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Dissertations / Theses on the topic 'Pattern recognition'
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1
An, Kyung Hee. "Concurrent Pattern Recognition and Optical Character Recognition." Thesis, University of North Texas, 1991. https://digital.library.unt.edu/ark:/67531/metadc332598/.
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The problem of interest as indicated is to develop a general purpose technique that is a combination of the structural approach, and an extension of the Finite Inductive Sequence (FI) technique. FI technology is pre-algebra, and deals with patterns for which an alphabet can be formulated.
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Yao, Xiaoqiang. "Pattern-recognition scheduling." Ohio : Ohio University, 1996. http://www.ohiolink.edu/etd/view.cgi?ohiou1177698616.
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Petheram, R. J. "Automatic pattern recognition." Thesis, University of Nottingham, 1989. http://eprints.nottingham.ac.uk/28974/.
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In this thesis the author presents a new method for the location, extraction and normalisation of discrete objects found in digital images. The extraction is by means of sub-pixcel contour following around the object. The normalisation obtains and removes the information concerning size, orientation and location of the object within an image. Analyses of the results are carried out to determine the confidence in recognition of patterns, and methods of cross correlation of object descriptions using Fourier transforms are demonstrated.
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Choakjarernwanit, Naruetep. "Statistical pattern recognition." Thesis, University of Surrey, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.306586.
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Placide, Eustache. "Hybrid pattern recognition." DigitalCommons@Robert W. Woodruff Library, Atlanta University Center, 1987. http://digitalcommons.auctr.edu/dissertations/3018.
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There are two basic approaches to pattern recognition: decision-theoretic and syntactic. However, in actual applications, a combination of both may be needed. One such hybrid technique consists of syntactic method coupled with stochasticity in its grammar. Randomness in the syntactic case is caused due to noise and insufficient information about characteristics of pattern classes. To absorb the effect of this randomness, the grammar must be generalized to include the probabilities of production rules.
In this paper, a preliminary discussion of issues involved with hybrid techniques, in general, and stochastic grammars, in particular, is provided. An efficient algorithm for an automatic learning of production probabilities is devised. Concepts are illustrated via examples.
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Smeraldi, Fabrizio. "Attention-driven pattern recognition /." [S.l.] : [s.n.], 2000. http://library.epfl.ch/theses/?nr=2153.
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Angstenberger, Larisa. "Dynamic fuzzy pattern recognition." [S.l.] : [s.n.], 2000. http://deposit.ddb.de/cgi-bin/dokserv?idn=962701106.
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Weir, D. K. "Pattern recognition of electrocardiograms." Thesis, University of Ulster, 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.355922.
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Lindén, Fredrik. "Fractal pattern recognition and recreation." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-181224.
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It speaks by itself that in order to find oil, one must know where to look for it. In this thesis I have investigated and created new tools to find salt in the bedrock, and to recreate images according to some parameters, (fractal dimension and lacunarity). The oil prospecting company Schlumberger gathers nowadays a huge amount of seismic information. It is very time consuming to interpret the seismic data by hand. My task is to find a good way to detect salt in the seismic images of the underworld, that can then be used to classify the seismic data. The theory indicates that the salt behaves as fractals, and by studying the fractal dimension and lacunarity we can make a prediction of where the salt can be located. I have also investigated three different recreation techniques, so that one can go from parameters values (fractal dimension and lacunarity) back to a possible recreation. It speaks by itself that in order to find oil, one must know where to look for it. In this thesis I have investigated and created new tools to find salt in the bedrock, and to recreate images according to some parameters, (fractal dimension and lacunarity). The oil prospecting company Schlumberger gathers nowadays a huge amount of seismic information. It is very time consuming to interpret the seismic data by hand. My task is to find a good way to detect salt in the seismic images of the underworld, that can then be used to classify the seismic data. The theory indicates that the salt behaves as fractals, and by studying the fractal dimension and lacunarity we can make a prediction of where the salt can be located. I have also investigated three different recreation techniques, so that one can go from parameters values (fractal dimension and lacunarity) back to a possible recreation.
