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Thompson, J. R. "Applications of pattern recognition in medicine." Thesis, Open University, 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.377939.
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Robinson, Daniel D. "Applications of pattern recognition and pattern analysis to molecule design." Thesis, University of Oxford, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.343465.
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PAOLANTI, MARINA. "Pattern Recognition for challenging Computer Vision Applications." Doctoral thesis, Università Politecnica delle Marche, 2018. http://hdl.handle.net/11566/252904.
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La Pattern Recognition è lo studio di come le macchine osservano l'ambiente, imparano a distinguere i pattern di interesse dal loro background e prendono decisioni valide e ragionevoli sulle categorie di modelli. Oggi l'applicazione degli algoritmi e delle tecniche di Pattern Recognition è trasversale. Con i recenti progressi nella computer vision, abbiamo la capacità di estrarre dati multimediali per ottenere informazioni preziose su ciò che sta accadendo nel mondo. Partendo da questa premessa, questa tesi affronta il tema dello sviluppo di sistemi di Pattern Recognition per applicazioni reali come la biologia, il retail, la sorveglianza, social media intelligence e i beni culturali. L'obiettivo principale è sviluppare applicazioni di computer vision in cui la Pattern Recognition è il nucleo centrale della loro
progettazione, a partire dai metodi generali, che possono essere sfruttati in più campi di ricerca, per poi passare a metodi e tecniche che affrontano problemi specifici. Di fronte a molti tipi di dati, come immagini, dati biologici e traiettorie, una difficoltà fondamentale è trovare rappresentazioni vettoriali rilevanti. Per la progettazione del sistema di riconoscimento dei modelli vengono eseguiti i seguenti passaggi: raccolta dati, estrazione delle caratteristiche, approccio di apprendimento personalizzato e analisi e valutazione comparativa. Per una valutazione completa delle prestazioni, è di grande importanza collezionare un dataset specifico perché i metodi di progettazione che sono adattati a un
problema non funzionano correttamente su altri tipi di problemi. I metodi su misura, adottati per lo sviluppo delle applicazioni proposte, hanno dimostrato di essere in grado di estrarre caratteristiche statistiche complesse e di imparare in modo efficiente le loro rappresentazioni, permettendogli di generalizzare bene attraverso una vasta gamma di compiti di visione computerizzata.Pattern Recognition is the study of how machines can observe the environment, learn to distinguish patterns of interest from their background, and make sound and reasonable decisions about the patterns categories. Nowadays, the application of Pattern Recognition algorithms and techniques is ubiquitous and transversal. With the recent advances in computer vision, we now have the ability to mine such massive visual data to obtain valuable insight about what is happening in the world. The availability of affordable and high resolution sensors (e.g., RGB-D cameras, microphones and scanners) and data sharing have resulted in huge repositories of digitized documents (text, speech, image and video). Starting from such a premise, this thesis addresses the topic of developing next generation Pattern Recognition systems for real applications such as Biology, Retail, Surveillance, Social Media Intelligence and Digital Cultural Heritage. The main goal is to develop computer vision applications in which Pattern Recognition is the key core in their design, starting from general methods, that can be exploited in more fields, and then passing to methods and techniques addressing specific problems. The privileged focus is on up-to-date applications of Pattern Recognition techniques to real-world problems, and on interdisciplinary research, experimental and/or theoretical studies yielding new insights that advance Pattern Recognition methods. The final ambition is to spur new research lines, especially within interdisciplinary research scenarios. Faced with many types of data, such as images, biological data and trajectories, a key difficulty was to nd relevant vectorial representations. While this problem had been often handled in an ad-hoc way by domain experts, it has proved useful to learn these representations directly from data, and Machine Learning algorithms, statistical methods and Deep Learning techniques have been particularly successful. The representations are then based on compositions of simple parameterized processing units, the depth coming from the large number of such compositions. It was desirable to develop new, efficient data representation or feature learning/indexing techniques, which can achieve promising performance in the related tasks. The overarching goal of this work consists of presenting a pipeline to select the model that best explains the given observations; nevertheless, it does not prioritize in memory and time complexity when matching models to observations. For the Pattern Recognition system design, the following steps are performed: data collection, features extraction, tailored learning approach and comparative analysis and assessment. The proposed applications open up a wealth of novel and important opportunities for the machine vision community. The newly dataset collected as well as the complex areas taken into exam, make the research challenging. In fact, it is crucial to evaluate the performance of state of the art methods to demonstrate their strength and weakness and help identify future research for designing more robust algorithms. For comprehensive performance evaluation, it is of great importance developing a library and benchmark to gauge the state of the art because the methods design that are tuned to a specic problem do not work properly on other problems. Furthermore, the dataset selection is needed from different application domains in order to offer the user the opportunity to prove the broad validity of methods. Intensive attention has been drawn to the exploration of tailored learning models and algorithms, and their extension to more application areas. The tailored methods, adopted for the development of the proposed applications, have shown to be capable of extracting complex statistical features and efficiently learning their representations, allowing it to generalize well across a wide variety of computer vision tasks, including image classication, text recognition and so on.
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Hayes, William S. "Pattern recognition and signal detection in gene finding." Diss., Georgia Institute of Technology, 1998. http://hdl.handle.net/1853/25420.
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Prendergast, David Jeremy. "Applications of statistical pattern recognition in medical imaging." Thesis, University of Manchester, 1993. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.629772.
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Evans, Fiona H. "Syntactic models with applications in image analysis /." [Perth, W.A.] : [University of W.A.], 2006. http://theses.library.uwa.edu.au/adt-WU2007.0001.
