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检索条件"主题词=change detection algorithms"

共 1589 条记录，以下是101-110 订阅

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A change-detection algorithm based on structure and colour

A change-detection algorithm based on structure and colour

引用

IEEE Conference on Advanced Video and Signal Based Surveillance

作者： Di Stefano, L Mattoccia, S Mola, M Univ Bologna ARCES DEIS I-40135 Bologna Italy

ISBN: (纸本)0769519717

The paper proposes a novel change-detection algorithm for automated videosurveillance applications. The algorithm is based on the idea of incorporating into the background model a set of simple low-level features capable of capturing effectively "structural" (i.e. robust with respect to illumination variations) information. Thanks to this approach, and unlike most conventional change-detection algorithms, the proposed algorithm is capable of handling correctly still and slow objects as well as of working properly throughout very long time spans. Moreover the algorithm can naturally interact with the higher-level processing modules found in advanced video-based surveillance systems in order to allow for flexible and intelligent background maintenance.

关键词： Cameras change detection algorithms detection algorithms Image analysis Image color analysis Layout Lighting Pixel Robustness Surveillance

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APPLICATION OF A SEMI-AUTOMATIC UNSUPERVISED change detection TO (SEMI-) NATURAL GRASSLAND LOSS AT VERY HIGH RESOLUTION

APPLICATION OF A SEMI-AUTOMATIC UNSUPERVISED CHANGE DETECTIO...

引用

IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Tarantino, Cristina Blonda, Palma Adamo, Maria CNR Inst Intelligent Syst Automat CNR ISSIA Via G Amendola 122 I-70126 Bari Italy

ISBN: (纸本)9781479979295

This paper focuses on the application of a semi-automatic unsupervised change detection algorithm called Cross Correlation Analysis (CCA) to the detection of (semi-) natural grasslands changes at Very High Resolution (VHR). A reference validated Land Cover/ Land Use map at time T1 and only one satellite image at time T2, with T2>T1, are required to detect changes occurred at T2 in the selected target class. This approach offers the possibility to reduce the costs of change detection when the acquisition of multi-seasonal VHR images at time T2 for supervised change detection is too expensive or when no archive VHR image is available in the past for unsupervised comparison between T1 and T2 images. A summer Worldview-2 image for a Natura 2000 test site was considered and the results appear encouraging.

关键词： change detection algorithms Earth Observation Data (Semi-) Natural Grassland Very High Resolution

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An adaptive threshold for change detection methods using a windowed entropy-based criterion - Application to fault-tolerant fusion in collaborative mobile robotics 4

An adaptive threshold for change detection methods using a w...

引用

4th Conference on Control and Fault Tolerant Systems (SysTol)

作者： Daass, Bilal Pomorski, Denis Haddadi, Kamel Univ Lille CNRS Cent LilleUMR 9189 CRIStAL Ctr Rech Informat Signal & Automat Lille F-59000 Lille France Univ Valenciennes Univ Lille CNRS IEMNCent LilleISENUMR 8520 F-59000 Lille France

ISBN: (纸本)9781728103808

Nowadays, process supervision occupies an important place in quality control, cooperative localization in mobile robotics, video and image processing, and intelligent system design, to name a few. Indeed, any failure of such processes can reduce performance and have serious consequences. The development of statistical methods, capable of detecting and locating anomalies in these dynamic systems as quickly as possible, is of real interest. In this context, we have proposed in a previous study a reformulation of the change detection strategy using an entropy-based criterion. Our approach allowed the calculation of an adaptive threshold, unlike the Bayes criterion. In this paper, we propose an improvement of this study by introducing the use of an optimal window of observations. We validate the proposed approach to the Exponentially Weighted Moving Average (EWMA) control charts, which is a commonly used change detection technique. Our strategy is illustrated on a well-known example of the literature. Finally, this windowed entropy-based criterion allows one to design a fault-tolerant fusion methodology, which is experimentally validated from an extended Kalman filter (EKF) in collaborative mobile robotics.

关键词： Microsoft Windows Robots Control charts change detection algorithms Entropy Fault tolerant systems Collaboration

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Asymptotic Quickest change detection And Isolation with Polynomial Delay Penalties

Asymptotic Quickest Change Detection And Isolation with Poly...

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Australian Control Conference (AuCC)

作者： Molloy, Timothy L. QUT Fac Sci & Engn Sch Elect Engn & Comp Sci Brisbane Qld 4000 Australia

ISBN: (纸本)9781922107909

In this paper, we consider the problem of quickly detecting and isolating an abrupt change in an observed stochastic processes under a polynomial penalty for delays. We propose an auxiliary matrix cumulative sum (AMCUSUM) detection and isolation algorithm, and show that it is optimal under our quickest change detection and isolation criterion with polynomial delay penalties in the asymptotic regime of few false alarms and false isolations. We also illustrate the AMCUSUM algorithm in simulations.

