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Dissertations / Theses on the topic 'Change detection'
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Wang, Bo. "Structural change detection via penalized regression." Diss., University of Iowa, 2018. https://ir.uiowa.edu/etd/6520.
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This dissertation research addresses how to detect structural changes in stochastic linear models. By introducing a special structure to the design matrix, we convert the structural change detection problem to a variable selection problem. There are many existing variable selection strategies, however, they do not fully cope with structural change detection. We design two penalized regression algorithms specifically for the structural change detection purpose. We also propose two methods involving these two algorithms to accomplish a bi-level structural change detection: they locate the change points and also recognize which predictors contribute to the variation of the model structure. Extensive simulation studies are shown to demonstrate the effectiveness of the proposed methods in a variety of settings. Furthermore, we establish asymptotic theoretical properties to justify the bi-level detection consistency for one of the proposed methods. In addition, we write an R package with computationally efficient algorithms for detecting structural changes. Comparing to traditional methods, the proposed algorithms showcase enhanced detection power and more estimation precision, with added capacity of specifying the model structures at all regimes.
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Hofer, Heiko. "Large-Scale Gradual Change Detection." Neubiberg Universitätsbibliothek der Universität der Bundeswehr, 2010. http://d-nb.info/1001920856/34.
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Yang, Jiangbin. "Change detection in autocorrelated processes." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape8/PQDD_0009/NQ41349.pdf.
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Comber, Alexis. "Automated land cover change detection." Thesis, University of Aberdeen, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.248628.
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This thesis describes a generic approach for automated land cover monitoring. Knowledge about land cover is acquired through a knowledge acquisition exercise and used to augment image analysis in order to determine land cover change direction. It is demonstrated that taking a task-oriented approach to the change problem avoids the specificity of more traditional data-oriented approaches. The approach described here involves four key analyses that have contributed to the overall problem solution: Identifying the knowledge used to determine different land cover elements (elicitation and modelling); Land cover remote sensing characteristics; Land cover bio-geographic characteristics; Investigation of the most suitable approach for combining evidence. The results of these investigations were applied to semi-natural change problems: evidence about areas known to have changed was reasoned with and change directions identified by applying the knowledge and interpreter rules of thumb. The results were compared with field surveys of the change areas, and were shown to have correctly identified the land cover change direction in each case.
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Bashir, Sulaimon A. "Change detection for activity recognition." Thesis, Robert Gordon University, 2017. http://hdl.handle.net/10059/3104.
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Activity Recognition is concerned with identifying the physical state of a user at a particular point in time. Activity recognition task requires the training of classification algorithm using the processed sensor data from the representative population of users. The accuracy of the generated model often reduces during classification of new instances due to the non-stationary sensor data and variations in user characteristics. Thus, there is a need to adapt the classification model to new user haracteristics. However, the existing approaches to model adaptation in activity recognition are blind. They continuously adapt a classification model at a regular interval without specific and precise detection of the indicator of the degrading performance of the model. This approach can lead to wastage of system resources dedicated to continuous adaptation. This thesis addresses the problem of detecting changes in the accuracy of activity recognition model. The thesis developed a classifier for activity recognition. The classifier uses three statistical summaries data that can be generated from any dataset for similarity based classification of new samples. The weighted ensemble combination of the classification decision from each statistical summary data results in a better performance than three existing benchmarked classification algorithms. The thesis also presents change detection approaches that can detect the changes in the accuracy of the underlying recognition model without having access to the ground truth label of each activity being recognised. The first approach called `UDetect' computes the change statistics from the window of classified data and employed statistical process control method to detect variations between the classified data and the reference data of a class. Evaluation of the approach indicates a consistent detection that correlates with the error rate of the model. The second approach is a distance based change detection technique that relies on the developed statistical summaries data for comparing new classified samples and detects any drift in the original class of the activity. The implemented approach uses distance function and a threshold parameter to detect the accuracy change in the classifier that is classifying new instances. Evaluation of the approach yields above 90% detection accuracy. Finally, a layered framework for activity recognition is proposed to make model adaptation in activity recognition informed using the developed techniques in this thesis.
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Jones, Zygmunt. "Wide-baseline image change detection." Thesis, Imperial College London, 2015. http://hdl.handle.net/10044/1/32406.
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Growth in the prevalence of cameras has resulted in larger amounts of available image data. This has resulted in demand for automated methods of analysing this data. One key area of demand is automated change detection, the automated detection of changes in a scene, as recorded by a reference and sample image. Established methods of change detection tend to rely on the reference and sample image being captured from the same position, but much of the available data does not fit this criteria. This thesis presents novel approaches to key challenges in wide-baseline cases involving differences in viewing angle of up to 30 degrees, including registration and the image region matching that are robust to the inherent registration errors. The developed algorithms are then combined into an end-to-end system. This thesis presents novel registration approaches including the use of a Delaunay triangulation mask that enables registration of each component triangle, a method of finding local planes in scenes by clustering matched feature points, the use of edge detection to register the edges of objects, and a method for registering planes that are orthogonal to a defined image plane and to the camera line. These techniques allow for the registration of complex 3D scenes with viewing angles of up to 30 degrees. The density of the available correspondences obtained using feature points is a key limiting factor in these methods and so ASIFT, a extension to the SIFT feature point that improves performance at wide angles is also introduced. ASIFT is shown to have an order of magnitude increase in correctly matches feature point density at 30 degrees. Though robust to wide differences in viewing angle, these registration techniques do nonetheless introduce registration errors of up to a few dozen pixels. For this reason the dense SIFT and shifted dense SIFT image comparison algorithms which are robust to registration errors of a few dozen pixels are also developed. The development of these comparison methods includes an analysis of SIFT descriptor statistics and their correlation. Finally these techniques are combined to form an end-to-end change detection system which is evaluated on a number of test datasets.
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Almutairi, Abdullah. "Monitoring land-cover change detection in an arid urban environment a comparison of change detection techniques /." Morgantown, W. Va. : [West Virginia University Libraries], 2000. http://etd.wvu.edu/templates/showETD.cfm?recnum=1410.
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Thesis (M.A.)--West Virginia University, 2000.Title from document title page. Document formatted into pages; contains xi, 77 p. : ill. (some col.), maps (some col.) Includes abstract. Includes bibliographical references (p. 74-77).
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Vongsy, Karmon Marie. "CHANGE DETECTION METHODS FOR HYPERSPECTRAL IMAGERY." Wright State University / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=wright1184010751.
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Yousif, Osama. "Change Detection Using Multitemporal SAR Images." Licentiate thesis, KTH, Geodesi och geoinformatik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-123494.
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Multitemporal SAR images have been used successfully for the detection of different types of environmental changes. The detection of urban change using SAR images is complicated due to the special characteristics of SAR images—for example, the existence of speckle and the complex mixture of the urban environment. This thesis investigates the detection of urban changes using SAR images with the following specific objectives: (1) to investigate unsupervised change detection, (2) to investigate reduction of the speckle effect and (3) to investigate spatio-contextual change detection. Beijing and Shanghai, the largest cities in China, were selected as study areas. Multitemporal SAR images acquired by ERS-2 SAR (1998~1999) and Envisat ASAR (2008~2009) sensors were used to detect changes that have occurred in these cities. Unsupervised change detection using SAR images is investigated using the Kittler-Illingworth algorithm. The problem associated with the diversity of urban changes—namely, more than one typology of change—is addressed using the modified ratio operator. This operator clusters both positive and negative changes on one side of the change-image histogram. To model the statistics of the changed and the unchanged classes, four different probability density functions were tested. The analysis indicates that the quality of the resulting change map will strongly depends on the density model chosen. The analysis also suggests that use of a local adaptive filter (e.g., enhanced Lee) removes fine geometric details from the scene. Speckle suppression and geometric detail preservation in SAR-based change detection, are addressed using the nonlocal means (NLM) algorithm. In this algorithm, denoising is achieved through a weighted averaging process, in which the weights are a function of the similarity of small image patches defined around each pixel in the image. To decrease the computational complexity, the PCA technique is used to reduce the dimensionality of the neighbourhood feature vectors. Simple methods to estimate the dimensionality of the new space and the required noise variance are proposed. The experimental results show that the NLM algorithm outperformed traditional local adaptive filters (e.g., enhanced Lee) in eliminating the effect of speckle and in maintaining the geometric structures in the scene. The analysis also indicates that filtering the change variable instead of the individual SAR images is effective in terms of both the quality of the results and the time needed to carry out the computation. The third research focuses on the application of Markov random field (MRF) in change detection using SAR images. The MRF-based change detection algorithm shows limited capacity to simultaneously maintain fine geometric detail in urban areas and combat the effect of speckle noise. This problem has been addressed through the introduction of a global constraint on the pixels’ class labels. Based on NLM theory, a global probability model is developed. The iterated conditional mode (ICM) scheme for the optimization of the MAP-MRF criterion function is extended to include a step that forces the maximization of the global probability model. The experimental results show that the proposed algorithm is better at preserving the fine structural detail, effective in reducing the effect of speckle, less sensitive to the value of the contextual parameter, and less affected by the quality of the initial change map compared with traditional MRF-based change detection algorithm.QC 20130610
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Brolin, Morgan. "Automatic Change Detection in Visual Scenes." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-301611.
