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检索条件"主题词=Detection Algorithms"

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An Outlier detection Algorithm Based on Spectral Clustering

An Outlier Detection Algorithm Based on Spectral Clustering

引用

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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Endmember detection using the Dirichlet process

Endmember detection using the Dirichlet process

引用

International Conference on Pattern Recognition

作者： Alina Zare Paul D. Gader Department of Computer and Information Science and Engineering University of Florida

ISBN: (纸本)9781424421749

An endmember detection algorithm for hyperspectral imagery using the Dirichlet process to determine the number of endmembers in a hyperspectral image is described. This algorithm provides an estimate of endmember spectra, proportion maps, and the number of endmembers needed for a scene. Updates to the proportion vector for a pixel are sampled using the Dirichlet process. As opposed to previous methods that prune unnecessary endmembers, the proposed algorithm is initialized with one endmember and new endmembers are added through sampling as needed. Results are shown on a two-dimensional dataset and a simulated dataset using endmembers selected from an AVIRIS hyperspectral image.

关键词： Hyperspectral imaging Hyperspectral sensors Pixel Layout Clustering algorithms Information science detection algorithms Image sampling Wavelength measurement Vectors

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Pitch detection Method for Noisy Speech Signals Based on Pre-Filter and Weighted Wavelet coefficients

Pitch Detection Method for Noisy Speech Signals Based on Pre...

引用

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

作者： Ru-wei LI Chang-chun BAO Hui-jing DOU Speech and Audio Signal Processing Lab School of Electronic Information and Control EngineeringBeijing University of TechnologyBeijing 100124China

Most of the current pitch detection algorithms can not work well under the high noise *** this reason,a pitch detection algorithm for noisy speech signal based on pre-filtering and weighted wavelet coefficients is ***,the noisy speech signals are ***,the speech pre-filtered is decomposed by the quadratic spline ***,the wavelet coefficients of three consecutive scales are weighted to emphasize the sharp change ***,three candidate pitch periods are extracted from the weighted ***,the pitch period is calculated by autocorrelation *** show that this algorithm can increase the performance of pitch detection in noisy environment and decreases computational complexity compared with DWT-NCCF method.

关键词： Wavelet coefficients Working environment noise Wavelet transforms Signal processing algorithms Autocorrelation Speech analysis Filters Speech enhancement detection algorithms Spline

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Anomaly detection Support Vector Machine and Its Application to Fault Diagnosis

Anomaly Detection Support Vector Machine and Its Application...

引用

IEEE International Conference on Data Mining (ICDM)

作者： Ryohei Fujimaki NEC Common Platform Software Research Laboratories

We address the issue of classification problems in the following situation: test data include data belonging to unlearned classes. To address this issue, most previous works have taken two-stage strategies where unclear data are detected using an anomaly detection algorithm in the first stage while the rest of data are classified into learned classes using a classification algorithm in the second stage. In this study, we propose anomaly detection support vector machine (ADSVM) which unifies classification and anomaly detection. ADSVM is unique in comparison with the previous work in that it addresses the two problems simultaneously. We also propose a multiclass extension of ADSVM that uses a pairwise voting strategy. We empirically present that ADSVM outperforms two-stage algorithms in application to an real automobile fault dataset, as well as to UCI benchmark datasets.

关键词： Fault detection Support vector machines Fault diagnosis Support vector machine classification detection algorithms Classification algorithms Voting Automobiles Benchmark testing Training data

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A detection Algorithm of Audio Spread Spectrum Data Hiding

A Detection Algorithm of Audio Spread Spectrum Data Hiding

引用

International Conference on Wireless Communications, Networking and Mobile Computing (WiCom)

作者： S. Gao R. M. Hu W. Zeng H. J. Ai C. R. Li National Engineering Research Center for Multimedia Software Wuhan University of China Wuhan China

In this paper, a method of passive steganalysis is proposed. We focus on detecting the existing of data hidden in audio files with spread spectrum (SS) data hiding. SS data hiding is considered as a process of adding noise. The technology of classifier and feature vector extraction are used to achieve the detection. First, we divide an audio signal into several frames. The wavelet coefficients before and after wavelet de-noise in each frame are calculated. Then, we pick some stat, of their difference as the feature vectors of the audio signal. Finally, according to the feature vectors of the audio signal, classifier will decide whether the audio signal have been processed by SS or not. In our experiment, support vector machines (SVM) play role of classifier, 600 audio files are used to be our experiment samples. After the feature vectors of all the samples are calculated, those feature vectors of samples are divided into two parts. One is testing part and the other is training part. The result of experiment shows that if the strength of data hiding is higher than 0.005, the rate of correct detection of training part is higher than 86.5% and the rate of correct detection of testing part is higher than 82.5%.

关键词： detection algorithms Spread spectrum communication Data encapsulation Signal processing Support vector machines Support vector machine classification Testing Feature extraction Data mining Wavelet coefficients

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Trajectory Outlier detection: A Partition-and-Detect Framework

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

引用

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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detection of Ship Wakes in SAR Images Based on Radon Transform

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

引用

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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Modeling and Clustering Techniques for Multi-Band Change detection

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

引用

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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A Stairway detection Algorithm based on Vision for UGV Stair Climbing

A Stairway Detection Algorithm based on Vision for UGV Stair...

引用

IEEE International Conference on Networking, Sensing and Control

作者： Yang Cong Xiaomao Li Ji Liu Yandong Tang Graduate School of Chinese Academy of Sciences Beijing China Robotic Laboratory Shenyang Institute of Automation Chinese Academy and Sciences China

In the paper, we present a vision based algorithm used to guide the unmanned ground vehicles (UGV) for autonomous stairways climbing and implement it on UGV successfully. The reliability of guiding UGV to climb stairs requires evaluating two offset parameters: the position of vehicle on stairs and the orientation angle to stairs. The intention of our algorithm is to estimate these two parameters through extracting the stair edges robustly. To achieve this goal, we apply the Gabor filter to eliminate the influence of the illumination and keep edges, and propose a fast method to remove small lines. Finally we link stair edges, and estimate the offset parameters used to steer the vehicle by RANSAC algorithm. Experiments on various stairways including indoor and outdoor are given in various light conditions. The results validate our algorithm.

关键词： detection algorithms Robotics and automation Vehicles Robustness Parameter estimation Gabor filters Robot vision systems Servomechanisms Lighting Mobile robots

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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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