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 ...
详细信息
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 changedetection 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 changedetection 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.
作者：
Molloy, Timothy L.QUT
Fac Sci & Engn Sch Elect Engn & Comp Sci Brisbane Qld 4000 Australia
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 (AMCU...
详细信息
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 changedetection 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.
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...
详细信息
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 changedetection 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 changedetection, 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.
In this paper, we propose a novel unsupervised changedetection approach for multitemporal remote sensing (RS) images based on superpixel segmentation and variational Gaussian mixture model (GMM). Firstly, the generat...
详细信息
ISBN:
(纸本)9781538633274
In this paper, we propose a novel unsupervised changedetection 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 changedetection. 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.
In this work, we study the problem of Quickest changedetection 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 a...
详细信息
ISBN:
(数字)9781737749721
ISBN:
(纸本)9781665489416
In this work, we study the problem of Quickest changedetection 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.
In this paper we propose an unsupervised approach to changedetection by computing the difference image directly in the feature spaces. The resulting difference kernel, that is a combination of kernels computed on the...
详细信息
ISBN:
(纸本)9781457710056
In this paper we propose an unsupervised approach to changedetection 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.
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 provid...
详细信息
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.
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...
详细信息
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 changedetection 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 changedetection test proposed in [1] (JB-Page test) as part of the changedetection schemes, are compared. Further, when JB-Page test is used for changedetection, 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.
Feature representation is very important for high resolution synthetic aperture radar (SAR) image interpretation, especially for unsupervised changedetection. In this paper we propose a superpixel-based change detect...
详细信息
ISBN:
(纸本)9781467371193
Feature representation is very important for high resolution synthetic aperture radar (SAR) image interpretation, especially for unsupervised changedetection. In this paper we propose a superpixel-based changedetection approach that utilize region covariance as feature representation. After segmenting SAR images into superpixels, the second order statistic of SAR feature vectors, i.e., the region covariance feature is extracted for each superpixel. In the difference map generation stage, the dissimilarities of corresponding superpixel pairs in multi-temporal SAR images are measured by calculating the Bartlett distances between region covariance features. After that, an adaptive thresholding method is applied to obtain the final detection results. Two multi-temporal TerraSAR-X high resolution SAR image sets are tested for the proposed approach and promising results are achieved.
In this paper, we propose an efficient joint imaging and coherent changedetection (CCD) algorithm to cope with the issue of interrupted synthetic aperture radar (SAR) environments. Information about the changing scen...
详细信息
ISBN:
(纸本)9781538641675
In this paper, we propose an efficient joint imaging and coherent changedetection (CCD) algorithm to cope with the issue of interrupted synthetic aperture radar (SAR) environments. Information about the changing scenes is considered using a partially coherent model, where the structure characteristics of changes are modeled by a Markov random fields (MRF) prior. Then the variational Bayesian expectation-maximization (VBEM) algorithm is employed to simultaneously approximate the posterior distributions of the change map and scene estimates. The proposed scheme has a superior changedetection performance over the classical coherent change detectoris. Representative simulations are conducted to demonstrate the validity of the devised algorithm.
暂无评论