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Sun, Te-Wei. "DEPARS, design pattern recognition system." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp04/mq28464.pdf.
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Hoang, Thai V. "Image Representations for Pattern Recognition." Phd thesis, Université Nancy II, 2011. http://tel.archives-ouvertes.fr/tel-00714651.
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La pertinence d'une application de traitement de signal relève notamment du choix d'une "représentation adéquate''. Par exemple, pour la reconnaissance de formes, la représentation doit mettre en évidence les propriétés salientes d'un signal; en débruitage, permettre de séparer le signal du bruit; ou encore en compression, de synthétiser fidèlement le signal d'entrée à l'aide d'un nombre réduit de coefficients. Bien que les finalités de ces quelques traitements soient distinctes, il apparait clairement que le choix de la représentation impacte sur les performances obtenues. La représentation d'un signal implique la conception d'un ensemble génératif de signaux élémentaires, aussi appelé dictionnaire ou atomes, utilisé pour décomposer ce signal. Pendant de nombreuses années, la conception de dictionnaire a suscité un vif intérêt des chercheurs dans des domaines applicatifs variés: la transformée de Fourier a été employée pour résoudre l'équation de la chaleur; celle de Radon pour les problèmes de reconstruction; la transformée en ondelette a été introduite pour des signaux monodimensionnels présentant un nombre fini de discontinuités; la transformée en contourlet a été conçue pour représenter efficacement les signaux bidimensionnels composées de régions d'intensité homogène, à frontières lisses, etc. Jusqu'à présent, les dictionnaires existants peuvent être regroupés en deux familles d'approches: celles s'appuyant sur des modèles mathématiques de données et celles concernant l'ensemble de réalisations des données. Les dictionnaires de la première famille sont caractérisés par une formulation analytique. Les coefficients obtenus dans de telles représentations d'un signal correspondent à une transformée du signal, qui peuvent parfois être implémentée rapidement. Les dictionnaires de la seconde famille, qui sont fréquemment des dictionnaires surcomplets, offrent une grande flexibilité et permettent d'être adaptés aux traitements de données spécifiques. Ils sont le fruit de travaux plus récents pour lesquels les dictionnaires sont générés à partir des données en vue de la représentation de ces dernières. L'existence d'une multitude de dictionnaires conduit naturellement au problème de la sélection du meilleur d'entre eux pour la représentation de signaux dans un cadre applicatif donné. Ce choix doit être effectué en vertu des spécificités bénéfiques validées par les applications envisagées. En d'autres termes, c'est l'usage qui conduit à privilégier un dictionnaire. Dans ce manuscrit, trois types de dictionnaire, correspondant à autant de types de transformées/représentations, sont étudiés en vue de leur utilisation en analyse d'images et en reconnaissance de formes. Ces dictionnaires sont la transformée de Radon, les moments basés sur le disque unitaire et les représentations parcimonieuses. Les deux premiers dictionnaires sont employés pour la reconnaissance de formes invariantes tandis que la représentation parcimonieuse l'est pour des problèmes de débruitage, de séparation des sources d'information et de classification. Cette thèse présentent des contributions théoriques validées par de nombreux résultats expérimentaux. Concernant la transformée de Radon, des pistes sont proposées afin d'obtenir des descripteurs de formes invariants, et conduisent à définir deux descripteurs invariants aux rotations, l'échelle et la translation. Concernant les moments basés sur le disque unitaire, nous formalisons les stratégies conduisant à l'obtention de moments orthogonaux. C'est ainsi que quatre moments harmoniques polaires génériques et des stratégies pour leurs calculs rapides sont introduits. Enfin, concernant les représentations parcimonieuses, nous proposons et validons un formalisme de représentation permettant de combiner les trois critères suivant : la parcimonie, l'erreur de reconstruction ainsi que le pouvoir discriminant en classification.
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Wong, K. H. "Dynamic programming in pattern recognition." Thesis, University of Cambridge, 1986. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.383059.
