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检索条件"主题词=change detection algorithms"

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A single pass algorithm of finding frequent vibrated items over online data streams

A single pass algorithm of finding frequent vibrated items o...

引用

International Conference on Digital Information Management (ICDIM)

作者： Guanling Lee Qiao-Tzu Chen Department of Computer Science and Information Engineering National Dong Hwa University Hualien Taiwan

Data streams are data items generated unbounded and continuously. To detect the vibration of data item s quantity, a single pass algorithm is proposed for mining vibrated items over online data streams in this paper. The change of data item can be reported at once by measuring its vibrated slope. Not only the change of data item will be detected, the period in which the data item is frequent vibrated is also reported. Moreover, a set of simulations is performed to show the benefit of our approach.

关键词： Vibration measurement Counting circuits change detection algorithms Frequency estimation Frequency conversion Computer science Data engineering Monitoring Marketing and sales Data mining

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An automatic video object segmentation scheme

An automatic video object segmentation scheme

引用

IEEE International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)

作者： Xiaoyan Zhang Lingxia Liu Xuchun Zhuang The Telecommunication Engineering Institute Air Force Engineering University Xi'an China

In this paper, a new scheme is presented for performing automatic video object segmentation. First of all, a new adaptive and reliable threshold estimation method has been proposed for the change detection process. Secondly, the initial edge model of the video object is obtained on the basis of the adaptive change detection and Canny edge detection. Thirdly, the edge model is tracked and updated in every frame to accommodate rotation and changes in shape of the tracked object and then the outer contour of the video object is determined based on the mathematical morphological operation. Lastly, an improved active contour which uses the gradient vector as the external force is applied to guide the initial outer contour moving to the actual video object contour. Experiment results show that the proposed algorithm is robust to the entire motion and local deformation of object, adaptable to the complex background and can get the closed and accurate video object contour.

关键词： Object segmentation Image edge detection Robustness change detection algorithms Reliability engineering Object detection Active contours Electric breakdown Video signal processing Mathematical model

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Trend Feature Mining Algorithm Based on Financial Time Series

Trend Feature Mining Algorithm Based on Financial Time Serie...

引用

International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Chi Xie Hua Tan Xiang Yu Business School Hunan University Changsha China

In this paper, we transform shares time series into price rate of change (ROC) time series based on the characteristics of financial time series, improving the trend feature algorithm and clustering algorithm, and providing a new corresponding feature similarity measure, therefore we can forecast time series into the detection of frequent and effective sets, which can help us to make data mining forecasts. Experimental results indicate that the proposed method is effective in event forecasting.

关键词： Data mining change detection algorithms Clustering algorithms Economic forecasting Business communication Frequency Fourier transforms Space technology Time measurement Wavelet transforms

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Video shot detection via information theoretic classification

Video shot detection via information theoretic classificatio...

引用

International Symposium on Signal Processing and Its Applications (ISSPA)

作者： Mahdi Asefi Mohamed-Yahia Dabbagh Department of Electrical and Computer Engineering University of Waterloo Canada

New hierarchical method for detecting and classifying shot boundaries in video sequences using information theoretic classification (ITC) rule is proposed. ITC relies on likelihood of class label transmission of a data point to data points in its vicinity. ITC focuses on maximizing the global transmission of true class labels and classifying the frames into two classes of cuts and non-cuts. Applying the same rule, the non-cut frames are also classified into two categories of arbitrary shot frames and gradual transition frames. The experimental results on different videos show excellent performance in terms of precision and recall.

关键词： Gunshot detection systems detection algorithms change detection algorithms Cameras Video sequences Measurement techniques Mutual information Image registration Entropy Error analysis

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Mining Concept Drifts from Data Streams Based on Multi-Classifiers

Mining Concept Drifts from Data Streams Based on Multi-Class...

引用

International Conference on Advanced Information Networking and Applications Workshops (AINAW)

作者： Yue Sun Guojun Mao Xu Liu Chunnian Liu Beijing University of Technology Beijing CN

Mining concept drifts is one of the most important fields in mining data streams. In this paper, a new ensemble algorithm called ICEA is proposed for mining concept drifts from data streams, which uses ensemble multi-classifiers to detect concept changes from the data streams in an incremental way. The experimental results show that ICEA algorithm performs higher accuracy and better adaptability than the popular methods such as SEA algorithm.

关键词： Streaming media change detection algorithms Partitioning algorithms Data mining Classification algorithms Mobile computing Sun Laboratories Computer science Telecommunication traffic

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Hybrid Object-Based Video Compression Scheme Using a Novel Content-Based Automatic Segmentation Algorithm

Hybrid Object-Based Video Compression Scheme Using a Novel C...

引用

IEEE International Conference on Communications (ICC)

作者： N. A. Tsoligkas D. Xu I. French School of Science and Technology University of Teesside Middlesbrough UK

This paper describes a hybrid object-based video coding scheme that achieves efficient compression by separating moving objects from stationary background and transmitting the shape, motion and residuals for each segmented object. In this scheme, a new content-based object segmentation algorithm is proposed, which does not assume any prior modeling of the objects being segmented. The binarization process, which finds large object regions, is based on a threshold function that calculates block histograms and takes image noise into account. The resultant binary mask is further processed using morphological operations. The motion vectors are estimated inside the change detection mask using block-matching method between two successive frames, and then the dense motion field is estimated using the motion vectors and the Horn-Schunck algorithm.