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This thesis proposes a Visual Scene Change Detector(VSCD) system which is a system which involves four parts, image retrieval, image registration, image change detection and panorama creation. Two prestudies are conducted in order to find a proposed image registration method and a image retrieval method. The two found methods are then combined with a proposed image registration method and a proposed panorama creation method to form the proposed VSCD. The image retrieval prestudy evaluates a SIFT related method with a bag of words related method and finds the SIFT related method to be the superior method. The image change detection prestudy evaluates 8 different image change detection methods. Result from the image change detection prestudy shows that the methods performance is dependent on the image category and an ensemble method is the least dependent on the category of images. An ensemble method is found to be the best performing method followed by a range filter method and then a Convolutional Neural Network (CNN) method. Using a combination of the 2 image retrieval methods and the 8 image change detection method 16 different VSCD are formed and tested. The final result show that the VSCD comprised of the best methods from the prestudies is the best performing method.Detta exjobb föreslår ett Visual Scene Change Detector(VSCD) system vilket är ett system som har 4 delar, image retrieval, image registration, image change detection och panorama creation. Två förstudier görs för att hitta en föreslagen image registration metod och en föreslagen panorama creation metod. De två föreslagna delarna kombineras med en föreslagen image registration och en föreslagen panorama creation metod för att utgöra det föreslagna VSCD systemet. Image retrieval förstudien evaluerar en ScaleInvariant Feature Transform (SIFT) relaterad method med en Bag of Words (BoW) relaterad metod och hittar att den SIFT relaterade methoden är bäst. Image change detection förstudie visar att metodernas prestanda är beroende av catagorin av bilder och att en enemble metod är minst beroende av categorin av bilder. Enemble metoden är hittad att vara den bästa presterande metoden följt av en range filter metod och sedan av en CNN metod. Genom att använda de 2 image retrieval metoder kombinerat med de 8 image change detection metoder är 16 st VSCD system skapade och testade. Sista resultatet visar att den VSCD som använder de bästa metoderna från förstudien är den bäst presterande VSCD.
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Garreau, Damien. "Change-point detection and kernel methods." Thesis, Paris Sciences et Lettres (ComUE), 2017. http://www.theses.fr/2017PSLEE061/document.
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Dans cette thèse, nous nous intéressons à une méthode de détection des ruptures dans une suite d’observations appartenant à un ensemble muni d’un noyau semi-défini positif. Cette procédure est une version « à noyaux » d’une méthode des moindres carrés pénalisés. Notre principale contribution est de montrer que, pour tout noyau satisfaisant des hypothèses raisonnables, cette méthode fournit une segmentation proche de la véritable segmentation avec grande probabilité. Ce résultat est obtenu pour un noyau borné et une pénalité linéaire, ainsi qu’une autre pénalité venant de la sélection de modèles. Les preuves reposent sur un résultat de concentration pour des variables aléatoires bornées à valeurs dans un espace de Hilbert, et nous obtenons une version moins précise de ce résultat lorsque l’on supposeseulement que la variance des observations est finie. Dans un cadre asymptotique, nous retrouvons les taux minimax usuels en détection de ruptures lorsqu’aucune hypothèse n’est faite sur la taille des segments. Ces résultats théoriques sont confirmés par des simulations. Nous étudions également de manière détaillée les liens entre différentes notions de distances entre segmentations. En particulier, nous prouvons que toutes ces notions coïncident pour des segmentations suffisamment proches. D’un point de vue pratique, nous montrons que l’heuristique du « saut de dimension » pour choisir la constante de pénalisation est un choix raisonnable lorsque celle-ci est linéaire. Nous montrons également qu’une quantité clé dépendant du noyau et qui apparaît dans nos résultats théoriques influe sur les performances de cette méthode pour la détection d’une unique rupture. Dans un cadre paramétrique, et lorsque le noyau utilisé est invariant partranslation, il est possible de calculer cette quantité explicitement. Grâce à ces calculs, nouveaux pour plusieurs d’entre eux, nous sommes capable d’étudier précisément le comportement de la constante de pénalité maximale. Pour finir, nous traitons de l’heuristique de la médiane, un moyen courant de choisir la largeur de bande des noyaux à base de fonctions radiales. Dans un cadre asymptotique, nous montrons que l’heuristique de la médiane se comporte à la limite comme la médiane d’une distribution que nous décrivons complètement dans le cadre du test à deux échantillons à noyaux et de la détection de ruptures. Plus précisément, nous montrons que l’heuristique de la médiane est approximativement normale centrée en cette valeurIn this thesis, we focus on a method for detecting abrupt changes in a sequence of independent observations belonging to an arbitrary set on which a positive semidefinite kernel is defined. That method, kernel changepoint detection, is a kernelized version of a penalized least-squares procedure. Our main contribution is to show that, for any kernel satisfying some reasonably mild hypotheses, this procedure outputs a segmentation close to the true segmentation with high probability. This result is obtained under a bounded assumption on the kernel for a linear penalty and for another penalty function, coming from model selection.The proofs rely on a concentration result for bounded random variables in Hilbert spaces and we prove a less powerful result under relaxed hypotheses—a finite variance assumption. In the asymptotic setting, we show that we recover the minimax rate for the change-point locations without additional hypothesis on the segment sizes. We provide empirical evidence supporting these claims. Another contribution of this thesis is the detailed presentation of the different notions of distances between segmentations. Additionally, we prove a result showing these different notions coincide for sufficiently close segmentations.From a practical point of view, we demonstrate how the so-called dimension jump heuristic can be a reasonable choice of penalty constant when using kernel changepoint detection with a linear penalty. We also show how a key quantity depending on the kernelthat appears in our theoretical results influences the performance of kernel change-point detection in the case of a single change-point. When the kernel is translationinvariant and parametric assumptions are made, it is possible to compute this quantity in closed-form. Thanks to these computations, some of them novel, we are able to study precisely the behavior of the maximal penalty constant. Finally, we study the median heuristic, a popular tool to set the bandwidth of radial basis function kernels. Fora large sample size, we show that it behaves approximately as the median of a distribution that we describe completely in the setting of kernel two-sample test and kernel change-point detection. More precisely, we show that the median heuristic is asymptotically normal around this value
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Braganza, Karl 1971. "Climate change detection and attribution using simple global indices." Monash University, School of Mathematical Sciences, 2002. http://arrow.monash.edu.au/hdl/1959.1/7783.
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Behrens, Richard J. "Change detection analysis with spectral thermal imagery." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1998. http://handle.dtic.mil/100.2/ADA356044.
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Thesis (M.S. in Space Systems Operations) Naval Postgraduate School, September 1998."September 1998." Thesis advisor(s): Richard Christopher Olsen, David D. Cleary. Includes bibliographical references (p. 129-131). Also available online.
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Carvalho, Bittencourt André. "Friction change detection in industrial robot arms." Thesis, KTH, Reglerteknik, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-106230.
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Industrial robots have been used as a key factor to improve productivity, quality and safety in manufacturing. Many tasks can be done by industrial robots and they usually play an important role in the system they are used, a robot stop or malfunction can compromise the whole plant as well as cause personal damages. The reliability of the system is therefore very important. Nevertheless, the tools available for maintenance of industrial robots are usually based on periodical inspection or a life time table, and do not consider the robot’s actual conditions. The use of condition monitoring and fault detection would then improve diagnosis. The main objective of this thesis is to define a parameter based diagnosis method for industrial robots. In the approach presented here, the friction phenomena is monitored and used to estimate relevant parameters that relate faults in the system. To achieve the task, the work first presents robot and friction models suitable to use in the diagnosis. The models are then identified with several different identification methods, considering the most suitable for the application sought. In order to gather knowledge about how disturbances and faults affect the friction phenomena, several experiments have been done revealing the main influences and their behavior. Finally, considering the effects caused by faults and disturbances, the models and estimation methods proposed, a fault detection scheme is built in order to detect three kind of behavioral modes of a robot (normal operation, increased friction and high increased friction), which is validated within some real scenarios.
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Fröjse, Linda. "Multitemporal Satellite Images for Urban Change Detection." Thesis, KTH, Geoinformatik och Geodesi, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-38539.
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The objective of this research is to detect change in urban areas using two satellite images (from 2001 and 2010) covering the city of Shanghai, China. These satellite images were acquired by Landsat-7 and HJ-1B, two satellites with different sensors. Two change detection algorithms were tested: image differencing and post-classification comparison. For image differencing the difference image was classified using unsupervised k-means classification, the classes were then aggregated into change and no change by visual inspection. For post-classification comparison the images were classified using supervised maximum likelihood classification and then the difference image of the two classifications were classified into change and no change also by visual inspection. Image differencing produced result with poor overall accuracy (band 2: 24.07%, band 3: 25.96%, band 4: 46.93%), while post-classification comparison produced result with better overall accuracy (90.96%). Post-classification comparison works well with images from different sensors, but it relies heavily on the accuracy of the classification. The major downside of the methodology of both algorithms was the large amount of visual inspection.
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Irie, Kenji. "Noise-limited scene-change detection in images." Diss., Lincoln University, 2009. http://hdl.handle.net/10182/1351.