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Kinna, David John. "Pattern recognition in chemical crystallography." Thesis, University of Oxford, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.318724.
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Daemi, M. F. "Information theory and pattern recognition." Thesis, University of Nottingham, 1990. http://eprints.nottingham.ac.uk/14003/.
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This thesis presents an account of an investigation into the use of information theory measures in pattern recognition problems. The objectives were firstly to determine the information content of the set of representations of an input image which are found at the output of an array of sensors; secondly to assess the information which may be used to allocate different patterns to appropriate classes in order to provide a means of recognition; and thirdly to assess the recognition capability of pattern recognition systems and their efficiency of utilization of information. Information assessment techniques were developed using fundamental principles of information theory. These techniques were used to assess the information associated with attributes such as orientation and location, of a variety of input images. The techniques were extended to permit the assessment of recognition capability and to provide a measure of the efficiency with which pattern recognition systems use the information available.
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Sardana, H. K. "Edge moments in pattern recognition." Thesis, University of Nottingham, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.357101.
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Füllen, Georg Karl-Heinz. "Protein engineering and pattern recognition." Thesis, Massachusetts Institute of Technology, 1994. http://hdl.handle.net/1721.1/17354.
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STOSIC, Dusan. "q-Gaussians for pattern recognition." Universidade Federal de Pernambuco, 2016. https://repositorio.ufpe.br/handle/123456789/17361.
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Pattern recognition plays an important role for solving many problems in our everyday lives: from simple tasks such as reading texts to more complex ones like driving cars. Subconsciously, the recognition of patterns is instantaneous and an innate ability to every human. However, programming (or “teaching”) a machine how to do the same can present an incredibly difficult task. There are many situations where irrelevant or misleading patterns, poorly represented classes, and complex decision boundaries make recognition very hard, or even impossible by current standards. Important contributions to the field of pattern recognition have been attained through the adoption of methods of statistical mechanics, which has paved the road for much of the research done in academia and industry, ranging from the revival of connectionism to modern day deep learning. Yet traditional statistical mechanics is not universal and has a limited domain of applicability - outside this domain it can make wrong predictions. Non-extensive statistical mechanics has recently emerged to cover a variety of anomalous situations that cannot be described within standard Boltzmann-Gibbs theory, such as non-ergodic systems characterized by long-range interactions, or long-term memories. The literature on pattern recognition is vast, and scattered with applications of non-extensive statistical mechanics. However, most of this work has been done using non-extensive entropy, and little can be found on practical applications of other non-extensive constructs. In particular, non-extensive entropy is widely used to improve segmentation of images that possess strongly correlated patterns, while only a small number of works employ concepts other than entropy for solving similar recognition tasks. The main goal of this dissertation is to expand applications of non-extensive distributions, namely the q-Gaussian, in pattern recognition. We present ourcontributions in the form of two (published) articles where practical uses of q-Gaussians are explored in neural networks. The first paper introduces q Gaussian transfer functions to improve classification of random neural networks, and the second paper extends this work to ensembles which involves combining a set of such classifiers via majority voting.