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Yan, Wing-fai. "Eye movement measurement for clinical applications using pattern recognition /." [Hong Kong : University of Hong Kong], 1988. http://sunzi.lib.hku.hk/hkuto/record.jsp?B12434024.
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Ma, Chengyuan. "A detection-based pattern recognition framework and its applications." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/33889.
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The objective of this dissertation is to present a detection-based pattern recognition framework and demonstrate its applications in automatic speech recognition and broadcast news video story segmentation.
Inspired by the studies of modern cognitive psychology and real-world pattern recognition systems, a detection-based pattern recognition framework is proposed to provide an alternative solution for some complicated pattern recognition problems. The primitive features are first detected and the task-specific knowledge hierarchy is constructed level by level; then a variety of heterogeneous information sources are combined together and the high-level context is incorporated as additional information at certain stages.
A detection-based framework is a â divide-and-conquerâ design paradigm for pattern recognition problems, which will decompose a conceptually difficult problem into many elementary sub-problems that can be handled directly and reliably. Some information fusion strategies will be employed to integrate the evidence from a lower level to form the evidence at a higher level. Such a fusion procedure continues until reaching the top level. Generally, a detection-based framework has many advantages: (1) more flexibility in both detector design and fusion strategies, as these two parts
can be optimized separately; (2) parallel and distributed computational components in primitive feature detection. In such a component-based framework, any primitive component can be replaced by a new one while other components remain unchanged; (3) incremental information integration; (4) high level context information as additional information sources, which can be combined with bottom-up processing at any stage.
This dissertation presents the basic principles, criteria, and techniques for detector design and hypothesis verification based on the statistical detection and decision theory. In addition, evidence fusion strategies were investigated in this dissertation. Several novel detection algorithms and evidence fusion methods were proposed and their effectiveness was justified in automatic speech recognition and broadcast news video segmentation system. We believe such a detection-based framework can be employed
in more applications in the future.
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甄榮輝 and Wing-fai Yan. "Eye movement measurement for clinical applications using pattern recognition." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1988. http://hub.hku.hk/bib/B31209026.
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Lopez-Bonilla, Roman Ernesto. "Object recognition in three-dimensions for robotic applications." Thesis, University of Bradford, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.305752.
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Chen, Guangyi. "Applications of wavelet transforms in pattern recognition and de-noising." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape7/PQDD_0006/MQ43552.pdf.
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Xu, Yun. "Chemometrics pattern recognition with applications to genetic and metabolomics data." Thesis, University of Bristol, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.435733.
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Otte, Sebastian [Verfasser]. "Recurrent Neural Networks for Sequential Pattern Recognition Applications / Sebastian Otte." München : Verlag Dr. Hut, 2017. http://d-nb.info/1149579382/34.
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Wu, Jianfei. "Vector-Item Pattern Mining Algorithms and their Applications." Diss., North Dakota State University, 2011. https://hdl.handle.net/10365/28841.
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Advances in storage technology have long been driving the need for new data mining techniques. Not only are typical data sets becoming larger, but the diversity of available attributes is increasing in many problem domains. In biological applications for example, a single protein may have associated sequence-, text-, graph-, continuous and item data. Correspondingly, there is growing need for techniques to find patterns in such complex data. Many techniques exist for mapping specific types of data to vector space representations, such as the bag-of-words model for text [58] or embedding in vector spaces of graphs [94, 91]. However, there are few techniques that recognize the resulting vector space representations as units that may be combined and further processed. This research aims to mine important vector-item patterns hidden across multiple and diverse data sources. We consider sets of related continuous attributes as vector data and search for patterns that relate a vector attribute to one or more items. The presence of an item set defines a subset of vectors that may or may not show unexpected density fluctuations. Two types of vector-item pattern mining algorithms have been developed, namely histogram-based vector-item pattern mining algorithms and point distribution vector-item pattern mining algorithms. In histogram-based vector-item pattern mining algorithms, a vector-item pattern is significant or important if its density histogram significantly differs from what is expected for a random subset of transactions, using ?? goodness-of-fit test or effect size analysis. For point distribution vector-item pattern mining algorithms, a vector-item pattern is significant if its probability density function (PDF) has a big KullbackLeibler divergence from random subsamples. We have applied the vector-item pattern mining algorithms to several application areas, and by comparing with other state-of-art algorithms we justify the effectiveness and efficiency of the algorithms.
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Agaiby, Hany. "Word boundary detection for engineering applications." Thesis, University of the West of Scotland, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.265933.
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Hui, Colin Chiu Wing. "VLSI architectures for digital television applications." Thesis, Queen's University Belfast, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.387928.
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Via, Cinzia Da. "Semiconductor pixel detectors for imaging applications." Thesis, University of Glasgow, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.362937.
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Stacey, Duncan T. B. "Advances and applications in broadband imaging microspectroscopy." Thesis, University of Liverpool, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.386823.
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Ma, Liying. "Constructive neural networks with applications to image compression and pattern recognition." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp05/NQ63990.pdf.
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Rohen, V. E. "Applications of statistical pattern recognition techniques to the analysis of ballistocardiograms." Thesis, University of Cambridge, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.235284.