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This thesis describes the theoretical, experimental, and practical aspects of a noise-limited method for scene-change detection in images. The research is divided into three sections: noise analysis and modelling, dual illumination scene-change modelling, and integration of noise into the scene-change model. The sources of noise within commercially available digital cameras are described, with a new model for image noise derived for charge-coupled device (CCD) cameras. The model is validated experimentally through the development of techniques that allow the individual noise components to be measured from the analysis of output images alone. A generic model for complementary metal-oxide-semiconductor (CMOS) cameras is also derived. Methods for the analysis of spatial (inter-pixel) and temporal (intra-pixel) noise are developed. These are used subsequently to investigate the effects of environmental temperature on camera noise. Based on the cameras tested, the results show that the CCD camera noise response to variation in environmental temperature is complex whereas the CMOS camera response simply increases monotonically. A new concept for scene-change detection is proposed based upon a dual illumination concept where both direct and ambient illumination sources are present in an environment, such as that which occurs in natural outdoor scenes with direct sunlight and ambient skylight. The transition of pixel colour from the combined direct and ambient illuminants to the ambient illuminant only is modelled. A method for shadow-free scene-change is then developed that predicts a pixel's colour when the area in the scene is subjected to ambient illumination only, allowing pixel change to be distinguished as either being due to a cast shadow or due to a genuine change in the scene. Experiments on images captured in controlled lighting demonstrate 91% of scene-change and 83% of cast shadows are correctly determined from analysis of pixel colour change alone. A statistical method for detecting shadow-free scene-change is developed. This is achieved by bounding the dual illumination model by the confidence interval associated with the pixel's noise. Three benefits arise from the integration of noise into the scene-change detection method: - The necessity for pre-filtering images for noise is removed; - All empirical thresholds are removed; and - Performance is improved. The noise-limited scene-change detection algorithm correctly classifies 93% of scene-change and 87% of cast shadows from pixel colour change alone. When simple post-analysis size-filtering is applied both these figures increase to 95%.
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Yelisetty, Sree Ramya Namuduri Kameswara. "Image change detection using wireless sensor network." Diss., A link to full text of this thesis in SOAR, 2007. http://soar.wichita.edu/dspace/handle/10057/1185.
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Thesis (M.S.)--Wichita State University, College of Engineering, Dept. of Electrical and Computer Engineering."May 2007." Title from PDF title page (viewed on Dec. 29, 2007). Thesis adviser: Kamesh Namuduri. Includes bibliographic references (leaves 37-40).
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Jabor, Abbas. "Novelty and change detection radiation physics experiments." Licentiate thesis, Stockholm : Fysiska institutionen, Kungliga Tekniska högskolan, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-4410.
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Yousif, Osama. "Urban Change Detection Using Multitemporal SAR Images." Doctoral thesis, KTH, Geoinformatik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-168216.
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Multitemporal SAR images have been increasingly used for the detection of different types of environmental changes. The detection of urban changes using SAR images is complicated due to the complex mixture of the urban environment and the special characteristics of SAR images, for example, the existence of speckle. This thesis investigates urban change detection using multitemporal SAR images with the following specific objectives: (1) to investigate unsupervised change detection, (2) to investigate effective methods for reduction of the speckle effect in change detection, (3) to investigate spatio-contextual change detection, (4) to investigate object-based unsupervised change detection, and (5) to investigate a new technique for object-based change image generation. Beijing and Shanghai, the largest cities in China, were selected as study areas. Multitemporal SAR images acquired by ERS-2 SAR and ENVISAT ASAR sensors were used for pixel-based change detection. For the object-based approaches, TerraSAR-X images were used. In Paper I, the unsupervised detection of urban change was investigated using the Kittler-Illingworth algorithm. A modified ratio operator that combines positive and negative changes was used to construct the change image. Four density function models were tested and compared. Among them, the log-normal and Nakagami ratio models achieved the best results. Despite the good performance of the algorithm, the obtained results suffer from the loss of fine geometric detail in general. This was a consequence of the use of local adaptive filters for speckle suppression. Paper II addresses this problem using the nonlocal means (NLM) denoising algorithm for speckle suppression and detail preservation. In this algorithm, denoising was achieved through a moving weighted average. The weights are a function of the similarity of small image patches defined around each pixel in the image. To decrease the computational complexity, principle component analysis (PCA) was used to reduce the dimensionality of the neighbourhood feature vectors. Simple methods to estimate the number of significant PCA components to be retained for weights computation and the required noise variance were proposed. The experimental results showed that the NLM algorithm successfully suppressed speckle effects, while preserving fine geometric detail in the scene. The analysis also indicates that filtering the change image instead of the individual SAR images was effective in terms of the quality of the results and the time needed to carry out the computation. The Markov random field (MRF) change detection algorithm showed limited capacity to simultaneously maintain fine geometric detail in urban areas and combat the effect of speckle. To overcome this problem, Paper III utilizes the NLM theory to define a nonlocal constraint on pixels class-labels. The iterated conditional mode (ICM) scheme for the optimization of the MRF criterion function is extended to include a new step that maximizes the nonlocal probability model. Compared with the traditional MRF algorithm, the experimental results showed that the proposed algorithm was superior in preserving fine structural detail, effective in reducing the effect of speckle, less sensitive to the value of the contextual parameter, and less affected by the quality of the initial change map. Paper IV investigates object-based unsupervised change detection using very high resolution TerraSAR-X images over urban areas. Three algorithms, i.e., Kittler-Illingworth, Otsu, and outlier detection, were tested and compared. The multitemporal images were segmented using multidate segmentation strategy. The analysis reveals that the three algorithms achieved similar accuracies. The achieved accuracies were very close to the maximum possible, given the modified ratio image as an input. This maximum, however, was not very high. This was attributed, partially, to the low capacity of the modified ratio image to accentuate the difference between changed and unchanged areas. Consequently, Paper V proposes a new object-based change image generation technique. The strong intensity variations associated with high resolution and speckle effects render object mean intensity unreliable feature. The modified ratio image is, therefore, less efficient in emphasizing the contrast between the classes. An alternative representation of the change data was proposed. To measure the intensity of change at the object in isolation of disturbances caused by strong intensity variations and speckle effects, two techniques based on the Fourier transform and the Wavelet transform of the change signal were developed. Qualitative and quantitative analyses of the result show that improved change detection accuracies can be obtained by classifying the proposed change variables.QC 20150529
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Li, Qing. "Recurrent-Event Models for Change-Points Detection." Diss., Virginia Tech, 2015. http://hdl.handle.net/10919/78207.
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The driving risk of novice teenagers is the highest during the initial period after licensure but decreases rapidly. This dissertation develops recurrent-event change-point models to detect the time when driving risk decreases significantly for novice teenager drivers. The dissertation consists of three major parts: the first part applies recurrent-event change-point models with identical change-points for all subjects; the second part proposes models to allow change-points to vary among drivers by a hierarchical Bayesian finite mixture model; the third part develops a non-parametric Bayesian model with a Dirichlet process prior. In the first part, two recurrent-event change-point models to detect the time of change in driving risks are developed. The models are based on a non-homogeneous Poisson process with piecewise constant intensity functions. It is shown that the change-points only occur at the event times and the maximum likelihood estimators are consistent. The proposed models are applied to the Naturalistic Teenage Driving Study, which continuously recorded textit{in situ} driving behaviour of 42 novice teenage drivers for the first 18 months after licensure using sophisticated in-vehicle instrumentation. The results indicate that crash and near-crash rate decreases significantly after 73 hours of independent driving after licensure. The models in part one assume identical change-points for all drivers. However, several studies showed that different patterns of risk change over time might exist among the teenagers, which implies that the change-points might not be identical among drivers. In the second part, change-points are allowed to vary among drivers by a hierarchical Bayesian finite mixture model, considering that clusters exist among the teenagers. The prior for mixture proportions is a Dirichlet distribution and a Markov chain Monte Carlo algorithm is developed to sample from the posterior distributions. DIC is used to determine the best number of clusters. Based on the simulation study, the model gives fine results under different scenarios. For the Naturalist Teenage Driving Study data, three clusters exist among the teenagers: the change-points are 52.30, 108.99 and 150.20 hours of driving after first licensure correspondingly for the three clusters; the intensity rates increase for the first cluster while decrease for other two clusters; the change-point of the first cluster is the earliest and the average intensity rate is the highest. In the second part, model selection is conducted to determine the number of clusters. An alternative is the Bayesian non-parametric approach. In the third part, a Dirichlet process Mixture Model is proposed, where the change-points are assigned a Dirichlet process prior. A Markov chain Monte Carlo algorithm is developed to sample from the posterior distributions. Automatic clustering is expected based on change-points without specifying the number of latent clusters. Based on the Dirichlet process mixture model, three clusters exist among the teenage drivers for the Naturalistic Teenage Driving Study. The change-points of the three clusters are 96.31, 163.83, and 279.19 hours. The results provide critical information for safety education, safety countermeasure development, and Graduated Driver Licensing policy making.Ph. D.
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Durkee, Nicholas A. "Temperature Robust Longwave Infrared Hyperspectral Change Detection." Wright State University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=wright1547481549821121.