关键词： Video compression Image segmentation Motion estimation Video coding Shape Object segmentation Histograms Morphological operations Motion detection change detection algorithms

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Foreground object localization using a flooding algorithm based on inter-frame change and colour

Foreground object localization using a flooding algorithm ba...

引用

IEEE Conference on Advanced Video and Signal Based Surveillance (AVSS)

作者： I. Grinias G. Tziritas Computer Science Department University of Crete Heraklion Greece

A Bayesian, fully automatic moving object localization method is proposed, using inter-frame differences and background/foreground colour as discrimination cues. change detection pixel classification to one of the labels "changed" or "unchanged" is obtained by mixture analysis, while histograms are used for statistical description of colours. High confidence, change detection based, statistical criteria are used to compute a map of initial labelled pixels. Finally, a region growing algorithm, which is named priority multi-label flooding algorithm, assigns pixels to labels using Bayesian dissimilarity criteria. Localization results on well-known benchmark image sequences as well as on webcam and compressed videos are presented.

关键词： Floods change detection algorithms Object detection Bayesian methods Histograms Image segmentation Videos Probability density function Computer science Application software

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Robust change Analysis of SAR Data Through Information-Theoretic Multitemporal Features

Robust Change Analysis of SAR Data Through Information-Theor...

引用

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Luciano Alparone Bruno Aiazzi Stefano Baronti Andrea Garzelli Filippo Nencini Department Electronics & Telecommunications University of Florence Florence Italy CNR Area di Ricerca di Firenze Institute of Applied Physics Nello Carrara Italy Department Information Engineering University of Sienna Siena Italy

Multi-temporal analysis of Synthetic Aperture Radar (SAR) images has gained an ever increasing attention due to the availability of several satellite platforms with different revisit times and to the intrinsic capability of the SAR system of producing all-weather observations. As a drawback, automated analysis in general and change detection in particular are made difficult by the inherent noisiness of SAR imagery. Even if a pre-processing step aimed at speckle reduction is adopted, most of algorithms borrowed from computer vision cannot be profitably used. In this work, a novel pixel feature suitable for change analysis is derived from information-theoretic concepts. It does not require preliminary de-speckling and capable of providing accurate change maps from a couple of SAR images. The rationale is that the negative of logarithm of the probability of an amplitude level in one image conditional to the level of the same pixel in the other image conveys an information on the amount of change occurred between the two passes. Experimental results carried out on two couples of multi-temporal SAR images demonstrate that the proposed IT feature outperform the Log-Ratio in terms of capability of discriminating changes.

关键词： Robustness Data analysis Information analysis Synthetic aperture radar Image analysis Availability Satellites Radar detection Speckle change detection algorithms

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Online Health Monitoring in Digitally Controlled Power Converters

Online Health Monitoring in Digitally Controlled Power Conve...

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Annual IEEE Conference on Power Electronics Specialists (PESC)

作者： Jeffrey Morroni Arseny Dolgov Mariko Shirazi Regan Zane Dragan Maksimovic Colorado Power Electronics Center University of Colorado Boulder Boulder CO USA

Spacecraft power systems and other mission- critical applications require robust power delivery systems. This paper focuses on two aspects of power converter health monitoring: detecting and analyzing loss-related and frequency response-related changes. Two hardware-efficient algorithms are discussed that can be implemented without any additional sensing points other than those required for operation of the converter and with minimal additional digital hardware. It is shown that available converter operating information can be processed to detect a significant (e.g. 2x) change in the power MOSFET R ON - In addition, a system identification-based approach with an additional digital pre-fllter is used to extract an accurate and useful frequency response of the converter despite large quantization noise caused by a low-resolution A/D converter. It is shown that this information can be used to accurately monitor the stability margins of the converter. The techniques are verified experimentally on a 90 W 50 V-15 V forward converter with FPGA control.

关键词： Monitoring Digital control Digital-to-frequency converters Space vehicles Power system analysis computing Robustness Frequency conversion change detection algorithms Hardware MOSFET circuits

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Incremental Local Outlier detection for Data Streams

Incremental Local Outlier Detection for Data Streams

引用

IEEE Symposium on Computational Intelligence and Data Mining (CIDM)

作者： Dragoljub Pokrajac Aleksandar Lazarevic Longin Jan Latecki CIS Department and AMRC Delaware State University Dover DE USA United Technologies Research Center Inc. East Hartford CT USA CIS Department Temple University Philadelphia PA USA

Outlier detection has recently become an important problem in many industrial and financial applications. This problem is further complicated by the fact that in many cases, outliers have to be detected from data streams that arrive at an enormous pace. In this paper, an incremental LOF (local outlier factor) algorithm, appropriate for detecting outliers in data streams, is proposed. The proposed incremental LOF algorithm provides equivalent detection performance as the iterated static LOF algorithm (applied after insertion of each data record), while requiring significantly less computational time. In addition, the incremental LOF algorithm also dynamically updates the profiles of data points. This is a very important property, since data profiles may change over time. The paper provides theoretical evidence that insertion of a new data point as well as deletion of an old data point influence only limited number of their closest neighbors and thus the number of updates per such insertion/deletion does not depend on the total number of points TV in the data set. Our experiments performed on several simulated and real life data sets have demonstrated that the proposed incremental LOF algorithm is computationally efficient, while at the same time very successful in detecting outliers and changes of distributional behavior in various data stream applications

关键词： Event detection Telecommunication traffic Data mining change detection algorithms Video surveillance Computational Intelligence Society Intrusion detection Traffic control Unsupervised learning Computer networks

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