReLU (rectified linear unit) have some intrinsic robustness and that together with additional constraints strong function safety claims can be made. We compare also the diagnostic coverage between floating point and fixed point implementation of CNNs and summarize key safety features needed to achieve a high diagnostic coverage.

关键词： Estimation Field programmable gate arrays OFDM Modulation Filtration Clocks Signal processing algorithms

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FRACTAL SPECKLE image ANALYSIS FOR SURFACE CHARACTERIZATION OF AEROSPACE STRUCTURES 5

FRACTAL SPECKLE IMAGE ANALYSIS FOR SURFACE CHARACTERIZATION ...

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5th International conference on Optical and Photonics Engineering

作者： Haridas, Aswin Crivoi, Alexandru Prabhathan, P. Chan, Kelvin Murukeshan, V. M. Nanyang Technol Univ Rolls Royce NTU Corp Lab 65 Nanyang Dr Singapore 637460 Singapore Nanyang Technol Univ Sch Mech & Aerosp Engn 50 Nanyang Dr Singapore 639798 Singapore Rolls Royce Singapore 6 Seletar Aerosp Rise Singapore 797575 Singapore

ISBN: (数字)9781510613737

ISBN: (纸本)9781510613737;9781510613720

Surface characterization of the working components has always been a subject of interest among researchers and industry specialists. Especially in the aerospace industry where the aerodynamic capabilities are largely altered by the surface quality of the component of interest, there remains an extensive need for developing systems for effectively characterizing the surface quality. To realize an optical based non-contact and an in-line surface roughness measurement system, it is essential to understand the relationship between the quality of the surface and statistical parameter of the reflected speckles. The range of the measurement system being proportional to the wavelength of light used makes the analysis fundamentally important in order to understand the properties of speckles at a different wavelength. In this context, this paper examines the nature of the formed IR speckles from three different diffusers by analyzing their raw structure. image processing algorithms that are developed study the different parameters of the 8-bit binary speckles, namely, the fractal property and number of connecting components. The paper also discusses the future work direction on relating the proposed analysis to derive the algorithm required for evaluating the surface finish parameters.

关键词： image processing Fractal dimension Infra-Red Surface Roughness

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image-based Visual Perception and Representation for Collision Avoidance 30

Image-based Visual Perception and Representation for Collisi...

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30th IEEE/CVF conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Cigla, Cevahir Brockers, Roland Matthies, Larry CALTECH Jet Prop Lab Pasadena CA 91125 USA

ISBN: (纸本)9781538607336

We present a novel on-board perception system for collision avoidance by micro air vehicles (MAV). An egocentric cylindrical representation is utilized to model the world using forward-looking stereo vision. This efficient representation enables a 360 degrees field of regard, as the vehicle moves around and disparity maps are fused temporally on the cylindrical map. For this purpose, we developed a new Gaussian Mixture Models-based disparity image fusion algorithm, with an extension to handle independently moving objects (IMO). The extension improves scene models in case of moving objects, where standard temporal fusion approaches cannot detect movers and introduce errors in world models due to the common static scene assumption. The on-board implementation of the vision pipeline provides disparity maps on a 360 degrees egocentric cylindrical surface at 10 Hz. The perception output is used in our system by real-time motion planning with collision avoidance on the MAV.

关键词： Estimation Field programmable gate arrays OFDM Modulation Filtration Clocks Signal processing algorithms

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image based method for aberration measurement of lithographic tools

Image based method for aberration measurement of lithographi...