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Niu, Yue S., Ning Hao, and Heping Zhang. "Multiple Change-Point Detection: A Selective Overview." INST MATHEMATICAL STATISTICS, 2016. http://hdl.handle.net/10150/622820.
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Very long and noisy sequence data arise from biological sciences to social science including high throughput data in genomics and stock prices in econometrics. Often such data are collected in order to identify and understand shifts in trends, for example, from a bull market to a bear market in finance or from a normal number of chromosome copies to an excessive number of chromosome copies in genetics. Thus, identifying multiple change points in a long, possibly very long, sequence is an important problem. In this article, we review both classical and new multiple change-point detection strategies. Considering the long history and the extensive literature on the change-point detection, we provide an in-depth discussion on a normal mean change-point model from aspects of regression analysis, hypothesis testing, consistency and inference. In particular, we present a strategy to gather and aggregate local information for change-point detection that has become the cornerstone of several emerging methods because of its attractiveness in both computational and theoretical properties.
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Björk, Tim. "Exploring Change Point Detection in Network Equipment Logs." Thesis, Karlstads universitet, Institutionen för matematik och datavetenskap (from 2013), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-85626.
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Change point detection (CPD) is the method of detecting sudden changes in timeseries, and its importance is great concerning network traffic. With increased knowledge of occurring changes in data logs due to updates in networking equipment,a deeper understanding is allowed for interactions between the updates and theoperational resource usage. In a data log that reflects the amount of network traffic, there are large variations in the time series because of reasons such as connectioncount or external changes to the system. To circumvent these unwanted variationchanges and assort the deliberate variation changes is a challenge. In this thesis, we utilize data logs retrieved from a network equipment vendor to detect changes, then compare the detected changes to when firmware/signature updates were applied, configuration changes were made, etc. with the goal to achieve a deeper understanding of any interaction between firmware/signature/configuration changes and operational resource usage. Challenges in the data quality and data processing are addressed through data manipulation to counteract anomalies and unwanted variation, as well as experimentation with parameters to achieve the most ideal settings. Results are produced through experiments to test the accuracy of the various change pointdetection methods, and for investigation of various parameter settings. Through trial and error, a satisfactory configuration is achieved and used in large scale log detection experiments. The results from the experiments conclude that additional information about how changes in variation arises is required to derive the desired understanding.
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Korotkov, Konstantin. "Automatic change detection in multiple pigmented skin lesions." Doctoral thesis, Universitat de Girona, 2014. http://hdl.handle.net/10803/260162.
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Malignant melanoma is the rarest and deadliest of skin cancers causing three times more deaths than all other skin-related malignancies combined. Fortunately, in its early stages, it is completely curable, making a total body skin examination (TBSE) a fundamental procedure for many patients. Despite the advances in body scanning techniques, automated assistance tools for TBSEs have not received due attention. This fact is emphasized in our literature review covering the area of computerized analysis of PSL images. Aiming at the automation of TBSEs, we have designed and built a total body scanner to acquire skin surface images using cross-polarized light. Furthermore, we have developed an algorithm for the automated mapping of PSLs and their change estimation between explorations. The initial tests of the scanner showed that it can be successfully applied for automated mapping and temporal monitoring of multiple lesionsEl melanoma maligne és el més rar i mortal de tots els càncers de pell, causant tres vegades més morts que el conjunt de totes les altres malalties malignes de la pell. Afortunadament, en les primeres etapes, és completament curable, fent de les exploracions de pell a nivell de cos complert (TBSE en anglès) un procés fonamental per a molts pacients. Malgrat els avenços en les tècniques d’escaneig cutani, les eines per a realitzar TBSEs de forma automàtica no han rebut massa atenció. Per tant, hem dissenyat i construït un escàner corporal de cobertura total per adquirir imatges de la superfície de la pell utilitzant llum amb polarització creuada. A més, hem desenvolupat un algoritme pel mapeig automàtic de les PSLs i l’estimació dels canvis entre exploracions. Els tests inicials de l’escàner mostren que aquest pot ésser utilitzat satisfactòriament pel mapeig automàtic i el control de canvis temporal de múltiples lesions
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Egea-Roca, Daniel. "Change detection techniques for GNSS signal-level integrity." Doctoral thesis, Universitat Autònoma de Barcelona, 2017. http://hdl.handle.net/10803/458425.
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El gran éxito y la facilidad de uso de los sistemas de navegación global por satélite (GNSSs) ha conducido a la definición de una gran cantidad de aplicaciones basadas en GNSS sin precedentes. De hecho, la tendencia muestra una nueva era de aplicaciones basadas en GNSS, las denominadas aplicaciones críticas, en las que la integridad física de los usuarios puede estar en riesgo en caso de un fallo del sistema. Un requisito importante en estas aplicaciones es la integridad, definida como una medida de la fiabilidad y confianza que se tiene en la información proporcionada por el sistema. Los primeros algoritmos de integridad fueron diseñados para trabajar en entornos aéreos, en concreto para aviación civil. Desafortunadamente, las aplicaciones críticas basadas en GNSS suelen estar asociadas con entornos terrestres y por lo tanto los algoritmos de integridad tradicionales suelen fallar. El principal motivo son los efectos locales como interferencias, multi-camino o el denominado spoofing que nos podemos encontrar en entornos terrestres. Estos efectos se asumen que están controlados en aviación civil, pero ese no es el caso en entornos terrestres. De este modo, se necesitan nuevas técnicas de integridad para aplicaciones críticas basadas en GNSS, la denominada integridad a nivel de señal (signal-level integrity).
Esta tesis investiga nuevos algoritmos de detección con el objetivo de proporcionar una nueva generación de técnicas de integridad en GNSS. Para ello, se considera el campo de detección de cambios estadísticos (SCD). Este campo es de interés porque considera la dimensión temporal, requisito indispensable para aplicaciones críticas ya que una detección rápida es necesaria. Por lo tanto, la primera parte de esta tesis se ocupa del estudio del campo de SCD, incluyendo tanto la detección rápida de cambios (QCD) como la detección de cambios transitorios (TCD). Se aportan nuevas contribuciones en el campo de TCD, incluyendo la denominada solución FMA y su caracterización estadística. Además, resultados numéricos muestran la superioridad de nuestras contribuciones con respecto otras contribuciones en la literatura de TCD. Finalmente, para concluir nuestro estudio de SCD, lo comparamos con esquemas clásicos de detección bajo el mismo marco matemático. Esta comparación muestra la conveniencia de SCD cuando se trata de detecciones rápida.
La principal contribución de esta tesis es la aplicación del campo de SCD a la detección de amenazas e integridad en GNSS. Para ello, primero investigamos varias propiedades de la señal GNSS que pueden ser de utilidad para la detección de amenazas locales. En segundo lugar, damos un paso adelante en el campo de detección de amenazas en GNSS proponiendo un nuevo marco basado en QCD. Sin embargo, para fines de integridad es deseable un retardo limitado y es aquí donde la teoría de TCD es interesante. Por esta razón, se considera un nuevo marco basado en TCD para la detección de multi-camino y algoritmos de integridad en GNSS, lo que conduce a la provisión de la integridad de nivel de señal. Se muestra una mejora notable por la soluciones propuestas de TCD con respecto a las soluciones actuales. En la última parte de la tesis, se validan los detectores de amenazas y el algoritmo de integridad a nivel de señal propuestos. Esto se hace utilizando seles GNSS reales capturadas en el contexto de un proyecto de investigación financiado por la Comisión Europea. Los resultados obtenidos en un escenario realista muestran la mejora de la precisión y la integridad mediante el uso de la solución propuesta con respecto a los algoritmos de integridad actuales. Además, se muestra que la solución propuesta trabaja en tiempo real, siendo por lo tanto muy atractiva para mejorar los algoritmos de integridad actuales y fácilmente implementables.The provision of accurate positioning is becoming essential to our modern society. One of the main reasons is the great success and ease of use of Global Navigation Satellite Systems (GNSSs), which has led to an unprecedented amount of GNSS-based applications. In particular, the current trend shows that a new era of GNSS-based applications and services is emerging. These applications are the so-called critical applications, in which the physical safety of users may be in danger due to a miss-performance of the system. These applications have stringent requirements in terms of integrity, which is a measure of reliability and trust that can be placed on the information provided by the system. Unfortunately, GNSS-based critical applications are usually associated with terrestrial environments and original integrity algorithms usually fail. The main impairments are due to local effects such as interference, multipath or spoofing, which are assumed to be controlled in civil aviation but they are not in terrestrial environments. Thus, a new methodology for integrity is necessary in order to detect local effects and provide the additional level of integrity needed for GNSS-based critical applications; the so-called signal-level integrity. This thesis investigates novel detection algorithms with the aim of providing a new generation of integrity techniques in GNSS. For this purpose, the framework of Statistical Change Detection (SCD) is considered. This framework is of particular interest because its optimal criterion target the temporal dimension. This is an indispensable requirement for critical applications, in which a prompt detection is necessary. Therefore, the first part of this dissertation deals with the study of the field of SCD, including both Quickest Change Detection (QCD) and Transient Change Detection (TCD). Novel contributions are provided in the field of TCD, including the finite moving average solution and its statistical characterization. Numerical results show the superiority of our contributions. Finally, to conclude our study of SCD we compare it with classical detection schemes under the same mathematical framework. This comparison shows the appropriateness of SCD when dealing with timely detections. The main contribution of this thesis is the application of the SCD framework to threat detection and integrity in GNSS. To this end, we first investigate several properties of the received GNSS signal that may be useful for local threat detection. This leads us to move a step forward in the field of threat detection by proposing a novel QCD-based framework. Nonetheless, for integrity purposes a bounded delay is desirable, and it is here where TCD is of interest. For this reason, a novel TCD-based framework is considered for both multipath detection and integrity algorithms in GNSS, thus leading to the provision of signal-level integrity. A notable improvement is shown by the proposed TCD-based solutions considered in this thesis with respect to current solutions. In the last part of the thesis, the goal is to validate the proposed threat detectors and signal-level integrity algorithm using real GNSS signals. Real signal gathered in the context of an EC-funded research project is processed to show and validate the results of the implemented detectors. The results obtained in a realistic scenario show the improvement of the accuracy and integrity by using the proposed solution for signal-level integrity, with respect to current integrity algorithms. Furthermore, the proposed solution is shown to have real-time processing capabilities, thus being very attractive to improve current integrity algorithms and easily implementable in mass-market receivers.