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This dissertation describes a new approach to the analysis of Ballisto-cardiograms using Statistical Pattern Recognition technique, as well as the design and development of a new ballistocardiograph and its associated software. Ballistocardiograms are the result of forces exerted on the body, caused by the ejection of blood from the heart and the passage of the blood through the arterial system. The apparatus used in this study for the collection and display of the ballistocardiograms consisted of a specially designed stool with highly sensitive piezoelectric elements, which converted the forces acting on the stool into electric signals, connected to a specially built interface which converted the analogue signal into digital data. These were in turn analysed using a BBC model B microcomputer running special software which was written as part of this work. The methods of analysis developed here are based on Statistical Pattern Recognition and consist of units dealing with the preprocessing of the data, extraction of optimal features, and with their classification. By their nature, the lengths of the ballistocardiograms vary not only from person to person but there are also differences between the lengths of individual beats in the same subject. This presents a major problem for successful analysis. A novel method for non-linear standardisation of the ballistocardiogram length was developed and used in this study. This method allows the comparison of ballistocardiograms of different lengths, by projecting them into a waveform of uniform length, whilst maintaining all the information contained in the shape of the original signal. The projection is based on local cross-correlation of a template ballistocardiogram with a subset of the ballistocardiograms to be analysed. This results in a set of standard length records which in turn are used to determine the transformation. A feature extraction method based on double eigenanalysis was used to reduce the dimensionality of the data and for the extraction of features which discriminate best between the different classes analysed. Four classes of subjects were used in this study. A normal group which consisted of generally healthy and physically fit people, whose ballistocardiograms were also used to develop the new method for length adjustments; a group of subjects with mild hypertension; a group of patients with coronary artery stenosis, who were undergoing treatment at the Papworth Hospital; and a group consisting of subjects with clinical history of recent myocardial infarction. It was found that after standardisation of the length of the ballistocardiograms, and after extraction of those features which contain most of the discriminant information, the Nearest- Neighbour rule discriminated well between the group of normal subjects and the three remaining groups. The groups of subjects with mild hypertension and with coronary artery stenosis proved more difficult to separate. This can possibly be explained by the similarities in the characteristics of these two groups as far as ballistocardiograms are concerned. It was also found that the parts of the wave that have most of the discriminatory information are those corresponding to the ejection phase, for all the groups in general, and those corresponding to the last peaks of the ballistocardiograms (post ejection phase), for the group with recent myocardial infarction. Ballistocardiography is shown in this work to be a good non- invasive method for the study of the general performance of the heart. The methods described here for discrimination between groups and classification of different ballistocardiograms, by means of the analysis of their shape alone, have also proved very powerful. In particular, the new length standardisation method allows a more accurate monitoring of the heart function, than could be achieved so far. The techniques developed in this researh may be used for the prediction of various heart diseases in their early stages. This, together with the portability of the apparatus developed in this research, could turn the new ballistocardiograph into a standard clinical device.
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Chatterjee, Shiladitya. "Applications of Pattern Recognition Entropy (PRE) and Informatics to Data Analysis." BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/8826.
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The primary focus of my work is the application of informatics methods to the fields of materials science and analytical chemistry. The statistical analysis of data has become increasingly important in understanding the properties of materials and analytes. Statistical methods like principal component analysis (PCA) and multivariate curve resolution (MCR) are widely used for analysis in chemistry and other fields given their ability to categorize spectra in an unsupervised way. PCA is relatively easy to apply and has appealing mathematical properties. However, the results can be challenging to interpret, even for experienced users. In contrast, MCR results can be more interpretable, because the factors resemble real spectra and do not have negative scores or loadings. Nevertheless, the useful orthogonality properties of the scores and loadings in PCA are sacrificed in doing so. Other statistical analysis methods like cluster analysis and partial least squares regression (PLS-R) present their own challenges. Pattern recognition entropy (PRE) is a novel application of Shannon’s information theory for understanding the underlying complexity in spectra. Unlike PCA and MCR, PRE is a summary statistic that adopts the mathematical quantification of information and applies it for chemometric analysis. PRE values reflect the shape and complexity of spectra. Chapter 1 contains a description of the analytical methods/instruments that provided the data I analyzed by PRE and other informatics tools, including (i) X-ray photoelectron spectroscopy (XPS) and time-of-flight secondary ion mass spectrometry (ToF-SIMS) and (ii) liquid chromatography-mass spectrometry (LC-MS) and capillary electrophoresis (CE), (iii) a discussion of some of the commonly used statistical analysis tools like PCA, MCR, cluster analysis and PLS-R, and (iv) a description of PRE. Chapter 2 describes in much greater detail the theory associated with the statistical tools I used and PRE. Chapter 3 describes the PRE and informatics analysis of depth profiles through thin films by XPS and ToF-SIMS. Chapter 4 introduces the concept of the ‘reordered spectrum’ as an intuitive, visual representation of spectra to address the abstraction associated with PRE result. Total ion current chromatograms (TICCs) generated using LC-MS are often extremely complex and ‘noisy’. Chapter 5 describes the application of PRE as a variable reduction method for producing higher quality TICCs. Chapter 6 discusses the limitations associated with the application of PRE to TICCs and presents a new method using cross-correlation (CC) in conjunction with a PRE analysis. Chapter 7 discusses a new methodology that uses CE and PRE to detect autologous blood doping (ABD). Chapter 8 presents my conclusions of this present work and discusses the scope of future work on PRE. The thesis also contains several appendices. Appendix 1 introduces polyallylamine (PAAm) as a simple, easy-to-apply adhesion promoter for the widely used photoresist SU-8. Appendices 2, 3 and 4 contain articles I wrote that relate to trends in modern XPS instrumentation and 5-8 contain supplemental information relating to Chapters 3, 4, 5, and 7 respectively.
