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检索条件"主题词=Change detection algorithms"

共 1588 条记录，以下是401-410 订阅

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HMM triangle relative entropy concepts in sequential change detection applied to vision-based dim target manoeuvre detection

HMM triangle relative entropy concepts in sequential change ...

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International Conference on Information Fusion

作者： Timothy L. Molloy Jason J. Ford School of Electrical Engineering and Computer Science Queensland University of Technology Brisbane QLD Australia

The quick detection of abrupt (unknown) parameter changes in an observed hidden Markov model (HMM) is important in several applications. Motivated by the recent application of relative entropy concepts in the robust sequential change detection problem (and the related model selection problem), this paper proposes a sequential unknown change detection algorithm based on a relative entropy based HMM parameter estimator. Our proposed approach is able to overcome the lack of knowledge of post-change parameters, and is illustrated to have similar performance to the popular cumulative sum (CUSUM) algorithm (which requires knowledge of the post-change parameter values) when examined, on both simulated and real data, in a vision-based aircraft manoeuvre detection problem.

关键词： Hidden Markov models Entropy Probabilistic logic detection algorithms change detection algorithms Density measurement Maximum likelihood estimation

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Multispectral remote sensing image change detection based on Markovian fusion

Multispectral remote sensing image change detection based on...

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International Conference on Agro-Geoinformatics

作者： Qiongcheng Xu Yunchen Pu Wei Wang Huamin Zhong Department of Automatic Control Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiaotong University Shanghai China

This paper presents a novel multispectral remote sensing image change detection (CD) algorithm based on Markovian fusion. This new method intends to obtain the optimal change map (change detection result) by fusing information contained in each band. The optimal change map are modeled as Markov Random Fields (MRF) which takes into account not only the spectral information of multiple bands but also the contextual information of both the pixels in the optimal change map and the relationship between the optimal change map and change maps of each band respectively, and thus, leads to a more accurate and robust change detection result. In the analysis of difference image, an unsupervised threshold selection algorithm based on Bayesian decision theory is introduced, which aims at extracting the changed information from the images. The finding of optimal change map is equivalent to minimizing the total Gibbs potential function by using simulated annealing algorithm. The experimental result of the proposed algorithm compared with the change map of each band is presented, which indicates that the proposed method improves the result effectively and is superior to any band's change map.

关键词： Remote sensing change detection algorithms Bayesian methods Robustness Markov random fields Accuracy Noise

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Video streaming in wireless sensor networks with low-complexity change detection enforcement

Video streaming in wireless sensor networks with low-complex...

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Baltic Congress on Future Internet Communications (BCFIC Riga)

作者： Claudio Salvadori Matteo Petracca Riccardo Pelliccia Marco Ghibaudi Paolo Pagano Real-Time Systems Laboratory Scuola Superiore Sant' Anna Pisa Italy Scuola Superiore SantAnna Research Unit Consorzio Nazionale Interuniversitario per le Telecomunicazioni Pisa Italy National Laboratory of Photonic Networks Consorzio Nazionale Interuniversitario per le Telecomunicazioni Pisa Italy

Video streaming applications in wireless sensor networks are attractive for enabling new pervasive high value services. The reduced amount of available bandwidth in such networks imposes the transmission of small size video frames at very low frame rates. Moreover, when compression techniques cannot be applied, due to constrained computational capabilities of sensor nodes, and uncompressed video frames must be sent through the network, lower image sizes and frame rates must be adopted. In such a scenario, when higher frame rates are required or multiple video streams must be enabled the required transmission bandwidth of each stream must be reduced. In this paper we present a low-complexity algorithm based on change detection which reduces the transmission bandwidth of a video stream based on raw images. Performance results show how the proposed technique can reduce the transmission bandwidth up to 87.22% and 66.78%, with respect to a flat video stream transmission, when low and high changes are experienced in the monitored scene.