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Hermans, Filip J. J. "Robust change detection in automotive & aerospace systems." Thesis, University of Manchester, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.580328.
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This thesis is a study of the use of fault detection in automotive and aerospace applications. It investigates three applications in particular. They are engine misfire detection, suspension monitoring and gas turbine sensor monitoring. The main aim is to produce robust monitoring algorithms for these systems and extract typical features which will facilitate the generation of monitoring solutions for similar but not directly related applications. To treat these problems, the thesis is divided into four main parts. They are as follows: Engine Misfire Detection: This part states the problem of monitoring the input of a continuous system, using measurable outputs, knowing that the input signal has a certain periodicity. The aim is to detect anomalies in the periodicity of the input. For the misfire application, the output is the engine crankshaft speed, the period the engine cycle and the anomaly in the periodic component the misfire. The anomalies are detected using signal processing methods based on recursive-vector least squares with discontinuous forgetting and generalised likelihood ratio. This detection algorithm augmented by a small look-up table is tested on real data representing a variety of driving conditions. Suspension Monitoring: The problem can be considered as that of monitoring the physical parameters of a continuous system. The monitoring is done based on a set of fixed models and some statistical evaluation. The models are formulated in discrete time using the h -operator. This leads to the generation of Kalman filter and Descriptor Kalman filter algorithms using the h-operator. For robustness and diagnostic reasons, a new criteria is proposed involving a sensitivity matrix. This allows simultaneous detection of several parameter changes independently. For the statistical evaluation, a new noise invariant cusum is introduced. The algorithm is finally tested on the tyre pressure and damper monitoring problem using simulations and real test rig and car data. Gas Turbine Sensor Monitoring: The aim is to monitor the sensors of non-linear time variant systems using a set of linear models i.e. robust monitoring. To incorporate robustness, the sliding mode observer is introduced and its fault detection capabilities investigated. This is done first using a simple but illustrative example and then using the real gas turbine data. Both illustrations show the advantages of the sliding mode observer. Sliding Mode Estimator: This part introduces the use of sliding mode principles for parameter estimation. This results in a sliding mode estimator. The tracking and decoupling capabilities of the sliding mode estimator are further compared with RLS. This is done using a simple example where the parameters are varying dependently, independently and/or abruptly. To substantiate the potential of the new estimator, the suspension monitoring problem is revisited with successful results.
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Wang, Chaoming. "THERMAL DETECTION OF BIOMARKERS USING PHASE CHANGE NANOPARTICLES." Master's thesis, University of Central Florida, 2010. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/3877.
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Most of existing techniques cannot be used to detect molecular biomarkers (i.e., protein and DNA) contained in complex body fluids due to issues such as enzyme inhibition or signal interference. This thesis describes a nanoparticle-based thermal detection method for the highly sensitive detections of multiple DNA biomarkers or proteins contained in different type of fluids such as buffer solution, cell lysate and milk by using solid-liquid phase change nanoparticles as thermal barcodes. Besides, this method has also been applied for thrombin detection by using RNA aptamer-functionalized phase change nanoparticles as thermal probes. Furthermore, using nanostructured Si surface that have higher specific area can enhance the detection sensitivity by four times compared to use flat aluminum surfaces. The detection is based on the principle that the temperature of solid will not rise above its melting temperature unless all solid is molten, thus nanoparticles will have sharp melting peak during a linear thermal scan process. A one-to-one correspondence can be created between one type of nanoparticles and one type of biomarker, and multiple biomarkers can be detected simultaneously using different type nanoparticles. The melting temperature and the heat flow reflect the type and the concentration of biomarker, respectively. The melting temperatures of nanoparticles are designed to be over 100°C to avoid interference from species contained in fluids. The use of thermal nanoparticles allows detection of multiple low concentration DNAs or proteins in a complex fluid such as cell lysate regardless of the color, salt concentration, and conductivity of the sample.M.S.
Department of Mechanical, Materials and Aerospace Engineering;
Engineering and Computer Science
Materials Science & Engr MSMSE
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Geng, Jun. "Quickest Change-Point Detection with Sampling Right Constraints." Digital WPI, 2015. https://digitalcommons.wpi.edu/etd-dissertations/440.
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The quickest change-point detection problems with sampling right constraints are considered. Specially, an observer sequentially takes observations from a random sequence, whose distribution will change at an unknown time. Based on the observation sequence, the observer wants to identify the change-point as quickly as possible. Unlike the classic quickest detection problem in which the observer can take an observation at each time slot, we impose a causal sampling right constraint to the observer. In particular, sampling rights are consumed when the observer takes an observation and are replenished randomly by a stochastic process. The observer cannot take observations if there is no sampling right left. The causal sampling right constraint is motivated by several practical applications. For example, in the application of sensor network for monitoring the abrupt change of its ambient environment, the sensor can only take observations if it has energy left in its battery. With this additional constraint, we design and analyze the optimal detection and sampling right allocation strategies to minimize the detection delay under various problem setups. As one of our main contributions, a greedy sampling right allocation strategy, by which the observer spends sampling rights in taking observations as long as there are sampling rights left, is proposed. This strategy possesses a low complexity structure, and leads to simple but (asymptotically) optimal detection algorithms for the problems under consideration. Specially, our main results include: 1) Non-Bayesian quickest change-point detection: we consider non-Bayesian quickest detection problem with stochastic sampling right constraint. Two criteria, namely the algorithm level average run length (ARL) and the system level ARL, are proposed to control the false alarm rate. We show that the greedy sampling right allocation strategy combined with the cumulative sum (CUSUM) algorithm is optimal for Lorden's setup with the algorithm level ARL constraint and is asymptotically optimal for both Lorden's and Pollak's setups with the system level ARL constraint. 2) Bayesian quickest change-point detection: both limited sampling right constraint and stochastic sampling right constraint are considered in the Bayesian quickest detection problem. The limited sampling right constraint can be viewed as a special case of the stochastic sampling right constraint with a zero sampling right replenishing rate. The optimal solutions are derived for both sampling right constraints. However, the structure of the optimal solutions are rather complex. For the problem with the limited sampling right constraint, we provide asymptotic upper and lower bounds for the detection delay. For the problem with the stochastic sampling right constraint, we show that the greedy sampling right allocation strategy combined with Shiryaev's detection rule is asymptotically optimal. 3) Quickest change-point detection with unknown post-change parameters: we extend previous results to the quickest detection problem with unknown post-change parameters. Both non-Bayesian and Bayesian setups with stochastic sampling right constraints are considered. For the non-Bayesian problem, we show that the greedy sampling right allocation strategy combined with the M-CUSUM algorithm is asymptotically optimal. For the Bayesian setups, we show that the greedy sampling right allocation strategy combined with the proposed M-Shiryaev algorithm is asymptotically optimal.
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Bodenham, Dean. "Adaptive estimation with change detection for streaming data." Thesis, Imperial College London, 2014. http://hdl.handle.net/10044/1/24484.
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Data streams have become ubiquitous over the last two decades; potentially unending streams of continuously-arriving data occur in fields as diverse as medicine, finance, astronomy and computer networks. As the world changes, so the behaviour of these streams is expected to change. This thesis describes sequential methods for the timely detection of changes in data streams based on an adaptive forgetting factor framework. These change detection methods are first formulated in terms of detecting a change in the mean of a univariate stream, but this is later extended to the multivariate setting, and to detecting a change in the variance. The key issues driving the research in this thesis are that streaming data change detectors must operate sequentially, using a fixed amount of memory and, after encountering a change, must continue to monitor for successive changes. We call this challenging scenario "continuous monitoring" to distinguish it from the traditional setting which generally monitors for only a single changepoint. Additionally, continuous monitoring demands that there be limited dependence on the setting of parameters controlling the performance of the algorithms. One of the main contributions of this thesis is the development of an efficient, fully sequential change detector for the mean of a univariate stream in the continuous monitoring context. It is competitive with algorithms that are the benchmark in the single changepoint setting, yet our change detector only requires a single control parameter, which is easy to set. The multivariate extension provides similarly competitive performance results. These methods are applied to monitoring foreign exchange streams and computer network traffic.