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International conference on Optical Instruments and Technology (OIT) at the Topical Meeting on Optoelectronic Imaging/Spectroscopy and Signal processing Technology

作者： Xu, Shuang Tao, Bo Guo, Yongxing Li, Gongfa Wuhan Univ Sci & Technol Key Lab Met Equipment & Control Technol Minist Educ Wuhan 430081 Hubei Peoples R China Wuhan Univ Sci & Technol Hubei Key Lab Mech Transmiss & Mfg Engn Wuhan 430081 Hubei Peoples R China

ISBN: (数字)9781510617520

ISBN: (纸本)9781510617520;9781510617513

Information of lens aberration of lithographic tools is important as it directly affects the intensity distribution in the image plane. Zernike polynomials are commonly used for a mathematical description of lens aberrations. Due to the advantage of lower cost and easier implementation of tools, image based measurement techniques have been widely used. Lithographic tools are typically partially coherent systems that can be described by a bilinear model, which entails time-consuming calculations and does not lend a simple and intuitive relationship between lens aberrations and the resulted images. Previous methods for retrieving lens aberrations in such partially coherent systems involve through-focus image measurements and time-consuming iterative algorithms. In this work, we propose a method for aberration measurement in lithographic tools, which only requires measuring two images of intensity distribution. Two linear formulations are derived in matrix forms that directly relate the measured images to the unknown Zernike coefficients. Consequently, an efficient non-iterative solution is obtained.

关键词： optical lithography lens aberration partially coherent imaging aberration measurement non-iterative method

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Convolutional Neural Network Based Localized Classification of Uterine Cervical Cancer Digital Histology images

Convolutional Neural Network Based Localized Classification ...

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Complex Adaptive systems conference on Engineering Cyber Physical systems (CAS)

作者： Almubarak, Haidar A. Stanley, R. Joe Long, Rodney Antani, Sameer Thoma, George Zuna, Rosemary Frazier, Shelliane R. Missouri Univ Sci & Technol Dept Elect & Comp Engn Rolla MO 65401 USA NIH Lister Hill Nationa Ctr Biomed Commun DHHS Natl Lib Med Bldg 10 Bethesda MD 20892 USA Univ Oklahoma Hlth Sci Ctr Dept Pathol Oklahoma City OK 73117 USA Univ Missouri Hosp & Clin Dept Surg Pathol Columbia MO 65202 USA

In previous research, we introduced an automated localized, fusion-based algorithm to classify squamous epithelium into Normal, CIN1, CIN2, and CIN3 grades of cervical intraepithelial neoplasia (CIN). The approach partitioned the epithelium into 10 segments. image processing and machine vision algorithms were used to extract features from each segment. The features were then used to classify the segment and the result was fused to classify the whole epithelium. This research extends the previous research by dividing each of the 10 segments into 3 parts and uses a convolutional neural network to classify the 3 parts. The result is then fused to classify the segments and the whole epithelium. The experimental data consists of 65 images. The proposed method accuracy is 77.25% compared to 75.75% using the previous method for the same dataset. (C) 2017 The Authors. Published by Elsevier B.V.

关键词： Cervical Cancer Convolutoin Neural Networks Data Fusion image classifcation Clinical Decision Support systems

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Corona discharge classification based on UAV data acquisition 21

Corona discharge classification based on UAV data acquisitio...

引用

21st International conference on Control systems and Computer Science (CSCS)

作者： Vidan, Cristian Maracine, Mihaela Mil Tech Acad Fac Mechatron & Integrated Armament Syst Aircraft Integrated Syst & Mech Dept Bucharest Romania Univ Politehn Bucuresti Fac Automat Control & Comp Comp Sci Dept Bucharest Romania

ISBN: (纸本)9781538618394

The Corona discharge represents one of the main concerns of the century in the field of electricity and powerline design, as it is both harmful for the environment as well as very costly to cover for the voltage losses on the transport. In this paper, we propose the design for a measurement and analysis platform for this phenomenon that can estimate the Corona discharge losses based on a video stream coming from an electro-optical sensor placed on board of an Unmanned Aerial Vehicle (UAV). After experimenting on a dataset generated from Corona discharge videos, our results suggest that of the supervised learning algorithms taken into consideration those based on Radial-Basis approaches are the most effective in classifying the information being fed to it. Before the classification task, we propose a starter set of filtering operation, composed of grayscale reduction, mean blurring and binary transformation.

关键词： Corona discharge UAV data classification data acquisition image processing machine learning

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Support Vector Machine Optimized by Firefly Algorithm for Emphysema Classification in Lung Tissue CT images 25

Support Vector Machine Optimized by Firefly Algorithm for Em...