Reconhecimento de padrões tem um papel importante na solução de diversos problemas no nosso quotidiano: a partir de tarefas simples como ler textos, até as mais complexas como dirigir carros. Inconscientemente, o reconhecimento de padrões pelo cérebro é instantâneo, representando uma habilidade inata de cada ser humano. No entanto, programar (ou “ensinar”) uma máquina para fazer o mesmo pode se tornar uma tarefa extremamente difícil. Há muitas situações onde padrões irrelevantes ou enganosos, classes mal representadas, ou bordas de decisões complexas, tornam o reconhecimento muito difícil, ou mesmo impossível pelos padrões atuais. Diversas contribuições importantes na área de reconhecimento de padrões foram alcançadas através da aplicação de métodos provenientes da mecânica estatística, que estimularam uma grande parte da pesquisa conduzida na academia bem como na indústria, desde o renascimento de conexionismo até o moderno conceito de “deep learning”. No entanto, a mecânica estatística tradicional não é universal e tem um domínio de aplicação limitado - fora deste domínio ela pode fazer previsões erradas. A mecânica estatística não-extensiva surgiu recentemente para atender uma variedade de situações anômalas que não podem ser descritas de forma adequada com a teoria de Boltzmann-Gibbs, tais como sistemas não-ergódicos, caracterizadas por interações de longo alcance, ou memórias de longo prazo. A literatura sobre reconhecimento de padrões é vasta, e dispersa com aplicações da mecânica estatística não-extensiva. No entanto, a maioria destes trabalhos utilizam a entropia não-extensiva, e existem poucas aplicações práticas de outros conceitos não-extensivos. Em particular, a entropia não extensiva é amplamente usada para aperfeiçoar segmentação de imagens que possuem padrões fortemente correlacionados, enquanto apenas um pequeno número de trabalhos empregam outros conceitos não-extensivos para resolver tarefas semelhantes. O objetivo principal desta dissertação é expandir aplicações de distribuições não-extensivas, como a q-Gaussiana, em reconhecimento de padrões. Nos apresentamos as nossas contribuições no formato de dois artigos (publicados) onde exploramos usos práticos da q-Gaussiana em redes neurais. O primeiro artigo introduz funções de transferência baseados na q-Gaussiana para aperfeiçoar a classificação de redes neurais aleatórias, e o segundo artigo estende este trabalho para ensembles, onde um conjunto de tais classificadores são combinados através de votação por maioria.
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Ihnatenko, N. V. "Systems for automatic pattern recognition." Thesis, Сумський державний університет, 2014. http://essuir.sumdu.edu.ua/handle/123456789/34837.
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Pattern recognition aims to make the process of learning and detection of patterns explicit, such that it can partially or entirely be implemented on computers. Automatic (machine) recognition, description, classification (grouping of patterns into pattern classes) have become important problems in a variety of engineering and scientific disciplines such as biology, psychology, medicine, marketing, computer vision, artificial intelligence, and remote sensing. In almost any area of science in which observations are studied but the underlying mathematical or statistical models are not available, pattern recognition can be used to support human concept acquisition or decision making. Given a group of objects, there are two ways to build a classification or recognition system, supervised, i.e., with a teacher, or unsupervised, without the help of a teacher.
When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/34837
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Dover, Kathryn. "Pattern Recognition in Stock Data." Scholarship @ Claremont, 2017. http://scholarship.claremont.edu/hmc_theses/105.
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Finding patterns in high dimensional data can be difficult because it cannot be easily visualized. There are many different machine learning methods to fit data in order to predict and classify future data but there is typically a large expense on having the machine learn the fit for a certain part of a dataset. We propose a geometric way of defining different patterns in data that is invariant under size and rotation. Using a Gaussian Process, we find that pattern within stock datasets and make predictions from it.
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Martino, Federico De. "Pattern recognition of brain signals." [Maastricht] : [Maastricht University], 2008. http://arno.unimaas.nl/show.cgi?fid=13359.
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Burles, Nathan. "Pattern recognition using associative memories." Thesis, University of York, 2014. http://etheses.whiterose.ac.uk/7368/.
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The human brain is extremely effective at performing pattern recognition, even in the presence of noisy or distorted inputs. Artificial neural networks attempt to imitate the structure of the brain, often with a view to mimicking its success. The binary correlation matrix memory (CMM) is a particular type of neural network that is capable of learning and recalling associations extremely quickly, as well as displaying a high storage capacity and having the ability to generalise from patterns already learned. CMMs have been used as a major component of larger architectures designed to solve a wide range of problems, such as rule chaining, character recognition, or more general pattern recognition. It is clear that the memory requirement of the CMMs will thus have a significant impact on the scalability of such architectures. A domain specific language for binary CMMs is developed, alongside an implementation that uses an efficient storage mechanism which allows memory usage to scale linearly with the number of associations stored. An architecture for rule chaining is then examined in detail, showing that the problem of scalability is indeed settled before identifying and resolving a number of important limitations to its capabilities. Finally an architecture for pattern recognition is investigated, and a memory efficient method to incorporate general invariance into this architecture is presented---this is specifically tested with scale invariance, although the mechanism can be used with other types of invariance such as skew or rotation.