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Ahmed, Kazi Iashtiak. "ENVISAT ASAR for Land Cover Mappingand Change Detection." Thesis, KTH, Geodesi och satellitpositionering, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-199863.
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The principal objective of this research is to investigate the capability of multi-temporal,multi-incidence angle, dual polarization ENVISAT ASAR imagery for extractinglanduse/land cover information in the rural-urban fringe of the Greater Toronto Area (GTA)using different image processing techniques and classification algorithms. An attempt todetermine the temporal change of landuse is also made.The multi-temporal ASAR imagery was first orthorectified using NTDB DEM and satelliteorbital models. Different image processing techniques, such as, Adaptive Speckle Filtering,Texture measures, Principal Component Analysis (PCA) were applied to the ASAR images.Backscatter profiles were generated for selected land cove classes. K Nearest neighbor (kNN)classifier was used to extract eleven land cover classes. Artificial Neural Network (ANN) wasalso tested with some selected combinations of ASAR imagery. The classification schemewas adopted from USGS alnuse/land cover classification scheme. Average accuracy, overallaccuracy and Kappa coefficients were calculated for all classifications.The raw ASAR images gave very poor results in identifying landuse/land cover classes due tothe presence of immense speckle. Enhanced Frost (EF) filtering significantly improved theclassification accuracies. For texture measures, eleven date Mean images produced the bestresult among all single set processed data. Combined Mean and Standard Deviation,combinations of different texture measures, further improved the results. Standard deviationprovided vital auxiliary boundary information to the classification resulting in theimprovement. The best kNN was achieved with combined Mean and Standard Deviation withmulti-incidence angle, dual polarization eleven date ASAR images. ANN further improvedthe classification results of the textured images. As for comparison of classifiers, It was foundthat, with complex combinations (dual polarization, multi-incidence angle), ANN performssignificantly better than kNN. The overall accuracy was 9.6% higher than that of kNN. Theresults were more or less similar in filtered images.Post classification change detection is largely dependent on classification accuracy ofindividual images. Even though, the classification results were somewhat satisfactory, theclassified ASAR image still had a significant amount or omission and commission errors withsome classes. The classification errors contributed a significant amount of noise in changedetection. The change detection procedure, however, was able to identify the areas ofsignificant change, for example, major new roads, new low and high built up areas and golfcourses.In brief, ENVISAT ASAR data was found to have vast potential in extracting land coverinformation. Especially with its all weather capability, ASAR can be used together with highresolutionoptical images for temporal studies of landuse/land cover change due to urbansprawl.
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Schröder, Anna Louise. "Methods for change-point detection with additional interpretability." Thesis, London School of Economics and Political Science (University of London), 2016. http://etheses.lse.ac.uk/3421/.
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The main purpose of this dissertation is to introduce and critically assess some novel statistical methods for change-point detection that help better understand the nature of processes underlying observable time series. First, we advocate the use of change-point detection for local trend estimation in financial return data and propose a new approach developed to capture the oscillatory behaviour of financial returns around piecewise-constant trend functions. Core of the method is a data-adaptive hierarchically-ordered basis of Unbalanced Haar vectors which decomposes the piecewise-constant trend underlying observed daily returns into a binary-tree structure of one-step constant functions. We illustrate how this framework can provide a new perspective for the interpretation of change points in financial returns. Moreover, the approach yields a family of forecasting operators for financial return series which can be adjusted flexibly depending on the forecast horizon or the loss function. Second, we discuss change-point detection under model misspecification, focusing in particular on normally distributed data with changing mean and variance. We argue that ignoring the presence of changes in mean or variance when testing for changes in, respectively, variance or mean, can affect the application of statistical methods negatively. After illustrating the difficulties arising from this kind of model misspecification we propose a new method to address these using sequential testing on intervals with varying length and show in a simulation study how this approach compares to competitors in mixed-change situations. The third contribution of this thesis is a data-adaptive procedure to evaluate EEG data, which can improve the understanding of an epileptic seizure recording. This change-point detection method characterizes the evolution of frequencyspecific energy as measured on the human scalp. It provides new insights to this high dimensional high frequency data and has attractive computational and scalability features. In addition to contrasting our method with existing approaches, we analyse and interpret the method’s output in the application to a seizure data set.
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Meola, Joseph. "A model-based approach to hyperspectral change detection." The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1320847592.
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Lingg, Andrew James. "Statistical Methods for Image Change Detection with Uncertainty." Wright State University / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=wright1357249370.
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Diskin, Yakov. "Volumetric Change Detection Using Uncalibrated 3D Reconstruction Models." University of Dayton / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1429293660.
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Mei, Yajun Lorden Gary. "Asymptotically optimal methods for sequential change-point detection /." Diss., Pasadena, Calif. : California Institute of Technology, 2003. http://resolver.caltech.edu/CaltechETD:etd-05292003-133431.
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Eriksson, Daniel. "Underwater Change Detection by Fusing Multiple Sonar Images." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-263233.
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Underwater change detection can be used for monitoring the seafloor and automatically alert when something changes on it. This could be especially useful in harbors or other critical sea-infrastructure. The idea is to constantly survey the seabed with Autonmous Underwater Vehicles (AUV) equipped with different sonars such as sidescan and multibeam sonars. The data set used, consists of two subsets were one subset is recorded before any man-made objects had been placed on the seabed, the second subsets consists of sidescan data taken after the placement. The goal of this thesis is to develop a general approach to automatically detect changes on the seabed in general, and specifically try to find the man-made objects that were placed on the seabed. In order to achieve this, the thesis will mainly analyze sidescan data. The approach considered in this thesis is based on detecting objects in the sidescan data with a template matching algorithm. Then calculating the position of the objects in a global coordinate system and store all the objects positions from the first subset in a database. After that detect the objects in the second subset and compare their positions to the database. If the position of an object in the second subset does not exists in the database, that object can be considered a new object, and thus a change detection has occurred. Different template matching methods were tested and compared to each other with two test cases. Furthermore, preprocessing of the data were tested and compared as well. In order to calculate the objects position an optimized transformation between the global coordinates and the sonar’s frame of reference were calculated, the transformation is an important part of solving the problem since it will be necessary to do it for any kind approach to change detection. The template matching proved to be difficult to work in all scenarios, where it could only successfully detect all objects in the easier test case. Furthermore, the change detection proved not to be working due to the low success rate of the template matching. However, the change detection will work if the object detection is good enough.Automatisk detektering av förändringar på havsbotten kan användas för att övervaka den och meddela när en förändring sker. Detta är särskilt viktigt och användbart i hamnområden eller andra kritiska infrastrukturer som finns i haven. Idén är att ständigt kartlägga havsbotten med autonoma undervattensfarkoster (AUV från engelskans Autonomous Underwater Vehicle) utrustade med olika typer av sonars som sidescansonar och multibeamsonar. Datan som denna rapport använder sig av består till största del av två datamängder med sidescandata. Den ena datamängden består av sidescandata som samlades in innan några specifika objekt hade placerats på botten. Den andra datamängden samlades in efter att objekten hade placerats på botten. Målet med denna rapport är att utveckla ett generellt tillvägagångssätt för att automatiskt kunna detektera förändringar på havsbotten och med det specifika målet att kunna detektera objekt som har placerats på havsbotten i den andra datamängden. För att uppnå målet, kommer en strategi för att detektera objekt i sidescandatan med hjälp av template matchning att användas. Efter det så räknas objektens position i ett globalt koordinatsystem ut. Detta görs för båda datamängderna för att sedan jämföras med varandra och de objekt som bara existerar i den andra datamängden kan anses vara ett av de placerade objekten. Olika metoder för att räkna ut template matchning testas och jämförs med varandra i två testfall. Fortsättningsvis så undersöks om en förbehandling av datan kan förbättra detektering av objekten. För att kunna räkna objektens position i ett globalt koordinatsystem så behövdes en transform mellan sonars och det globala koordinatsystemet räknas ut. Transformen är viktig att hitta för att den behövs för alla strategier för att lösa problemet med detektering av förändringar på havsbotten. Template matchningen visade sig inte fungera i alla scenarier, där den bara lyckades att hitta alla objekt i ett lättare fall. Fortsättningsvis gick det inte att uppnå målet med att hitta de objekt som hade placerats ut på havsbotten på grund av att template matchningen inte gick att få generell nog att fungera på all data. Trots detta så visade sig att automatisk detektering av förändringar är möjligt om det går att hitta alla objekt i datan.
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Lung-Yut-Fong, Alexandre. "Evaluation of Kernel Methods for Change Detection and Segmentation : Application to Audio Onset Detection." Thesis, Uppsala University, Department of Information Technology, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-98274.
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Finding changes in a signal is a pervasive topic in signal processing. Through the example of audio onset detection to which we apply an online framework, we evaluate the ability of a class of machine learning techniques to solve this task.
The goal of this thesis is to review and evaluate some kernel methods for thetwo-sample problem (one-class Support Vector Machine, Maximum MeanDiscrepancy and Kernel Fisher Discriminant Analysis) on the change detection task, by benchmarking our proposed framework on a set of annotated audio files to which we can compare our results to the ground-truth onset times.
