检索结果-内蒙古大学图书馆

23rd Seminar on Current Trends in Theory and Practice of Informatics, SOFSEM 1996

作者： Werner, Tomáš Hlaváč, Václav Leonardis, Aleš Pajdla, Tomáš Czech Technical University Center for Machine Perception Karlovo namesti 13 Prague 212135 Czech Republic Technical University of Vienna Institute for Image Processing and Pattern Recognition Treitlstrasse 3 WienA-1040 Austria University of Ljubljana Slovenia

ISBN: (纸本)3540619941

Recently, much attention has been devoted to image-based scene representations. They allow to construct an arbitrary view of a 3D scene by the interpolation (transfer) from a sparse set of real 2-D (reference) images, rather than by rendering an explicit 3-D model. While many authors address mainly the purely geometrical aspect of the task, we focus on the problem, how to select the optimal set of reference views. Selection of reference views from a dense set of real primary views is posed as a selection and fitting of parametric models. The selected set must minimize a weighted sum of the number of reference views and the total fit error. We propose two different algorithms solving this optimization problem. The experimental results on synthetic and real data indicate the feasibility of the approach for 1-DOF camera movement. We discuss the possibility to extend one of the algorithms for more general case. © 1996, Springer-Verlag. All rights reserved.

关键词： Three dimensional computer graphics

来源：评论

学校读者我要写书评

暂无评论

Server-Side Prediction of Source IP Addresses Using Density Estimation

Server-Side Prediction of Source IP Addresses Using Density ...

引用

International Conference on Availability, Reliability and Security, ARES

作者： Markus Goldstein Matthias Reif Armin Stahl Thomas Breuel Research Group Image Understanding and Pattern Recognition (IUPR) German Research Center for Artificial Intelligence Kaiserslautern Germany Research Group Image Understanding and Pattern Recognition (IUPR) Technical University of Kaiserslautern Kaiserslautern Germany

Source IP addresses are often used as a major feature for user modeling in computer networks. Particularly in the field of distributed denial of service (DDoS) attack detection and mitigation traffic models make extensive use of source IP addresses for detecting anomalies. Typically the real IP address distribution is strongly undersampled due to a small amount of observations. Density estimation overcomes this shortage by taking advantage of IP neighborhood relations. In many cases simple models are implicitly used or chosen intuitively as a network based heuristic. In this paper we review and formalize existing models including a hierarchical clustering approach first. In addition, we present a modified k-means clustering algorithm for source IP density estimation as well as a statistical motivated smoothing approach using the Nadaraya-Watson kernel-weighted average. For performance evaluation we apply all methods on a 90 days real world dataset consisting of 1.3 million different source IP addresses and try to predict the users of the following next 10 days. ROC curves and an example DDoS mitigation scenario show that there is no uniformly better approach: k-means performs best when a high detection rate is needed whereas statistical smoothing works better for low false alarm rate requirements like the DDoS mitigation scenario.

关键词： Computer crime Traffic control Telecommunication traffic pattern recognition Predictive models Smoothing methods Machine learning Quality of service Routing Density functional theory

来源：评论

学校读者我要写书评

暂无评论

An improved image segmentation algorithm based on MRF and Sobel operator 12

An improved image segmentation algorithm based on MRF and So...

引用

SPIE 12th International Symposium on Multispectral image processing and pattern recognition, MIPPR 2023

作者： Gao, Yao Tian, Keyun Zhu, Cheng Hubei Key Laboratory of Optical Information and Pattern Recognition China Hubei Engineering Research Center of Video Image and HD Projection China School of Electrical and Information Engineering Wuhan Institute of Technology Wuhan430205 China

ISBN: (数字)9781510674943

ISBN: (纸本)9781510674936

In response to the issues of over-segmentation, excessive noise, and suboptimal segmentation results commonly encountered in existing image segmentation algorithms based on Markov Random Fields (MRF), this paper proposes an enhanced image segmentation algorithm that integrates MRF with the Sobel operator. The algorithm begins by performing an initial segmentation of the image using a Markov Random Field (MRF)-based method. Subsequently, an enhanced Sobel operator is employed to eliminate noise points and extract fine edge details from the image. Finally, the segmentation result is refined through pixel-wise operations with the edge detection result, resulting in the ultimate segmentation output. The evaluation of segmentation performance is conducted using the Dice coefficient and Mean Hausdorff Distance as assessment metrics. Through experimental analysis, the method in this paper can improve the segmentation effect of the traditional MRF segmentation algorithm, and has better performance and higher adaptivity. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： image segmentation

来源：评论

学校读者我要写书评

暂无评论

Photovoltaic panel string detection based on prior knowledge and feature learning 12

Photovoltaic panel string detection based on prior knowledge...