关键词： Delays change detection algorithms Signal processing algorithms Australia Stochastic processes Standards Probability distribution Probability distribution Signal processing algorithms Stochastic Processes Markov chain Australia delay Polynomial fast-switching change detection algorithms penalty

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Active Learning classification and change detection on multispectral images 12

Active Learning classification and change detection on multi...

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12th IEEE International Conference on Industrial Informatics (INDIN)

作者： Mantripragada, Kiran Quintanilha, Jose Alberto Giannotti, Mariana Abrantes Univ Sao Paulo Polytech Sch IBM Res Brazil BR-05508 Sao Paulo Brazil Univ Sao Paulo Polytech Sch Sao Carlos Brazil

ISBN: (纸本)9781479949052

The performance of image classification usually depends on the quality of labelled datasets to be used as training samples. In the context of remote sensing, the acquisition of ground-truth data can be a difficult and expensive task because it depends on the comprehensive surveys over the area of interest while the labelling task must be performed by experienced professionals. On the other hand, algorithms based on Active Learning can be helpful to overcome the lack of training samples. We present a cohesive algorithm for image classification and change detection based on Active Learning, that tackles the lack of ground-truth data. Afterwards, we compute the Principal Component Analysis over post-classification images to detect deforestation on the eastern side of So Paulo urban area. Our approach provides a way to automatically select data samples, while it also suggests a category. The user provides the category data (labelling task) to the selected pixels which are further used as training data in the final classification step. We applied the algorithm over four 6-channels multispectral images of the Landsat 5/TM device and we classified the pixels in two categories ("forest" and "non-forest") for the years of 1986, 1996, 2003, and 2011. The change detection, is computed through an automatic threshold applied on the post-classification images. We were able to quantify de deforestation suffered by the eastern side of Sao Paulo city along the years. Our results show that the remaining 31% of forest in 1986 reach a minimum of 25% in 2003, but afterwards it recovered to 27% of the area in 2011.

关键词： change detection algorithms Histograms Maximum likelihood estimation Monitoring Principal component analysis Remote sensing Training

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Unsupervised change detection of Remote Sensing Images Using Superpixel Segmentation and Variational Gaussian Mixture Model 9

Unsupervised Change Detection of Remote Sensing Images Using...

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9th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp)

作者： Yang, Gang Li, Heng-Chao Liu, Chi Southwest Jiaotong Univ Sch Informat Sci & Technol Chengdu Sichuan Peoples R China

ISBN: (纸本)9781538633274

In this paper, we propose a novel unsupervised change detection approach for multitemporal remote sensing (RS) images based on superpixel segmentation and variational Gaussian mixture model (GMM). Firstly, the generated difference image is segmented into multiple superpixels using entropy rate superpixel (ERS) segmentation, which allows for the spatial contextual information to be taken into account in change detection. As such, we utilize the GMM to model the distribution of these superpixels, and assign each superpixel to one component using variational inference (VI) algorithm. Subsequently, according to mean square error (MSE) criterion, the resulting clusters are further grouped into two classes, respectively representing the changed class and unchanged class. As a consequence, we can achieve the change mask (CM) by assigning the superpixels (and its pixels) to the corresponding classes. Experimental results demonstrate the effectiveness of the proposed method with two real multitemporal RS images.

关键词： Image segmentation change detection algorithms Gaussian mixture model Erbium Mathematical model Clustering algorithms

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Uncertainty-Aware Quickest change detection: An Experimental Study 25

Uncertainty-Aware Quickest Change Detection: An Experimental...

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25th International Conference of Information Fusion (FUSION)

作者： Hare, James Zachary Kaplan, Lance DEVCOM Army Res Lab Adelphi MD 20783 USA

ISBN: (数字)9781737749721

ISBN: (纸本)9781665489416

In this work, we study the problem of Quickest change detection which aims to detect when a stream of observations transitions from being drawn from a pre-change distribution to a post-change distribution as quickly as possible. Traditionally, either information is completely known about the distributions, or no information is known and their parameters are estimated using frequentist approaches, e.g., Generalized Likelihood Ratio test. Recently, the Uncertain Likelihood Ratio (ULR) test was proposed for the QCD problem which relaxes both of these assumptions to form a Bayesian test that allows for no knowledge, partial knowledge, and full knowledge of the parameters of the distributions. In this work, we extend the ULR test to improve the order of operations required to compute the test statistic using a windowing method to form the Windowed Uncertain Likelihood Ratio (W-ULR) algorithm. We then applied it to multivariate Gaussian observations and empirically evaluated the average detection delay and missed detections for various false alarm rates under various operating conditions. The results show that the W-ULR outperforms the (windowed) GLR test, which is consistent with the initial findings.