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25th International conference on Central Europe on Computer Graphics, Visualization and Computer Vision (WSCG)

作者： Tuba, Eva Tuba, Milan Simian, Dana Univ Belgrade Fac Math Studentski Trg 16 Belgrade 11000 Serbia John Naisbitt Univ Fac Comp Sci Bulevar Umetnosti 29 Belgrade 11070 Serbia Lucian Blaga Univ Fac Sci Ion Ratiu St 5-7 Sibiu 550012 Romania

ISBN: (纸本)9788086943503

Digital images and digital image processing facilitated significant progress in numerous areas where medicine is an important one of them. Computer-aided detection and diagnostics systems are used to assist specialists in interpretation of medical digital images. One of the important research issues is detection and classification of the chronic obstructive pulmonary disease in lung CT images In this paper we proposed a method for emphysema classification based on texture and intensity features. Only six different characteristics of the uniform local binary pattern and intensity histogram were used as input vector for support vector machine that was used as classifier. Feature vector was significantly reduced compared to the other state-of-the-art methods while the classification accuracy was increased. On images from standard dataset global accuracy of our proposed algorithm was 98.18% compared to 95.24% and 93.9% of two other compared algorithms.

关键词： Support vector machines lung tissue classification CT images image processing firefly algorithm swarm intelligence

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A method of object recognition for single pixel imaging

A method of object recognition for single pixel imaging

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International conference on Optical Instruments and Technology (OIT) at the Topical Meeting on Optoelectronic Imaging/Spectroscopy and Signal processing Technology

作者： Li, Boxuan Zhang, Wenwen Nanjing Univ Sci & Technol Jiangsu Key Lab Spectral Imaging & Intelligent Se Nanjing Jiangsu Peoples R China

ISBN: (数字)9781510617520

ISBN: (纸本)9781510617520;9781510617513

Computational ghost imaging(CGI), utilizing a single-pixel detector, has been extensively used in many fields. However, in order to achieve a high-quality reconstructed image, a large number of iterations are needed, which limits the flexibility of using CGI in practical situations, especially in the field of object recognition. In this paper, we purpose a method utilizing the feature matching to identify the number objects. In the given system, approximately 90% of accuracy of recognition rates can be achieved, which provides a new idea for the application of single pixel imaging in the field of object recognition

关键词： Imaging systems Computational imaging image recognition algorithms and filter

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Unsupervised Segmentation of Nonstationary Data using Triplet Markov Chains 19

Unsupervised Segmentation of Nonstationary Data using Triple...

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19th International conference on Enterprise Information systems (ICEIS)

作者： Boudaren, Mohamed El Yazid Monfrini, Emmanuel Bey, Kadda Beghdad Habbouchi, Ahmed Pieczynski, Wojciech Ecole Mil Polytech POB 17 Algiers 16111 Algeria Univ Paris Saclay Telecom SudParis SAMOVAR CNRS F-91011 Evry France

ISBN: (纸本)9789897582479

An important issue in statistical image and signal segmentation consists in estimating the hidden variables of interest. For this purpose, various Bayesian estimation algorithms have been developed, particularly in the framework of hidden Markov chains, thanks to their efficient theory that allows one to recover the hidden variables from the observed ones even for large data. However, such models fail to handle nonstationary data in the unsupervised context. In this paper, we show how the recent triplet Markov chains, which are strictly more general models with comparable computational complexity, can be used to overcome this limit through two different ways: (i) in a Bayesian context by considering the switches of the hidden variables regime depending on an additional Markov process;and, (ii) by introducing Dempster-Shafer theory to model the lack of precision of the hidden process prior distributions. which is the origin of data nonstationarity. Furthermore, this study analyzes both approaches in order to determine which one is better-suited for nonstationary data. Experimental results are shown for sampled data and noised images.

关键词： Data Segmentation Hidden Markov Chains Nonstationary Data Signal processing Triplet Markov Chains

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