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Ma, Jinhua. "Dependency modeling for information fusion with applications in visual recognition." HKBU Institutional Repository, 2013. https://repository.hkbu.edu.hk/etd_ra/1522.
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Bulas, Cruz Jose Afonso Moreno. "Image processing applications using a transputer-based system." Thesis, University of Bristol, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.294371.
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Canuto, Anne Magaly de Paula. "Combining neural networks and fuzzy logic for applications in character recognition." Thesis, University of Kent, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.344107.
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Murnion, Shane D. "Neural and genetic algorithm applications in GIS and remote sensing." Thesis, Queen's University Belfast, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.337024.
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Thomson, Andrew Richard. "A CAM-based processor array for real-time image applications." Thesis, University of Bristol, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.386072.
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Balakrishnan, Sreeram Viswanath. "Solving combinatorial optimization problems using neural networks with applications in speech recognition." Thesis, University of Cambridge, 1992. https://www.repository.cam.ac.uk/handle/1810/283679.
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Kundakcioglu, O. Erhun. "Combinatorial and nonlinear optimization techniques in pattern recognition with applications in healthcare." [Gainesville, Fla.] : University of Florida, 2009. http://purl.fcla.edu/fcla/etd/UFE0024768.
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Kwok, Kwok Sai. "Algorithms for image segmentation and their applications to video signal processing." Thesis, Imperial College London, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.244298.
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Travis, Clive Hathaway. "The inverse problem and applications to optical and eddy current imaging." Thesis, University of Surrey, 1989. http://epubs.surrey.ac.uk/804869/.
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Tivive, Fok Hing Chi. "A new class of convolutional neural networks based on shunting inhibition with applications to visual pattern recognition." Access electronically, 2006. http://www.library.uow.edu.au/adt-NWU/public/adt-NWU20061025.164437/index.html.
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Boussakta, Said. "Algorithms and development of the number theoretic and related fast transforms with applications." Thesis, University of Newcastle Upon Tyne, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.293568.
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Langford, Mitchel. "Some applications of digital image processing for automation in palynology." Thesis, University of Hull, 1988. http://hydra.hull.ac.uk/resources/hull:3098.
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Koubaroulis, D. A. "The multimodal neighbourhood signature for modelling object colour appearance and applications in computer vision." Thesis, University of Surrey, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.365142.
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Giovanini, Renato de Macedo [UNESP]. "SSVEP-EEG signal pattern recognition system for real-time brain-computer interfaces applications." Universidade Estadual Paulista (UNESP), 2017. http://hdl.handle.net/11449/151710.
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
There are, nowadays, about 110 million people in the world who live with some type of severe motor disability. Specifically in Brazil, about 2.2% of the population are estimated to live with a condition of difficult locomotion. Aiming to help these people, a vast variety of devices, techniques and services are currently being developed. Among those, one of the most complex and challenging techniques is the study and development of Brain-Computer Interfaces (BCIs). BCIs are systems that allow the user to communicate with the external world controlling devices without the use of muscles or peripheral nerves, using only his decoded brain activity. To achieve this, there is a need to develop robust pattern recognition systems, that must be able to detect the user’s intention through electroencephalography (EEG) signals and activate the corresponding output with reliable accuracy and within the shortest possible processing time. In this work, different EEG signal processing techniques were studied, and it is presented the development of a EEG under visual stimulation (Steady-State Visual Evoked Potentials - SSVEP) pattern recognition system. Using only Open Source tools and Python programming language, modules to manage datasets, reduce noise, extract features and perform classification of EEG signals were developed, and a comparative study of different techniques was performed, using filter banks and Discrete Wavelet Transforms (DWT) as feature extraction approaches, and the classifiers K-Nearest Neighbors, Multilayer Perceptron and Random Forests. Using DWT approach with Random Forest and Multilayer Perceptron classifiers, high accuracy rates up to 92 % were achieved in deeper decomposition levels. Then, the small-size microcomputer Raspberry Pi was used to perform time processing evaluation, obtaining short processing times for every classifiers. This work is a preliminary study of BCIs at the Laboratório de Instrumentação e Engenharia Biomédica, and, in the future, the system here presented may be part of a complete SSVEP-BCI system.
Existem, atualmente, cerca de 110 milhões de pessoas no mundo que vivem com algum tipo de deficiência motora severa. Especificamente no Brasil, é estimado que cerca de 2.2% da população conviva com alguma condição que dificulte a locomoção. Com o intuito de auxiliar tais pessoas, uma grande variedade de dispositivos, técnicas e serviços são atualmente desenvolvidos. Dentre elas, uma das técnicas mais complexas e desafiadoras é o estudo e o desenvolvimento de Interfaces Cérebro-Computador (ICMs). As ICMs são sistemas que permitem ao usuário comunicar-se com o mundo externo, controlando dispositivos sem o uso de músculos ou nervos periféricos, utilizando apenas sua atividade cerebral decodificada. Para alcançar isso, existe a necessidade de desenvolvimento de sistemas robustos de reconhecimento de padrões, que devem ser capazes de detectar as intenções do usuáro através dos sinais de eletroencefalografia (EEG) e ativar a saída correspondente com acurácia confiável e o menor tempo de processamento possível. Nesse trabalho foi realizado um estudo de diferentes técnicas de processamento de sinais de EEG, e o desenvolvimento de um sistema de reconhecimento de padrões de sinais de EEG sob estimulação visual (Potenciais Evocados Visuais de Regime Permanente - PEVRP). Utilizando apenas técnicas de código aberto e a linguagem Python de programação, foram desenvolvidos módulos para realizar o gerenciamento de datasets, redução de ruído, extração de características e classificação de sinais de EEG, e um estudo comparativo de diferentes técnicas foi realizado, utilizando-se bancos de filtros e a Transformada Wavelet Discreta (DWT) como abordagens de extração de características, e os classificadores K-Nearest Neighbors, Perceptron Multicamadas e Random Forests. Utilizando-se a DWT juntamente com Random Forests e Perceptron Multicamadas, altas taxas de acurácia de até 92 % foram obtidas nos níveis mais profundos de decomposição. Então, o computador Raspberry Pi, de pequenas dimensões, foi utilizado para realizar a avaliação do tempo de processamento, obtendo um baixo tempo de processamento para todos os classificadores. Este trabalho é um estudo preliminar em ICMs no Laboratório de Instrumentação e Engenharia Biomédica e, no futuro, pode ser parte de um sistema ICM completo.