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Calvo-Zaragoza, Jorge. "Pattern Recognition for Music Notation." Doctoral thesis, Universidad de Alicante, 2016. http://hdl.handle.net/10045/63415.
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UL-ISLAM, IHTESHAM. "Feature Fusion for Pattern Recognition." Doctoral thesis, Politecnico di Torino, 2015. http://hdl.handle.net/11583/2592755.
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Information or data fusion is one of the solutions adopted for improving the performance
of a pattern recognition system. Information can be gathered either from
multiple data sources or through the use of multiple representations generated from
a single data source. A single representation summarizes the information and provides
a single cue on the data, and thus may not be able to fully reveal the inherent
characteristics of the data. In visual recognition, image representations are generally
categorized into global and local based types. A global representation captures
features corresponds to some holistic characteristic in the image, and produces a
coarse representation. Differently, a local representation reveals detail variations
and traits inherent to the image. Psychological findings have shown that humans
equally rely on both local and global visual information. Moreover, there is a large
agreement in literature that the combination of different features, i.e. a multiview
perspective, can have a positive effect on the performance of a pattern recognition
system. In fact, different features can represent different and complementary characteristics
of the data; in other words, each feature set represent a different view on
the original dataset. Thus it is expected that a visual recognition system can benefit
from different representations (both local and global) through the use of information
fusion.
Information can generally be consolidated at three different levels: (i) decision
level; (ii) match score level; and (iii) feature level. In the literature match level and
decision level fusion (i.e. combining the output of different classifier, each of them
working on different feature sets) have been extensively studied, whereas feature
level fusion is a relatively understudied problem because of the difficulties inherent
to its correct implementation. Feature level fusion may incorporate redundant, noisy
or trivial information and the concatenated feature vectors may lead to the problem
of curse of dimensionality. In addition, the feature sets may not be compatible
and relationship between different feature spaces may not be known. Moreover,
this integration comes at a cost, which may incur in units of time, computational
resources or even money. Nevertheless, it is thought that fusing features at this level
would still retain a richer source of discriminative information.
Motivated by the belief, this thesis investigates the use of feature level fusion and
its correlation with feature selection and classification tasks for two recent pattern
recognition problems. These include the classification of six types of HEp2 staining
patterns and the automatic verification of kinship relations in a pair of face images.
several image attributes are proposed, that are better capable of characterizing the
different kind of images associated with the two said classification tasks. Feature
level fusion of the different attributes is performed followed by a careful reduction of
features, through the use of pertinent feature selection and classification algorithms,
that decide the most representative and discriminative feature sets for the patterns to
classify. Results indicate that the proposed techniques working on the combination
of features of different natures, which are capable of describing the data under
different perspectives, is an effective strategy in achieving higher accuracy.
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Ou, Chung-Pei. "Protein array for small molecules recognition using pattern recognition." Thesis, Imperial College London, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.420941.
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Al, Rifaee Mustafa Moh'd Husien. "Unconstrained iris recognition." Thesis, De Montfort University, 2014. http://hdl.handle.net/2086/10949.