引用

SPIE 12th International Symposium on Multispectral image processing and pattern recognition, MIPPR 2023

作者： Li, Bolin Cheng, Bolin Ye, Liang Hubei Key Laboratory of Optical Information and Pattern Recognition China Hubei Engineering Research Center of Video Image and HD Projection China School of Electrical and Information Engineering Wuhan Institute of Technology Wuhan430205 China

ISBN: (数字)9781510674943

ISBN: (纸本)9781510674936

image-based photovoltaic panel inspection has become one of the important tasks of photovoltaic power generation. Due to the repeated patterns of photovoltaic string numbers and different specifications, it is easy to cause false detection by traditional template matching or model training methods. In order to solve this problem, this paper proposes a photovoltaic panel string detection method based on prior knowledge and feature learning. First, the infrared inspection image is segmented for the first time by using the difference in radiation characteristics between the photovoltaic string and the environmental background. Subsequently, according to the local features of the border around the PV string template image, feature matching is performed in the segmented foreground area, and then determine the exact position of the string through the closure and integrity of the matching area. Experiments show that this method has high detection accuracy and has good adaptability to complex and diverse string specifications. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Inspection

来源：评论

学校读者我要写书评

暂无评论

Defect detection of photovoltaic panel based on morphological segmentation 12

Defect detection of photovoltaic panel based on morphologica...

引用

SPIE 12th International Symposium on Multispectral image processing and pattern recognition, MIPPR 2023

作者： Cheng, Bolin Li, Bolin Ye, Liang Hubei Key Laboratory of Optical Information and Pattern Recognition China Hubei Engineering Research Center of Video Image and HD Projection China School of Electrical and Information Engineering Wuhan Institute of Technology Wuhan430205 China

ISBN: (数字)9781510674943

ISBN: (纸本)9781510674936

The automatic inspection of photovoltaic panels based on infrared images is one of the important tasks in the daily maintenance of photovoltaic panels in photovoltaic power plants. In this paper, a defect detection method of infrared thermal image photovoltaic panel based on morphological segmentation is proposed. First of all, according to the infrared characteristics of the photovoltaic plant station and the morphological characteristics of the photovoltaic panel, the accurate region of each photovoltaic panel is determined in the inspection image. Then, statistical method is used for feature analysis to determine whether there are defects in photovoltaic panels, and then the defective photovoltaic panels are segmented and different defects are classified according to morphological characteristics, and finally the defect detection and classification of photovoltaic panels are realized. Through the experiment in the actual inspection image of photovoltaic plant station, it is verified that the proposed method has a high accuracy of defect detection and recognition. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Morphology

来源：评论

学校读者我要写书评

暂无评论

Rao-Blackwellised extended Kalman filter with correlated noises

引用

Journal of Information and Computational Science 2013年第10期10卷 3117-3124页

作者： Hu, Zhentao Fu, Chunling Institute of Image Processing and Pattern Recognition Henan University Kaifeng 475004 China Basic Experiment Teaching Center Henan University Kaifeng 475004 China

Aiming at the adverse influence of correlation between measurement noise and process noise for filtering precision in nonlinear system estimation, Firstly, Rao-Blackwellised modeling technique is introduced to disassemble nonlinear and linear component of system state, and then Kalman filter and extended Kalman filter are used to realize estimation for them, respectively. On that basis, we construct the Rao-Blackwellised extended Kalman filter. Secondly, through rearrange the state transition function and make it independent between process noise and measurement noise, we give the decoupling method of correlated noises. Finally, combined with the Rao-Blackwellised extended Kalman filter and the decoupling method of correlated noises, a novel Rao-Blackwellised extended Kalman filter with correlated noises is proposed in this paper. The new method not only improves the filtering precision, but also decreases the calculated amount relative to standard extended Kalman filter, The theoretical analysis and experimental results show the efficiency of the proposed algorithm. Copyright © 2013 Binary Information Press.

关键词： Extended Kalman filters

来源：评论

学校读者我要写书评

暂无评论

A novel level set method for remote sensing image based on K-means

A novel level set method for remote sensing image based on K...