关键词： change detection algorithms Training data Delays Computational efficiency Bayes methods

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UNSUPERVISED change detection IN THE FEATURE SPACE USING KERNELS

UNSUPERVISED CHANGE DETECTION IN THE FEATURE SPACE USING KER...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Volpi, Michele Tuia, Devis Camps-Valls, Gustavo Kanevski, Mikhail Univ Lausanne Inst Geomat & Anal Risk CH-1015 Lausanne Switzerland Univ Valencia Image Proc Lab E-46003 Valencia Spain

ISBN: (纸本)9781457710056

In this paper we propose an unsupervised approach to change detection by computing the difference image directly in the feature spaces. The resulting difference kernel, that is a combination of kernels computed on the coregistered and radiometrically matched input images, is used to train a nonlinear partitioning algorithm. In order to apply the kernel k-means, issues related to the initialization and to the tuning of parameters (e.g. the Gaussian RBF bandwidth) are considered. To validate the proposed unsupervised algorithm, two multitemporal VHR remote sensing images are used.

关键词： Algorithm design and analysis change detection algorithms Clustering algorithms Feature extraction Kernel Partitioning algorithms Remote sensing feature extraction feature space geophysical image processing image matching image resolution kernel k-means a

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A Hierarchical, Nonparametric, Sequential change-detection Test

A Hierarchical, Nonparametric, Sequential Change-Detection T...

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International Joint Conference on Neural Networks (IJCNN)

作者： Alippi, Cesare Boracchi, Giacomo Roveri, Manuel Politecn Milan Dipartimento Elettron & Informaz I-20133 Milan Italy

ISBN: (纸本)9781424496365

Design of applications working in nonstationary environments requires the ability to detect and anticipate possible behavioral changes affecting the system under investigation. In this direction, the literature provides several tests aiming at assessing the stationarity of a data generating process;of particular interest are nonparametric sequential change-point detection tests that do not require any a-priori information regarding both process and change. Moreover, such tests can be made automatic through an on-line inspection of sequences of data, hence making them particularly interesting to address real applications. Following this approach, we suggest a novel two-level hierarchical change-detection test designed to detect possible occurrences of changes by observing incoming measurements. This hierarchical solution significantly reduces the number of false positives at the expenses of a negligible increase of false negatives and detection delays. Experiments show the effectiveness of the proposed approach both on synthetic dataset and measurements from real applications.

关键词： change detection algorithms Delay Indexes Monitoring Probability density function Process control Training data generating process data handling data sequence online inspection hierarchical change-detection test nonparametric change-detection test sequential change-detection test

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Universal Compression of a Piecewise Stationary Source Through Sequential change detection 24

Universal Compression of a Piecewise Stationary Source Throu...

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24th National Conference on Communications (NCC)

作者： Chittam, Dheeraj Kumar Bansal, Rakesh Srivastava, Ritvik Honeywell Tech Solut Lab Pvt Ltd Bangalore 560028 Karnataka India Indian Inst Technol Dept Elect Engn Kanpur 208016 Uttar Pradesh India

ISBN: (纸本)9781538612248

This paper focuses on universal compression of a piecewise stationary source using sequential change detection algorithms. The change detection algorithms that we have considered assume minimal knowledge of the source and make use of universal estimators of entropy. Here, data in each segment is characterized either by an I.I.D. random process or a first order Markov process. Simulation study of a modified sequential change detection test proposed by Jacob and Bansal [1] is carried out. Next, an algorithm to effectively compress a piece-wise stationary sequence using such change detection algorithms is proposed. Overall compression efficiency achieved with Page's Cumulative Sum (CUSUM) test and the modified change detection test proposed in [1] (JB-Page test) as part of the change detection schemes, are compared. Further, when JB-Page test is used for change detection, four different compression algorithms, namely, Lempel Ziv Welch (LZW), Lempel Ziv (LZ78), Burrows Wheeler Transform (BWT) and Context Tree Weighting (CTW) algorithms are compared based on their impact on overall compression.

关键词： Compression algorithms Markov processes Probability distribution detection algorithms change detection algorithms Random processes Jacobian matrices

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