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Taillade, Thibault. "A new strategy for change detection in SAR time-series : application to target detection." Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPAST050.
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La détection de cibles telles que des navires ou des véhicules dans les images SAR (Synthetic Aperture radar) est un défi important pour la surveillance et la sécurité. Dans certains environnements tels que les zones urbaines, portuaires ou les forêts observées à basses fréquences radar, la détection de ces objets devient difficile en raison des propriétés de rétrodiffusion élevées de l'environnement. Pour résoudre ce problème, la détection de changement (CD) entre différentes images SAR permet de supprimer l'effet de l'environnement et ainsi une meilleur détection des cibles. Cependant, dans différents environnements à forte fréquentation, un chevauchement temporel des cibles peut se produire et génère une erreur d'interprétation possible car l'issue de la détection de changement repose sur une différence relative entre des objets de tailles ou de propriétés différentes. C'est un problème critique lorsque le but est de visualiser et d'obtenir le nombre d'objets à une acquisition donnée, dans les zones à fortes fréquentations comme les ports ou les zones urbaines. Idéalement, cette détection de changement devrait se réaliser entre une image constituée seulement de l'environnement et une image contenant les cibles d’intérêts. Grâce à l'accessibilité actuelle aux séries temporelles d'images SAR, nous proposons de générer une scène de référence (Frozen Background Image - FBR) qui n'est constituée que de l'environnement temporellement statique. La détection de changement entre une image SAR et cette image FBR vise donc a obtenir une map de détection des cibles éphémères présentes. Cette stratégie a été mise en œuvre pour la détection des navires en milieu portuaire et dans le contexte de véhicules cachés sous couvert forestierThe detection of targets such as ships or vehicles in SAR (Synthetic Aperture Radar) images is an essential challenge for surveillance and security purpose. In some environments such as urban areas, harbor areas or forest observed at low radar frequencies, detecting these objects becomes difficult due to the high backscattering properties of the surrounding background. To overcome this issue, change detection (CD) between SAR images enables to cancel the background and highlight successfully targets present within the scene. However, in several environments, a temporal overlapping of targets may occur and generates possible misinterpretation because the outcome relies on the relative change between objects of different sizes or properties. This is a critical issue when the purpose is to visualize and obtain the number of targets at a specific day in high attendance areas such as harbors or urban environments. Ideally, this change detection should occur between a target-free image and onewith possible objects of interest. With the current accessibility to SAR time-series, we propose to compute a frozen background reference (FBR) image that will consists only in the temporally static background. Performing change detection from this FBR image and any SAR image aim to highlight the presence of ephemeral targets. This strategy has been implemented for ship detection in harbor environment and in the context of vehicles hidden under foliage
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Schleip, Christoph. "Climate change detection in natural systems by Bayesian methods." kostenfrei, 2009. http://mediatum2.ub.tum.de/node?id=805580.
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Maranganti, Sashikiran. "Vegetation Change Detection in India Using MODIS Satellite Images." Thesis, Linköping University, Department of Computer and Information Science, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-56591.
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Due to man made events and natural causes many regions are currently undergoing rapid and wide ranging changes in land cover globally including developing and developed countries. India is one of them where land use and land cover change are taking place at a rapid pace. Forests are the most valuable natural resources available to the mankind on planet earth. On the one hand, they are the essential source of livelihood for the poor and marginalized sections of the society; on the other hand they provide furniture and other items of desire for the rich. Forest land cover change is an important input for modeling ecological and environmental processes at various scales. Rapid delineation in naturally forested regions is one of the major environmental issues facing the world today. It has been estimated that vegetation change threatens about one sixth of the world's population and one quarter of global terrestrial land. Vegetation cover plays a key role in terrestrial biophysical process and is related to a number of ways to the dynamics of global climate. Monitoring seasonal changes in vegetation activity and crop phenology over wide areas is essential for many applications, such as estimation of net primary production, deciding time boundary conditions for crop yield modeling and supporting decisions about water supply. Vegetations are the major part of land cover and their changes have an important influence on the energy and mass biochemical cycles and are also a key indicator of regional ecological environment change. Urbanization, demand of land for agriculture and demand of timbers for industrial purposes are the main reasons of manmade natural forest destruction. Though we are planting trees through reforestation and afforestation programs but these new forests never can be the representative of natural forest. In order to understand and manage environment at large variety of temporal and spatial scales, up-to-date and reliable information is required all the time. Remote Sensing is a valuable data source which can provide us land-use/land-cover change information on a continuous basis with very high accuracy. Remotely sensed data like aerial photographs and satellite images are the only option that allows detecting land cover changes on a large scale. Satellite images have the potential of offering the most accurate and latest information compared to statistical, topographic or land use maps. In this study an attempt has been made in analyzing vegetation change detection that took place between 2000 and 2005 using Terra MODIS 32 day 500m time series data on a monthly basis. With the launch of National Aeronautics and Space Administration (NASA) onboard aqua and terra platform, a new generation of satellite sensor data is now available. Normalized Difference Vegetation Index method has been employed for accurate classification of images and has proved to be successful.
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Han, Sung Won. "Efficient change detection methods for bio and healthcare surveillance." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/34828.
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For the last several decades, sequential change point problems have been studied in both the theoretical area (sequential analysis) and the application area (industrial SPC). In the conventional application, the baseline process is assumed to be stationary, and the shift pattern is a step function that is sustained after the shift. However, in biosurveillance, the underlying assumptions of problems are more complicated. This thesis investigates several issues in biosurveillance such as non-homogeneous populations, spatiotemporal surveillance methods, and correlated structures in regional data.
The first part of the thesis discusses popular surveillance methods in sequential change point problems and off-line problems based on count data. For sequential change point problems, the CUSUM and the EWMA have been used in healthcare and public health surveillance to detect increases in the rates of diseases or symptoms. On the other hand, for off-line problems, scan statistics are widely used. In this chapter, we link the method for off-line problems to those for sequential change point problems. We investigate three methods--the CUSUM, the EWMA, and scan statistics--and compare them by conditional expected delay (CED).
The second part of the thesis pertains to the on-line monitoring problem of detecting a change in the mean of Poisson count data with a non-homogeneous population size. The most common detection schemes are based on generalized likelihood ratio statistics, known as an optimal method under Lodern's criteria. We propose alternative detection schemes based on the weighted likelihood ratios and the adaptive threshold method, which perform better than generalized likelihood ratio statistics in an increasing population. The properties of these three detection schemes are investigated by both a theoretical approach and numerical simulation.
The third part of the thesis investigates spatiotemporal surveillance based on likelihood ratios. This chapter proposes a general framework for spatiotemporal surveillance based on likelihood ratio statistics over time windows. We show that the CUSUM and other popular likelihood ratio statistics are the special cases under such a general framework. We compare the efficiency of these surveillance methods in spatiotemporal cases for detecting clusters of incidence using both
Monte Carlo simulations and a real example.
The fourth part proposes multivariate surveillance methods based on likelihood ratio tests in the presence of spatial correlations. By taking advantage of spatial correlations, the proposed methods can perform better than existing surveillance methods by providing the faster and more accurate detection. We illustrate the application of these methods with a breast cancer case in New Hampshire when observations are spatially correlated.
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Yardley, Victoria Anne. "Magnetic detection of microstructure change in power plant steels." Thesis, University of Cambridge, 2003. https://www.repository.cam.ac.uk/handle/1810/221825.
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Power plant components are expected to withstand service at high temperature and pressure for thirty years or more. One of the main failure mechanisms under these conditions is creep. The steel compositions and heat treatments for this application are chosen to confer microstructural stability and creep resistance. Nevertheless, gradual microstructural changes, which eventually degrade the creep properties, occur during the long service life. Conservative design lives are used in power plant, and it is often found that components can be used safely beyond the original design life. However, to benefit from this requires reliable monitoring methods. One such technique involves relating the microstructural state to measurable magnetic properties. Magnetic domain walls interact energetically with microstructural features such as grain boundaries, carbides and dislocations, and are 'pinned' in place at these sites until a sufficiently large field is applied to free them. When this occurs, the sudden change in magnetisation as the walls move can be detected as a voltage signal (Barkhausen noise). Previous work has suggested that grain boundaries and carbide particles in power plant steels act as pinning sites with characteristic strengths and strength distributions. In this study, the concept of pinning site strength distributions was used to develop a model for the variation of the Barkhausen noise signal with applied field. This gave a good fit to published data. The modelling parameters characterising pinning site strengths showed good correlations with grain and carbide particle sizes. New Barkhausen noise data were obtained from tempered power plant steel samples for further model testing. The Orientation Imaging Microscopy (OIM) technique was used to investigate the grain orientations and grain boundary properties in the steel and their possible role in Barkhausen noise behaviour. The model again fitted the data well, and a clear relationship could be seen between the pinning strength parameter and the severity of tempering (as expressed by the Larson-Miller tempering parameter) to which the steel was subjected. The experimental results suggest that the Barkhausen noise characteristics of the steels investigated depend strongly on the strain at grain boundaries. As tempering progresses and the grain boundary dislocation density falls, the pinning strength of the grain boundaries also decreases. A clear difference in Barkhausen noise response could be seen between a 2¼Cr1Mo traditional power-plant steel and an 11Cr1Mo steel designed for superior heat resistance. A study of an oxide dispersion strengthened ferrous alloy, in which the microstructure undergoes dramatic coarsening on recrystallisation, was used to investigate further the effects of grain boundaries and particles on Barkhausen noise. The findings from these experiments supported the conclusion that grain boundary strain reduction gave large changes in the observed Barkhausen noise.