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Giovanini, Renato de Macedo. "SSVEP-EEG signal pattern recognition system for real-time brain-computer interfaces applications /." Ilha Solteira, 2017. http://hdl.handle.net/11449/151710.
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Orientador: Aparecido Augusto de CarvalhoResumo: There are, nowadays, about 110 million people in the world who live with some type of severe motor disability. Specifically in Brazil, about 2.2% of the population are estimated to live with a condition of difficult locomotion. Aiming to help these people, a vast variety of devices, techniques and services are currently being developed. Among those, one of the most complex and challenging techniques is the study and development of Brain-Computer Interfaces (BCIs). BCIs are systems that allow the user to communicate with the external world controlling devices without the use of muscles or peripheral nerves, using only his decoded brain activity. To achieve this, there is a need to develop robust pattern recognition systems, that must be able to detect the user’s intention through electroencephalography (EEG) signals and activate the corresponding output with reliable accuracy and within the shortest possible processing time. In this work, different EEG signal processing techniques were studied, and it is presented the development of a EEG under visual stimulation (Steady-State Visual Evoked Potentials - SSVEP) pattern recognition system. Using only Open Source tools and Python programming language, modules to manage datasets, reduce noise, extract features and perform classification of EEG signals were developed, and a comparative study of different techniques was performed, using filter banks and Discrete Wavelet Transforms (DWT) as feature extraction approach... (Resumo completo, clicar acesso eletrônico abaixo)
Mestre
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Siirtola, P. (Pekka). "Recognizing human activities based on wearable inertial measurements:methods and applications." Doctoral thesis, Oulun yliopisto, 2015. http://urn.fi/urn:isbn:9789526207698.
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Abstract Inertial sensors are devices that measure movement, and therefore, when they are attached to a body, they can be used to measure human movements. In this thesis, data from these sensors are studied to recognize human activities user-independently. This is possible if the following two hypotheses are valid: firstly, as human movements are dissimilar between activities, also inertial sensor data between activities is so different that this data can be used to recognize activities. Secondly, while movements and inertial data are dissimilar between activities, they are so similar when different persons are performing the same activity that they can be recognized as the same activity. In this thesis, pattern recognition -based solutions are applied to inertial data to find these dissimilarities and similarities, and therefore, to build models to recognize activities user-independently. Activity recognition within this thesis is studied in two contexts: daily activity recognition using mobile phones, and activity recognition in industrial context. Both of these contexts have special requirements and these are considered in the presented solutions. Mobile phones are optimal devices to measure daily activity: they include a wide range of useful sensors to detect activities, and people carry them with them most of the time. On the other hand, the usage of mobile phones in active recognition includes several challenges; for instance, a person can carry a phone in any orientation, and there are hundreds of smartphone models, and each of them have specific hardware and software. Moreover, as battery life is always as issue with smartphones, techniques to lighten the classification process are proposed. Industrial context is different from daily activity context: when daily activities are recognized, occasional misclassifications may disturb the user, but they do not cause any other type of harm. This is not the case when activities are recognized in industrial context and the purpose is to recognize if the assembly line worker has performed tasks correctly. In this case, false classifications may be much more harmful. Solutions to these challenges are presented in this thesis. The solutions introduced in this thesis are applied to activity recognition data sets. However, as the basic idea of the activity recognition problem is the same as in many other pattern recognition procedures, most of the solutions can be applied to any pattern recognition problem, especially to ones where time series data is studiedTiivistelmä Liikettä mittaavista antureista, kuten kiihtyvyysantureista, saatavaa tietoa voidaan käyttää ihmisten liikkeiden mittaamiseen kiinnittämällä ne johonkin kohtaan ihmisen kehoa. Väitöskirjassani tavoitteena on opettaa tähän tietoon perustuvia käyttäjäriippumattomia malleja, joiden avulla voidaan tunnistaa ihmisten toimia, kuten käveleminen ja juokseminen. Näiden mallien toimivuus perustuu seuraavaan kahteen oletukseen: (1) koska henkilöiden liikkeet eri toimissa ovat erilaisia, myös niistä mitattava anturitieto on erilaista, (2) useamman henkilön liikkeet samassa toimessa ovat niin samanlaisia, että liikkeistä mitatun anturitiedon perusteella nämä liikkeet voidaan päätellä kuvaavan samaa toimea. Tässä väitöskirjassa käyttäjäriippumaton ihmisten toimien tunnistus perustuu hahmontunnistusmenetelmiin ja tunnistusta on sovellettu kahteen eri asiayhteyteen: arkitoimien tunnistamiseen älypuhelimella sekä toimintojen tunnistamiseen teollisessa ympäristössä. Molemmilla