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检索条件"主题词=detection Algorithms"

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Massively Parallel Anomaly detection in Online Network Measurement

Massively Parallel Anomaly Detection in Online Network Measu...

引用

International Conference on Computer Communications and Networks (ICCCN)

作者： Shashank Shanbhag Tilman Wolf Department of Electrical and Computer Engineering University of Massachusetts Amherst MA USA

ISBN: (纸本)9781424423897

Detecting anomalies during the operation of a network is an important aspect of network management and security. Recent development of high-performance embedded processing systems allow traffic monitoring and anomaly detection in real-time. In this paper, we show how such processing capabilities can be used to run several different anomaly detection algorithms in parallel on thousands of different traffic subclasses. The main challenge in this context is to manage and aggregate the vast amount of data generated by these processes. We propose (1) a novel aggregation process that uses continuous anomaly information (rather than binary outputs) from existing algorithms and (2) an anomaly tree representation to illustrate the state of all traffic subclasses. Aggregated anomaly detection results show a lower false positive and false negative rate than any single anomaly detection algorithm.

关键词： detection algorithms Telecommunication traffic Signal processing algorithms Monitoring Prototypes Real time systems Electric variables measurement Predictive models Frequency Floods

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Automatic lattice detection in near-regular histology array images

Automatic lattice detection in near-regular histology array ...

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IEEE International Conference on Image Processing

作者： Brian A. Canada Georgia K. Thomas Keith C. Cheng James Z. Wang Yanxi Liu PA USA

Near-regular texture (NRT), denoting deviations from otherwise symmetric wallpaper patterns, is commonly observable in the real world. Existing lattice detection algorithms capture the underlying lattice of an NRT pattern and all of its individual texels, facilitating an automated analysis of NRT. Many real world images, as in those of zebrafish larval histology arrays, depart significantly from regularity and challenge the current state of the art wallpaper group theory-based lattice detection methods. We propose an alternative 2D lattice detection algorithm that exploits translation and reflection symmetries and specific imaging cues. By outperforming existing methods on histology array images, our algorithm leads us towards complete automation of high-throughput histological image processing while broadening the spectrum of NRT computation.

关键词： Lattices detection algorithms Biomedical imaging Microscopy Pattern analysis Algorithm design and analysis Reflection Automation Image processing Biology computing

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Curb detection Based on a Multi-Frame Persistence Map for Urban Driving Scenarios

Curb Detection Based on a Multi-Frame Persistence Map for Ur...

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International Conference on Intelligent Transportation

作者： Florin Oniga Sergiu Nedevschi Marc Michael Meinecke Computer Science Department Technical University of Cluj Napoca Romania Research Department Volkswagen AG Wolfsburg Germany

An approach for the detection of straight and curved curbs (border of relevant traffic isles, sidewalks, etc) is presented, in the context of urban driving assistance systems. A rectangular elevation map is built from 3D dense stereo data. Edge detection is applied to the elevation map in order to highlight height variations. We propose a method to reduce significantly the 3D noise from dense stereo, using a multiframe persistence me persistence map: temporal filtering is performed for edge points, based on the ego car motion, and only persistent points are validated. The Hough accumulator for lines is built with the persistent edge points. A scheme for extracting straight curbs (as curb segments) and curved curbs (as chains of curb segments) is proposed. Each curb segment is refined using a RANSAC approach to fit optimally the 3D data of the curb. The algorithm was evaluated in an urban scenario. It works in real-time and provides robust detection of curbs.

关键词： Roads Image edge detection Layout Intelligent transportation systems Filtering Vehicle detection Surface texture detection algorithms Noise reduction Data mining

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An Outlier detection Algorithm Based on Spectral Clustering

An Outlier Detection Algorithm Based on Spectral Clustering

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Pacific-Asia Workshop on Computational Intelligence and Industrial Application, PACIIA

作者： Peng Yang Biao Huang Chongqing University of Arts and Sciences Chongqing China

Outlier detection is widely used for many areas such as credit card fraud detection, discovery of criminal activities in electronic commerce, weather prediction and marketing. In this paper, we demonstrate the effectiveness of spectral clustering in dataset with outliers. Through spectral method we can use the information of feature space with eigenvectors rather than that of the whole dataset to obtain stable clusters. Then we introduce the cluster-based local outlier factor to identify and find the outliers in dataset. The experimental results show that our outlier detection algorithm outperforms the K-means based algorithm with high precision and low false alarm rate as well as desirable coverage ratio.

关键词： detection algorithms Clustering algorithms Credit cards Electronic commerce Weather forecasting Conferences Computational intelligence Computer industry Electronics industry Art

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Static text region detection in video sequences using color and orientation consistencies

Static text region detection in video sequences using color ...

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International Conference on Pattern Recognition

作者： Daehyun Kim Kwanghoon Sohn School of Electrical and Electronics Engineering Yonsei University South Korea

ISBN: (纸本)9781424421749

Motion compensated error of the static text in the frame rate conversion (FRC) is most annoying artifact because people cannot read it. In this paper, we present a novel static text region detection algorithm for preventing the motion compensation error in FRC. We use some consistent properties of the static text that the color of the text is spatio-temporally consistent and the orientation of the text boundary is preserved in consecutive frames. We observe whether each pixelpsilas consistency is preserved for several frames, and then we decide it as a static text. Our algorithm can not only perfectly extract the static text region but also easily be implemented in hardware because of its ease.

关键词： Video sequences Motion estimation Motion compensation Motion detection detection algorithms Image edge detection Training data TV Hardware Background noise

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detection of Ship Wakes in SAR Images Based on Radon Transform

Detection of Ship Wakes in SAR Images Based on Radon Transfo...