关键词： Streaming media Bandwidth Image coding Wireless sensor networks PSNR change detection algorithms Transform coding

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ESTABLISHING BASELINE INFORMATION ON AN ENHANCED COAL BED METHANE SITE WITH SPOT VGT-S10 AND ALOS PALSAR FOR SAFETY MONITORING

ESTABLISHING BASELINE INFORMATION ON AN ENHANCED COAL BED ME...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Li, Xiaojing Ge, Linlin Hu, Zhe Cholathat, Rattanasuda Univ New S Wales Sch Surveying & Spatial Informat Syst Fac Engn Sydney NSW 2052 Australia

ISBN: (纸本)9781467311595

CO2 Enhanced Coal Bed Methane (ECBM) has been considered as one of the promising clean coal technologies to reduce green house gas emissions. Australia and China have begun a demonstrational project to explore the potential of ECBM in the gas/coal fields in Shanxi Province, China. A total of 12 ALOS PALSAR images and 354 SPOT VGT-S10 images have been successfully used to establish baseline information of ground deformation and vegetation dynamics on the site based on DInSAR and NDVI time series analysis respectively. Cross analysis between SOPT and MODIS has also been given. Such baseline information will play an important role in the future for monitoring environmental safety at the site.

关键词： change detection algorithms remote monitoring displacement measurements vegetation stress mapping NDVI

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Robust and efficient change detection algorithm based on 3D line segments

Robust and efficient change detection algorithm based on 3D ...

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IEEE Convention of the Electrical and ELectronic Engineers in Israel

作者： Tom Zohar Ido Ariav Meir Bar-Zohar Signal and Image Processing Laboratory Department of Electrical Engineering Technion-Israel Institute of Technology Haifa Israel

In this paper we examine and improve a new approach for change detection (introduced in [1]) which is based on the appearance and disappearance of 3D line segments as seen in a new image. These 3D line segments are estimated from a set of learning images taken from arbitrary viewpoints and under arbitrary light conditions in an unsupervised manner. The main advantage of the proposed method lies in the fact that the change detection is performed by comparing line segments, and not surfaces or gray levels. Computing 3D surfaces in an image can be computationally intensive, and other methods such as image subtraction or cross-correlation are sensitive to lighting conditions and changes in viewpoints. Moreover, most man-made objects such as buildings, cars, and even cities viewed from above consist mainly of straight linesman-made objects, and therefore this method is highly applicable for such structures. The proposed algorithm first focuses on the reconstruction of a set of 3D line segments forming a certain 3D scene using a set of 2D line segments obtained from the learning images in an unsupervised manner, without any prior knowledge on the cameras' positions or relative distance. In the change detection stage, we use the reconstructed 3D scene of line segments to check if changes, such as appearance or disappearance of objects, have occurred in a given test image. This test image can be taken from arbitrary viewpoint and under arbitrary lighting conditions. Our change detection algorithm not only distinguishes between the states of "changed" and "not-changed" line segments, it also classifies the "changed" line segments as appeared - objects that entered the scene in the test image, and disappeared - objects that left the 3D scene reconstructed from the lines of the learning images.

关键词： Image segmentation Image reconstruction Cameras Lighting change detection algorithms Solid modeling Clutter

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Energy-efficient quickest change detection in sensor networks

Energy-efficient quickest change detection in sensor network...

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IEEE/SP Workshop on Statistical Signal Processing (SSP)

作者： Taposh Banerjee Venugopal V. Veeravalli Department of Electrical and Computer Engineering Coordinated Science Laboratory University of Illinois Urbana-Champaign USA

We propose a minimax formulation for quickest change detection in sensor networks, where there is a cost associated with taking observations at each sensor. We present two energy efficient algorithms, centralized and distributed, in which on-off observation control is employed and the change is detected under a constraint on the false alarm rate. We show that these algorithms are asymptotically optimal as the false alarm rate goes to zero and have good performance compared to the straightforward approach of fractional sampling in which the observations are selected randomly.

关键词： change detection algorithms Indexes Random variables Bayesian methods Vectors Centralized control Algorithm design and analysis

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Data-efficient minimax quickest change detection

Data-efficient minimax quickest change detection

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Taposh Banerjee Venugopal V. Veeravalli Department of Electrical and Computer Engineering Coordinated Science Laboratory University of Illinois Urbana Champaign Urbana-Champaign USA

In [1], a Bayesian two-threshold algorithm was obtained for quickest detection of a change in the distribution of a sequence of random variables, subject to constraints of probability of false alarm and observation cost. This algorithm was shown to be asymptotically optimal and to have good trade-off curves. In this paper, the results in [1] are extended to the more practically relevant minimax setting. Motivated by the structure of the algorithm developed in [1], a CUSUM based algorithm, called DE-CUSUM is proposed, which can be used for on-off observation control and to detect change as quickly as possible subject to a false alarm constraint. It is shown that the DE-CUSUM algorithm inherits the good qualities of the algorithm in [1], i.e., it is also asymptotically optimal and has good trade-off curves. Numerical results show that the DE-CUSUM algorithm provides a substantial savings in the observation cost over the naive approach of fractional sampling.