sovellusalueilla on omat erityisvaatimuksensa ja -haasteensa. Älypuhelimien liikettä mittaavien antureihin perustuva tunnistus on haastavaa esimerkiksi siksi, että puhelimen asento ja paikka voivat vaihdella. Se voi olla esimerkiksi laukussa tai taskussa, lisäksi se voi olla missä tahansa asennossa. Myös puhelimen akun rajallinen kesto luo omat haasteensa. Tämän vuoksi tunnistus tulisi tehdä mahdollisimman kevyesti ja vähän virtaa kuluttavalla tavalla. Teollisessa ympäristössä haasteet ovat toisenlaisia. Kun tarkoituksena on tunnistaa esimerkiksi työvaiheiden oikea suoritusjärjestys kokoamislinjastolla, yksikin virheellinen tunnistus voi aiheuttaa suuren vahingon. Teollisessa ympäristössä tavoitteena onkin tunnistaa toimet mahdollisimman tarkasti välittämättä siitä kuinka paljon virtaa ja tehoa tunnistus vaatii. Väitöskirjassani kerrotaan kuinka nämä erityisvaatimukset ja -haasteet voidaan ottaa huomioon suunniteltaessa malleja ihmisten toimien tunnistamiseen. Väitöskirjassani esiteltyjä uusia menetelmiä on sovellettu ihmisten toimien tunnistamiseen. Samoja menetelmiä voidaan kuitenkin käyttää monissa muissa hahmontunnistukseen liittyvissä ongelmissa, erityisesti sellaisissa, joissa analysoitava tieto on aikasarjamuotoista
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Horn, Alastair N. "Iterated function systems, the parallel progressive synthesis of fractal tiling structures and their applications to computer graphics." Thesis, University of Oxford, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.257907.
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Onescu, Mircea Marian. "Adaptive measures of similarity - fuzzy hamming distance - and its applications to pattern recognition problems." Cincinnati, Ohio : University of Cincinnati, 2006. http://www.ohiolink.edu/etd/view.cgi?acc%5Fnum=ucin1163708478.
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Thesis (Ph. D.)--University of Cincinnati, 2006..Title from electronic thesis title page (viewed Jan.27, 2007). Includes abstract. Keywords: Fuzzy Hamming Distance, artificial intelligence, fuzzy, image retrieval system Includes bibliographical references.
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Al-aqeeli, Abdulqadir. "Reconfigurable wavelet-based architecture for pattern recognition applications using a field programmable gate array." Ohio : Ohio University, 1998. http://www.ohiolink.edu/etd/view.cgi?ohiou1177008904.
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Al-aqeeli, Abulqadir. "Reconfigurable wavelet-based architecture for pattern recognition applications using a field programmable gate array." Ohio University / OhioLINK, 1998. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1177008904.
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IONESCU, MIRCEA MARIAN. "ADAPTIVE MEASURES OF SIMILARITY - FUZZY HAMMING DISTANCE - AND ITS APPLICATIONS TO PATTERN RECOGNITION PROBLEMS." University of Cincinnati / OhioLINK, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1163708478.
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TESFAYE, YONATAN TARIKU. "Applications of a graph theoretic based clustering framework in computer vision and pattern recognition." Doctoral thesis, Università IUAV di Venezia, 2018. http://hdl.handle.net/11578/282321.
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Prasad, Saurabh. "MULTI-CLASSIFIERS AND DECISION FUSION FOR ROBUST STATISTICAL PATTERN RECOGNITION WITH APPLICATIONS TO HYPERSPECTRAL CLASSIFICATION." MSSTATE, 2008. http://sun.library.msstate.edu/ETD-db/theses/available/etd-11052008-125134/.
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In this dissertation, a multi-classifier, decision fusion framework is proposed for robust classification of high dimensional data in small-sample-size conditions. Such datasets present two key challenges. (1) The high dimensional feature spaces compromise the classifiers generalization ability in that the classifier tends to over-fit decision boundaries to the training data. This phenomenon is commonly known as the Hughes phenomenon in the pattern classification community. (2) The small-sample-size of the training data results in ill-conditioned estimates of its statistics. Most classifiers rely on accurate estimation of these statistics for modeling training data and labeling test data, and hence ill-conditioned statistical estimates result in poorer classification performance. This dissertation tests the efficacy of the proposed algorithms to classify primarily remotely sensed hyperspectral data and secondarily diagnostic digital mammograms, since these applications naturally result in very high dimensional feature spaces and often do not have sufficiently large training datasets to support the dimensionality of the feature space. Conventional approaches, such as Stepwise LDA (S-LDA) are sub-optimal, in that they utilize a small subset of the rich spectral information provided by hyperspectral data for classification. In contrast, the approach proposed in this dissertation utilizes the entire high dimensional feature space for classification by identifying a suitable partition of this space, employing a bank-of-classifiers to perform local classification over this partition, and then merging these local decisions using an appropriate decision fusion mechanism. Adaptive classifier weight assignment and nonlinear pre-processing (in kernel induced spaces) are also proposed within this framework to improve its robustness over a wide range of fidelity conditions. Experimental results demonstrate that the proposed framework results in significant improvements in classification accuracies (as high as a 12% increase) over conventional approaches.