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Deer, Peter. "Change detection in remote sensing using supervised fuzzy classification." Title page, abstract and contents only, 1999. http://hdl.handle.net/2440/19340.
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Title page, contents and abstract only. The complete thesis in print form is available from the University Library.Thesis (Ph.D.)--University of Adelaide, Dept. of Geography and Dept. of Computer Science, 1999
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Yang, Guo-Hua, and 楊國華. "Topographical Change Detection." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/56938056995259999140.
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Lan, Yuang-Tzong, and 藍元宗. "Lane Change Detection Based on." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/86031609660748377644.
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碩士國立中正大學
電機工程研究所
89
As the number of vehicles increased rapidly, we need to manage the traffic system more efficiently. One of the important issues is to reduce the occurring rate of accidents. In some road sections it is illegal to do lane change according to traffic regulations. The reason is that doing lane change in these road sections has potential danger to cause accidents. Therefore we proposed a method to detect vehicles that violate the rule based on image processing technique. In this paper the behavior of lane change will be detected from a macroscopic point of view. It means that we don’t detect an individual vehicle and track it to see if it is doing lane change. Instead we analyze the setting region and transform the information obtained from images to a 2D spatial-temporal diagram. We then extract lane change events from the spatial-temporal diagram. Via experiments the system is proven to be able to detect lane change event efficiently.
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Kurt, Mehmet Necip. "Data-Driven Quickest Change Detection." Thesis, 2020. https://doi.org/10.7916/d8-yz99-3e67.
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The quickest change detection (QCD) problem is to detect abrupt changes in a sensing environment as quickly as possible in real time while limiting the risk of false alarm. Statistical inference about the monitored stochastic process is performed through observations acquired sequentially over time. After each observation, QCD algorithm either stops and declares a change or continues to have a further observation in the next time interval. There is an inherent tradeoff between speed and accuracy in the decision making process. The design goal is to optimally balance the average detection delay and the false alarm rate to have a timely and accurate response to abrupt changes.
The objective of this thesis is to investigate effective and scalable QCD approaches for real-world data streams. The classical QCD framework is model-based, that is, statistical data model is assumed to be known for both the pre- and post-change cases. However, real-world data often exhibit significant challenges for data modeling such as high dimensionality, complex multivariate nature, lack of parametric models, unknown post-change (e.g., attack or anomaly) patterns, and complex temporal correlation. Further, in some cases, data is privacy-sensitive and distributed over a system, and it is not fully available to QCD algorithm. This thesis addresses these challenges and proposes novel data-driven QCD approaches that are robust to data model mismatch and hence widely applicable to a variety of practical settings.
In Chapter 2, online cyber-attack detection in the smart power grid is formulated as a partially observable Markov decision process (POMDP) problem based on the QCD framework. A universal robust online cyber-attack detection algorithm is proposed using the model-free reinforcement learning (RL) for POMDPs. In Chapter 3, online anomaly detection for big data streams is studied where the nominal (i.e., pre-change) and anomalous (i.e., post-change) high-dimensional statistical data models are unknown. A data-driven solution approach is proposed, where firstly a set of useful univariate summary statistics is computed from a nominal dataset in an offline phase and next, online summary statistics are evaluated for a persistent deviation from the nominal statistics.
In Chapter 4, a generic data-driven QCD procedure is proposed, called DeepQCD, that learns the change detection rule directly from the observed raw data via deep recurrent neural networks. With sufficient amount of training data including both pre- and post-change samples, DeepQCD can effectively learn the change detection rule for all complex, high-dimensional, and temporally correlated data streams. Finally, in Chapter 5, online privacy-preserving anomaly detection is studied in a setting where the data is distributed over a network and locally sensitive to each node, and its statistical model is unknown. A data-driven differentially private distributed detection scheme is proposed, which infers network-wide anomalies based on the perturbed and encrypted statistics received from nodes. Furthermore, analytical privacy-security tradeoff in the network-wide anomaly detection problem is investigated.
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Yang, E., and 楊傳億. "Culture、Relation and Change detection." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/19250683518027393295.
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碩士國立中正大學
心理學所
98
Cultural experience may influence the way we think. Nisbett and his colleagues suggested that Easterners view the world holistically, attend to the entire field, and take into account the context; whereas Westerners view the world analytically and pay attention to the attributes of salient objects. However, studies of Nisbett and his colleagues may be confounded by the extent to which the relations were taken into account, that is, Easterners may attend to the relations more than Westerners. The present study examined this proposal by applying the change blindness paradigm. In both experiment 1 and experiment 2, Taiwanese participants compared to American participants were more sensitive to the manipulation of interpersonal relations, although this finding was restricted to focal objects. However, no difference was found for cultural groups when the nonsocial stimuli were adopted in experiment 3. These results suggested that Easterners compared to Westerners were more sensitive to interpersonal relations and directed more attention to interpersonal relations. However, this cultural difference was restricted to social stimuli only. No evidence was found in supporting the claim that cultural experiences may shape fundamental cognitive processes.
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Hsieh, Chia-Chin, and 謝嘉進. "Subpixel Change Detection and Identification Based on SpectralUnmixing: An Application to Change Detection of Landslide." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/65839349056953372664.
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碩士國立成功大學
資訊工程學系碩博士班
94
Most of change detection algorithms for multi-temporal images are performed in unit of pixels. Due to the limit of spatial resolution, a pixel is, in many cases, a mixed pixel that consists of more than one ground cover types. We reviewed several spectral unmixing techniques such as independent component analysis (ICA), non-negative matrix factorization (NMF), unsupervised fully constrained least squares linear unmixing (UFCLSLU) and vertex component analysis (VCA). We employed the spectral unmixing techniques to explore subpixel information and to detect subpixel-scale changes. Furthermore, we demonstrated an application of subpixel change detection to detection of landslide expansions. The abundance feature extracted from multispectral images by spectral unmixing was incorporated with the slope feature into the process of landslide change identification based on the post-classification comparison procedure. Our result shows that the subpixel change detection method can provide more detailed information about landslide changes than pixel-based change detection algorithms.
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Kit, Dmitry Mark. "Change detection models for mobile cameras." Thesis, 2012. http://hdl.handle.net/2152/ETD-UT-2012-05-5127.
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Change detection is an ability that allows intelligent agents to react to unexpected situations. This mechanism is fundamental in providing more autonomy to robots. It has been used in many different fields including quality control and network intrusion. In the visual domain, however, most research has been confined to stationary cameras and only recently have researchers started to shift to mobile cameras.
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We propose a general framework for building internal spatial models of the visual experiences. These models are used to retrieve expectations about visual inputs which can be compared to the actual observation in order to identify the presence of changes. Our framework leverages the tolerance to small view changes of optic flow and color histogram representations and a self-organizing map to build a compact memory of camera observations. The effectiveness of the approach is demonstrated in a walking simulation, where spatial information and color histograms are combined to detect changes in a room. The location signal allows the algorithm to query the self-organizing map for the expected color histogram and compare it to the current input. Any deviations can be considered changes and are then localized on the input image.
Furthermore, we show how detecting a vehicle entering or leaving the camera's lane can be reduced to a change detection problem. This simplifies the problem by removing the need to track or even know about other vehicles. Matching Pursuit is used to learn a compact dictionary to describe the observed experiences. Using this approach, changes are detected when the learned dictionary is unable to reconstruct the current input.
The human experiments presented in this dissertation support the idea that humans build statistical models that evolve with experience. We provide evidence that not only does this experience improve people's behavior in 3D environments, but also enables them to detect chromatic changes.
Mobile cameras are now part of our everyday lives, ranging from built-in laptop cameras to cell phone cameras. The vision of this research is to enable these devices with change detection mechanisms to solve a large class of problems. Beyond presenting a foundation that effectively detects changes in environments, we also show that the algorithms employed are computationally inexpensive. The practicality of this approach is demonstrated by a partial implementation of the algorithm on commodity hardware such as Android mobile devices.text
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Li, Ming-ru, and 李明儒. "Shot Change Detection By Fractal Signature." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/58116866433996412970.
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碩士國立中山大學
資訊工程學系研究所
94
The developing of multimedia to make the video data to increase very quickly.So how to acquire the data that we want in a short time is a more important topic. Shot change detection is the first step for latter operation like classification and annotations. There are two type of shot change, one is abrupt shot change and the other one is gradual transition. Dissolve is the one of gradual transition that often seen but hard to detection, so in the paper would to propose a robust method to solve this problem. In this paper we use fractal orthonormal basis for our feature to compare frames in the video to the first frame of video, and use the quantification between those frames to draw a graph. By analyzing the graph and the characteristic of dissolve in the graph we can locate the approximately the start frame and the end frame of the dissolve. But by the action of video camera or motion of object in frame we may obtained the inaccurate start frame or end frame of the dissolve. So we need to refine the more accurate start and end frame of the dissolve, and we will explain about this in Chapter 3-4