关键词： change detection algorithms Delay Bayesian methods Random variables Approximation algorithms Algorithm design and analysis

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The PerfSim Algorithm for Concept Drift detection in Imbalanced Data

The PerfSim Algorithm for Concept Drift Detection in Imbalan...

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IEEE International Conference on Data Mining Workshops (ICDM Workshops)

作者： Daniel K. Antwi Herna L. Viktor Nathalie Japkowicz School of Electrical Engineering and Computer Science University of Ottawa Ottawa Canada

ISBN: (纸本)9781467351645

There is currently a surge of interest in adaptive learning algorithms for applications ranging from ozone level peak predictions, learning stock market indicators, and detecting smart phone usage patterns. In such scenarios, the detection of change (or drift) in the concept being learned is important to ensure that correct, timely and relevant models are constructed. In addition, such data is often imbalanced and, to further complicate the issue, we are frequently interested in learning the minority class. It follows that ignoring these two aspects during learning may lead to unreliable, or even incorrect, models being built. In this research we discuss the interplay between concept drift detection and imbalanced data sets in order to ensure reliable results. We introduce a novel algorithm that, rather than considering a single performance evaluation measure such as accuracy for change detection, considers all the components of a confusion matrix and employs the cosine similarity coefficient. We evaluate our algorithm against a real world mobile phone database, as well as benchmarking datasets, and we compare it with two other state-of-the-art methods. The results show that our approach is particularly sensitive to concept drifts occurring in imbalanced data sets. Our evaluation indicates that our algorithm is able to detect concept drift reliably. Further, our method is shown to perform very well compared to the other techniques, especially when the drift occurs in the minority class of a class imbalance problem.

关键词： Vectors change detection algorithms Data models Reliability Accuracy Training Prediction algorithms

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Improving foreground segmentations with probabilistic superpixel Markov random fields

Improving foreground segmentations with probabilistic superp...

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IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Alexander Schick Martin Bäuml Rainer Stiefelhagen Fraunhofer IOSB Germany Karlsruhe Institute of Technology Germany

We propose a novel post-processing framework to improve foreground segmentations with the use of Probabilistic Superpixel Markov Random Fields. First, we convert a given pixel-based segmentation into a probabilistic superpixel representation. Based on these probabilistic superpixels, a Markov random field exploits structural information and similarities to improve the segmentation. We evaluate our approach on all categories of the change detection 2012 dataset. Our approach improves all performance measures simultaneously for eight different basis foreground segmentation algorithms.

关键词： Image segmentation Probabilistic logic change detection algorithms Benchmark testing Markov random fields Motion segmentation

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DETECTING LAND COVER change BY EVALUATING THE INTERNAL COVARIANCE MATRIX OF THE EXTENDED KALMAN FILTER

DETECTING LAND COVER CHANGE BY EVALUATING THE INTERNAL COVAR...

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IEEE International Geoscience and Remote Sensing Symposium

作者： B. P. Salmon W. Kleynhans F. van den Bergh J. C. Olivier K. J. Wessels Remote Sensing Research Unit Meraka Institute CSIR Department of Electrical Electronic and Computer Engineering University of Pretoria

ISBN: (纸本)9781467311601

In this paper, the internal operations of an Extended Kalman Filter is investigated to see if any useful information can be derived to detect land cover change in a MODIS time series. The Extended Kalman Filter expands its internal covariance if a significant change in reflectance value is observed, followed by adapting the state parameters to compensate for this change. The analysis shows a change detection accuracy above 90% can be attained when evaluating the elements within the internal covariance matrix to detect new human settlements, with a corresponding false alarm rate below 11%.

关键词： change detection algorithms Covariance matrix Kalman Filter Spatial information Time series analysis

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