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Jaafar, Mohd Zuli. "Chemometrics and pattern recognition methods with applications to environmental and quantitative structure-activity relationship studies." Thesis, University of Bristol, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.541608.
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Lemanska, Agnieszka. "Chemometrics and pattern recognition applications to high-shear wet granulation process monitoring and metabolomic data." Thesis, University of Bristol, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.551294.
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Pattern recognition is an analytical process that is now playing an increasingly important role in the interpretation and evaluation of data across many scientific disciplines. This thesis examines the application of pattern recognition techniques to both pharmaceutical process data and metabolomic data, and attempts to identify trends, as well as provide solutions to problems in these fields. Pattern recognition can be divided into univariate and multivariate techniques. Although this thesis examines both types of methods, the focus is primarily on multivariate techniques, because the pharmaceutical process data and the metabolomic data are both highly multivariate. Previous studies have provided data from these areas using a variety of analytical techniques such as acoustic emission (AE), nuclear magnetic resonance (NMR) and denaturing gradient gel electrophoresis (DGGE), and in this work multivariate pattern recognition is successful at extracting valuable information from these data. The first approach is the monitoring of a high-shear wet granulation process using AE spectroscopy. Granulation is a highly complex multi-step process, and there are many factors and variables, such as the duration of different phases, quantities of added materials, and speed of the granulator equipment. These factors can all have a considerable effect on the quality of the final granules that are produced, although it can be very difficult to pinpoint exactly to what degree they each influence the process. The combination of AE with pattern recognition techniques is studied here as an approach to identify the trends that are due to each variable. This study uses multiway analysis such as parallel factor analysis (PARAFAC) and multiway partial least squares (MPLS) to examine the data, as well as slope analysis techniques to analyse the trends between the acoustic profiles of batches that are characterized by different process variables. The results suggest that AE combined with pattern recognition techniques can be particularly useful to the pharmaceutical industry for the monitoring and control of granulation processes. The second approach looks at pattern recognition applied to the metabolomic data produced by two sources. An NMR dataset was obtained from human samples of saliva, half of which had been treated with a mouthwash. Multiple factors contributed to variance in the dataset. The total variance was split into parts characterized by these underlying experimental factors. Multilevel techniques were applied, based on a hierarchical relationship between the factors. Also, as the experimental factors in the study varied due to the programmed experimental design, ANOVA based techniques were utilized to interpret this variance. The results suggest that in metabolomic studies on humans there is a high variation between the subjects. This variation was dominant and once extracted, trends that were due to other experimental factors could be analysed. The second metabolomic dataset was a DGGE dataset. Pattern recognition techniques, in particular • principal coordinates analysis (PCO), were used to analyse the trends in microbial profiles of human sweat. It was shown that PCO components can be effectively used as an input for various classification techniques in order to observe individual or group separation.
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DE, GIORGI ANDREA. "Novel pattern recognition methods for classification and detection in remote sensing and power generation applications." Doctoral thesis, Università degli studi di Genova, 2018. http://hdl.handle.net/11567/930347.
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Huang, X. (Xiaohua). "Methods for facial expression recognition with applications in challenging situations." Doctoral thesis, Oulun yliopisto, 2014. http://urn.fi/urn:isbn:9789526206561.
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Abstract In recent years, facial expression recognition has become a useful scheme for computers to affectively understand the emotional state of human beings. Facial representation and facial expression recognition under unconstrained environments have been two critical issues for facial expression recognition systems. This thesis contributes to the research and development of facial expression recognition systems from two aspects: first, feature extraction for facial expression recognition, and second, applications to challenging conditions. Spatial and temporal feature extraction methods are introduced to provide effective and discriminative features for facial expression recognition. The thesis begins with a spatial feature extraction method. This descriptor exploits magnitude while it improves local quantized pattern using improved vector quantization. It also makes the statistical patterns domain-adaptive and compact. Then, the thesis discusses two spatiotemporal feature extraction methods. The first method uses monogenic signal analysis as a preprocessing stage and extracts spatiotemporal features using local binary pattern. The second method extracts sparse spatiotemporal features using sparse cuboids and spatiotemporal local binary pattern. Both methods increase the discriminative capability of local binary pattern in the temporal domain. Based on feature extraction methods, three practical conditions, including illumination variations, facial occlusion and pose changes, are studied for the applications of facial expression recognition. First, with near-infrared imaging technique, a discriminative component-based single feature descriptor is proposed to achieve a high degree of robustness and stability to illumination variations. Second, occlusion detection is proposed to dynamically detect the occluded face regions. A novel system is further designed for handling effectively facial occlusion. Lastly, multi-view discriminative neighbor preserving embedding is developed to deal with pose change, which formulates multi-view facial expression recognition as a generalized eigenvalue problem. Experimental results on publicly