引用

2014 International Conference on Applied Mechanics, Mechatronics and Intelligent System, AMMIS 2014

作者： Xu, Er Jing Jia, Zhen Hong Wang, Lie Jun Hu, Ying Jie Yang, Jie College of Information science and engineering Xinjiang University Urumchi 830046 China Knowledge Engineering and Research Discovery Institute Auckland University of Technology Auckland New Zealand The Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai 200240 China

ISBN: (纸本)9783038351757

Due to the characteristic of remote sensing image, we propose a novel method based on K-means algorithm also with the improved multi-phrase level set model. Comparing with the classical multi-phase C-V model, the improved model considers the region area information, gradient information and edge detection. Proper use of gradient information can overcome the inaccurate edge localization defects in image segmentation. The edge detection is used for keeping the boundary information better in the evolution process. For the reason of picking up the contour's convergent speed and enable the avoidance of trap. Four stages are constructed. Firstly, a median filtering is applied to smooth the original image and reduce parts of noise. Secondly, the usage of K-means algorithm gains more obvious differences of characteristics. Next, the reconstruction of gradient is obtained by using Sobel operator. Finally, segmentation result is achieved by using an improved method of multi-phase level set image segmentation. Experimental results show that the proposed approach has advantages in rapid and efficient application of remote sensing image segmentation. © (2014) Trans Tech Publications, Switzerland.

关键词： image segmentation

来源：评论

学校读者我要写书评

暂无评论

Multi-sensor measurement adaptive filtering algorithm in measurement uncertainty

引用

Journal of Information and Computational Science 2012年第4期9卷 843-851页

作者： Hu, Zhentao Fu, Chunling Liu, Xianxing Laboratory of Image Processing and Pattern Recognition Henan University Kaifeng 475004 China Basic Experiment Teaching Center Henan University Kaifeng 475004 China

The reliable estimation of system state in multi-sensor uncertainty is always the hot and knotty issue of nonlinear filtering theory. Aiming to the reasonable utilization of measurement information, a novel multi-sensor measurement adaptive filtering algorithm is proposed in this paper. Firstly, Combining with the construction of measurement likelihood and maximize principle, the equivalent measurement are affirmed. Next, the selection and fusion of measurement without disturbance influence are accomplished by the introduction of statistic distance among the measurements, which can realize the optimization of equivalent measurement to some extend. The new method improves the adverse influence of disturbance in multi-sensor information fusion process, and retains the litter computational complexity relative to the distributed fusion structure. The theoretical analysis and experimental results show the efficiency of the proposed algorithm. Copyright © 2012 Binary Information Press.

关键词： Extended Kalman filters

来源：评论

学校读者我要写书评

暂无评论

Accurate extraction of line-structured optical stripe centerlines under low exposure 12

Accurate extraction of line-structured optical stripe center...

引用

SPIE 12th International Symposium on Multispectral image processing and pattern recognition, MIPPR 2023

作者： Zhu, Cheng Tian, Keyun Gao, Yao Ding, Zhiqiang Hubei Key Laboratory of Optical Information and Pattern Recognition China Hubei Engineering Research Center of Video Image and HD Projection China School of Electrical and Information Engineering Wuhan Institute of Technology Wuhan430205 China

ISBN: (数字)9781510674967

ISBN: (纸本)9781510674950

The line-structure light bar images acquired in industrial metal parts inspection may suffer from inaccurate extraction of the centerline of the line structure light bar due to the interference of ambient lighting and strong diffuse reflection and weak reflection of the part surface material. For this reason, this study proposes a method based on G-HL (Gauss-Hyper-Laplace Distribution) curve fitting to improve the center extraction accuracy of the line structure light bar in complex environments. First, the method uses Gaussian filtering to denoise the line-structured light image. Then, the laser line target pixel region is localized by finding the upper and lower boundaries of the line-structured light bar using the neighborhood gradient for each column of the line-structured light image, and the width of the line-structured light bar is calculated by the difference of the boundary coordinates, and the fitting interval is determined. Finally, within the fitting interval, the G-HL curve is used to fit the gray scale of the line-structured light cross-section to obtain the G-HL parameter, and the point of the maximum value of the G-HL curve is taken to be the coordinates of the center of the line-structured light. Experiments prove that the proposed method can accurately extract the center of the line structure light in low exposure. Compared with the methods such as the grayscale center of gravity method and the extreme value method, the proposed method has high accuracy and robustness. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Curve fitting

来源：评论

学校读者我要写书评

暂无评论

Texture analysis in the presence of speckle noise 12

Texture analysis in the presence of speckle noise

引用

12th Annual International Geoscience and Remote Sensing Symposium, IGARSS 1992

作者： Schistad, Anne H. Jain, Anil K. Norwegian Computing Center P.O. Box 114 Blindern Oslo 3N-0314 Norway Pattern Recognition and Image Processing Lab. Computer Science Department Michigan State University East LansingMI48824 United States

ISBN: (纸本)0780301382

We investigate the performance of selected texture models for the purpose of land use classification. The texture models are evaluated based on the resulting classification error rates. Three classes of texture models are evaluated: fractal models, lognormal random fields and grey level co-occurrence matrices. The effect of filtering and noise transformation is investigated. The lognormal random field model gives the best performance. © IEEE 1992.

关键词： Land use

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：