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9th International Conference on Signal Processing(第九届国际信号处理学术会议)(ICSP'08)

作者： Yewen Wu Junfeng Wang Department of Electronic Engineering Shanghai Jiaotong UniversityShanghai200240China

The Radon transform is widely used to detect ship wakes in Synthetic Aperture Radar (SAR) *** wakes have linear features in the image,and correspond to peaks and troughs in the Radon ***,the ship wakes can be detected by searching for peaks and troughs in the Radon *** this paper,a novel algorithm based on the Radon transform is presented to detect ship wakes in SAR *** the variation of the sea clutter,this algorithm uses a locally-adaptive method to search for peaks and troughs in the Radon *** addition,a maximum directional-derivative method is used to locate the starting points of the detected ship *** algorithm is tested on real SAR images,and the results demonstrate its effectiveness.

关键词： Marine vehicles Radar detection Synthetic aperture radar detection algorithms Oceans Clutter Sea surface Image sensors Automatic testing Displays

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Trajectory Outlier detection: A Partition-and-Detect Framework

Trajectory Outlier Detection: A Partition-and-Detect Framewo...

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IEEE 24th International Conference on Data Engineering (ICDE 2008), vol.1

作者： Jae-Gil Lee Jiawei Han Xiaolei Li Department of Computer Science University of Illinois Urbana IL USA

ISBN: (纸本)9781424418367;1424418364

Outlier detection has been a popular data mining task. However, there is a lack of serious study on outlier detection for trajectory data. Even worse, an existing trajectory outlier detection algorithm has limited capability to detect outlying sub-trajectories. In this paper, we propose a novel partition-and-detect framework for trajectory outlier detection, which partitions a trajectory into a set of line segments, and then, detects outlying line segments for trajectory outliers. The primary advantage of this framework is to detect outlying sub-trajectories from a trajectory database. Based on this partition-and-detect framework, we develop a trajectory outlier detection algorithm TRAOD. Our algorithm consists of two phases: partitioning and detection. For the first phase, we propose a two-level trajectory partitioning strategy that ensures both high quality and high efficiency. For the second phase, we present a hybrid of the distance-based and density-based approaches. Experimental results demonstrate that TRAOD correctly detects outlying sub-trajectories from real trajectory data.

关键词： trajectory outliers Frameworks Line sections trajectory data detection algorithms partitions Ensure

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Modeling and Clustering Techniques for Multi-Band Change detection

Modeling and Clustering Techniques for Multi-Band Change Det...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Karin Griffis Maja Bystrom Department of Electrical and Computer Engineering Boston University USA

In this paper, two unsupervised methods for multi-band change detection are presented. Both methods model the multi-band difference image histogram in order to characterize different degrees of observed spectral changes. In the first approach, we extend the single-band change detection algorithm proposed by Prieto and Bruzzone in which a two-component mixture density is fit to the observed difference image histogram, where the components correspond to the changed and unchanged populations. The second approach employs the hierarchical modal associative clustering algorithm proposed by Li et al., in which a hierarchy of kernel densities at different bandwidths is employed to model the multi-band difference image histogram. The kernel density modes correspond to different scales of changes and are analyzed with respect to increasing kernel bandwidth so that changes occurring at different scales may be identified. Experiments, carried out on ASTER data; are conducted to display the changes captured by each method as well as to illustrate how the degrees of detected changes can be interpreted with respect to model complexity or scale.

关键词： Histograms Kernel Change detection algorithms Bandwidth detection algorithms Clustering algorithms Remote sensing Displays Biological system modeling Alarm systems

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Curvelet decomposition for detection of cylindrical targets

Curvelet decomposition for detection of cylindrical targets

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IEEE International Conference on Image Processing

作者： Frank Crosby US Naval Surface Warfare Center Panama FL USA

Curvelets are a multiscale system with very high directional sensitivity. A new detection algorithm is herein described which operates on a curvelet decomposition of acoustic imagery. The algorithm detects the presence of cylindrical targets through a statistical mapping of curvelet coefficients. The coefficients are calculated as an inner product between image features and a curvelet basis element. The similarity in appearance between cylindrical targets and curvelet basis elements yield an accurate detection algorithm with a very low false alarm rate.

关键词： Acoustic signal detection Synthetic aperture sonar detection algorithms Cities and towns Bandwidth Wavelet transforms Object detection Sensor phenomena and characterization Vehicles Sonar detection

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Multisensor joint fusion and detection of mines using SAR and Hyperspectral

Multisensor joint fusion and detection of mines using SAR an...

引用

IEEE SENSORS

作者： Nasser M. Nasrabadi US Army Research Laboratory Adelphi MD USA

In this paper a new nonlinear joint fusion and detection algorithm is proposed for locating anomalies from two different types of sensor data (synthetic aperture radar (SAR) and hyperspectral sensor (HS) data). The proposed approach jointly exploits the nonlinear correlation or dependencies between the two sensors in order to simultaneously fuse and detect the objects of interest (mines). A well-known anomaly detector, so called RX algorithm is extended to perform fusion and detection simultaneously at the pixel level by appropriately concatenating the information from the two sensors. This approach is then extended to its nonlinear version using the idea of kernel learning theory which implicitly exploits the higher order dependencies (nonlinear correlations) between the two sensor data through an appropriate kernel.

关键词： Radar detection Hyperspectral sensors Sensor fusion Kernel Hyperspectral imaging detection algorithms Synthetic aperture radar Fuses Object detection Detectors

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