available databases show that the effectiveness of the proposed approaches for the applications of facial expression recognitionTiivistelmä Kasvonilmeiden tunnistamisesta on viime vuosina tullut tietokoneille hyödyllinen tapa ymmärtää affektiivisesti ihmisen tunnetilaa. Kasvojen esittäminen ja kasvonilmeiden tunnistaminen rajoittamattomissa ympäristöissä ovat olleet kaksi kriittistä ongelmaa kasvonilmeitä tunnistavien järjestelmien kannalta. Tämä väitöskirjatutkimus myötävaikuttaa kasvonilmeitä tunnistavien järjestelmien tutkimukseen ja kehittymiseen kahdesta näkökulmasta: piirteiden irrottamisesta kasvonilmeiden tunnistamista varten ja kasvonilmeiden tunnistamisesta haastavissa olosuhteissa. Työssä esitellään spatiaalisia ja temporaalisia piirteenirrotusmenetelmiä, jotka tuottavat tehokkaita ja erottelukykyisiä piirteitä kasvonilmeiden tunnistamiseen. Ensimmäisenä työssä esitellään spatiaalinen piirteenirrotusmenetelmä, joka parantaa paikallisia kvantisoituja piirteitä käyttämällä parannettua vektorikvantisointia. Menetelmä tekee myös tilastollisista malleista monikäyttöisiä ja tiiviitä. Seuraavaksi työssä esitellään kaksi spatiotemporaalista piirteenirrotusmenetelmää. Ensimmäinen näistä käyttää esikäsittelynä monogeenistä signaalianalyysiä ja irrottaa spatiotemporaaliset piirteet paikallisia binäärikuvioita käyttäen. Toinen menetelmä irrottaa harvoja spatiotemporaalisia piirteitä käyttäen harvoja kuusitahokkaita ja spatiotemporaalisia paikallisia binäärikuvioita. Molemmat menetelmät parantavat paikallisten binärikuvioiden erottelukykyä ajallisessa ulottuvuudessa. Piirteenirrotusmenetelmien pohjalta työssä tutkitaan kasvonilmeiden tunnistusta kolmessa käytännön olosuhteessa, joissa esiintyy vaihtelua valaistuksessa, okkluusiossa ja pään asennossa. Ensiksi ehdotetaan lähi-infrapuna kuvantamista hyödyntävää diskriminatiivistä komponenttipohjaista yhden piirteen kuvausta, jolla saavutetaan korkea suoritusvarmuus valaistuksen vaihtelun suhteen. Toiseksi ehdotetaan menetelmä okkluusion havainnointiin, jolla dynaamisesti havaitaan peittyneet kasvon alueet. Uudenlainen menetelmä on kehitetty käsittelemään kasvojen okkluusio tehokkaasti. Viimeiseksi työssä on kehitetty moninäkymäinen diskriminatiivisen naapuruston säilyttävään upottamiseen pohjautuva menetelmä käsittelemään pään asennon vaihtelut. Menetelmä kuvaa moninäkymäisen kasvonilmeiden tunnistamisen yleistettynä ominaisarvohajotelmana. Kokeelliset tulokset julkisilla tietokannoilla osoittavat tässä työssä ehdotetut menetelmät suorituskykyisiksi kasvonilmeiden tunnistamisessa
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Dong, Ming. "A New Measure of Classifiability and its Applications." University of Cincinnati / OhioLINK, 2001. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1003516324.
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Yu, Hua. "Pattern recognition methods for automated detection and quantification: applications to passive remote sensing and near infrared spectroscopy." Diss., University of Iowa, 2014. https://ir.uiowa.edu/etd/1522.
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Pattern recognition has over past decades become a fast growing area of chemometrics. Accurate, user-friendly, and fast pattern recognition methods are desired to accommodate the increased capacity of automated instruments to obtain large-scale data under complex circumstances. It has found significant applications in diverse fields such as environmental monitoring and biomedical diagnostics. In this dissertation, the capabilities of pattern recognition methods in case studies related to environmental remote sensing and biomedical sensing are investigated.
For remote sensing applications, two types of airborne spectroscopic data, passive Fourier transform infrared (FTIR) and gamma-ray, are subject to analysis in order to develop automated classifiers for either ammonia vapor or the radioisotope cesium-137 in the open-air. Support vector machine (SVM) classification is the primary pattern recognition method used in this work. In order to overcome the limitation of available representative patterns associated with airborne data, and provide sufficient patterns presenting the analyte-active class for use in the training set, a spectral simulation protocol is employed to generate abundant patterns bearing both the signature of the target analyte and the background spectral profile. Signal processing procedures including segment selection and digital filtering are further used to extract the information most relevant to the target analyte out the acquired raw data. Also, to ease the computational demand from the SVM, an alternative pattern recognition method, piecewise linear discriminant analysis (PLDA) is applied to optimize signal processing conditions for final SVM classification. Process control techniques are applied to the SVM score profiles of prediction sets to improve pattern recognition performance by incorporating probabilities associated with every SVM score. Ammonia classifiers developed from this methodology result in classification performance with high sensitivity and selectivity, and the cesium-137 classifiers developed from the same concepts exhibit excellent sensitivity to test data with very low signal strengths. Under the case of ammonia classification, the relationship between the concentration profile of the active patterns in the training set and the limit of detection of the corresponding classifier is investigated. Classifiers built to detect low concentrations of ammonia are developed and tested through this work.
For a glucose sensing application, studies are conducted to provide sound performance diagnostics for an established calibration model for glucose from near infrared spectroscopic data. Six-component aqueous matrixes of glucose in the presence of five other interfering species, all spanning physiological levels, serve as samples to be analyzed. A novel residual modeling protocol is proposed to retrieve the residual glucose concentrations, the concentration not being predicted by the calibration model, from the residual spectra, the portion of the raw spectra not being used by the calibration model. The recovered glucose concentration from the residual modeling can be used as a means, combined with process control techniques, to evaluate the performance of the established calibration model. Several modeling techniques are used for residual modeling, including PLS, support vector regression (SVR), a hybrid method, PLS-aided SVR, and an amplified version of the hybrid, amplified PLS-aided SVR. Through this work, a calibration updating strategy is developed which provides an effective way to monitor the established calibration model.