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This research focuses on iris recognition, the most accurate form of biometric identification. The robustness of iris recognition comes from the unique characteristics of the human, and the permanency of the iris texture as it is stable over human life, and the environmental effects cannot easily alter its shape. In most iris recognition systems, ideal image acquisition conditions are assumed. These conditions include a near infrared (NIR) light source to reveal the clear iris texture as well as look and stare constraints and close distance from the capturing device. However, the recognition accuracy of the-state-of-the-art systems decreases significantly when these constraints are relaxed. Recent advances have proposed different methods to process iris images captured in unconstrained environments. While these methods improve the accuracy of the original iris recognition system, they still have segmentation and feature selection problems, which results in high FRR (False Rejection Rate) and FAR (False Acceptance Rate) or in recognition failure. In the first part of this thesis, a novel segmentation algorithm for detecting the limbus and pupillary boundaries of human iris images with a quality assessment process is proposed. The algorithm first searches over the HSV colour space to detect the local maxima sclera region as it is the most easily distinguishable part of the human eye. The parameters from this stage are then used for eye area detection, upper/lower eyelid isolation and for rotation angle correction. The second step is the iris image quality assessment process, as the iris images captured under unconstrained conditions have heterogeneous characteristics. In addition, the probability of getting a mis-segmented sclera portion around the outer ring of the iris is very high, especially in the presence of reflection caused by a visible wavelength light source. Therefore, quality assessment procedures are applied for the classification of images from the first step into seven different categories based on the average of their RGB colour intensity. An appropriate filter is applied based on the detected quality. In the third step, a binarization process is applied to the detected eye portion from the first step for detecting the iris outer ring based on a threshold value defined on the basis of image quality from the second step. Finally, for the pupil area segmentation, the method searches over the HSV colour space for local minima pixels, as the pupil contains the darkest pixels in the human eye. In the second part, a novel discriminating feature extraction and selection based on the Curvelet transform are introduced. Most of the state-of-the-art iris recognition systems use the textural features extracted from the iris images. While these fine tiny features are very robust when extracted from high resolution clear images captured at very close distances, they show major weaknesses when extracted from degraded images captured over long distances. The use of the Curvelet transform to extract 2D geometrical features (curves and edges) from the degraded iris images addresses the weakness of 1D texture features extracted by the classical methods based on textural analysis wavelet transform. Our experiments show significant improvements in the segmentation and recognition accuracy when compared to the-state-of-the-art results.
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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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Mankoff, Jennifer C. "An architecture and interaction techniques for handling ambiguity in recognition-based input." Diss., Georgia Institute of Technology, 2001. http://hdl.handle.net/1853/8214.
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Mahmood, A. "Automatic drawing recognition." Thesis, University of Nottingham, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.381072.
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Scott, Emily A. "Recognition of aerospace acoustic sources using advanced pattern recognition techniques." Thesis, This resource online, 1991. http://scholar.lib.vt.edu/theses/available/etd-03022010-020131/.
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Suh, Bongwon. "Image management using pattern recognition systems." College Park, Md. : University of Maryland, 2005. http://hdl.handle.net/1903/2455.
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Thesis (Ph. D.) -- University of Maryland, College Park, 2005.Thesis research directed by: Computer Science. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Hall, Arthur Daniel. "Pipelined image processing for pattern recognition." Thesis, University of Cambridge, 1992. https://www.repository.cam.ac.uk/handle/1810/251523.
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Colven, David Michael. "Tactile pattern recognition using neural networks." Thesis, University of Ottawa (Canada), 1993. http://hdl.handle.net/10393/6513.
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This thesis presents a system for the capture and recognition of tactile images using neural networks. Neural Networks utilizing the backpropagation technique are used to provide a general purpose recognition engine for several classes of pattern recognition problems. Examples of successful networks are presented with discussion of the results and methodology for development of each. The system consists of the following: Image capture, training of network using an iterative approach and testing of the network against independent images not present during training. Pattern capture is performed by scanning a force sensitive tactile sensor that interfaces to a general purpose computer. Following capture, examples of tactile patterns of desired types are stored in a training file and the training goal of the network set. The goal is determined by the "trainer" who when the patterns are captured indicates the pattern type. Related patterns are given the same class name. The Network is required to consist of as many output neurons as classification types. The goal is that an output neuron becomes "activated" when its pattern types are present and "de-activated" when another type is present. Patterns in the training file are then recursively applied to the inputs of the Neural Network. Once the Network converges to the desired goal it is tested against a new set of patterns to determine if the network has learned to apply generalization in its recognition of the patterns. The training set and network topology may be modified in heuristic fashion until satisfactory results are achieved.
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Zieba, Maciej. "Multistage neural networks for pattern recognition." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2087.
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In this work the concept of multistage neural networks is going to be presented. The possibility of using this type of structure for pattern recognition would be discussed and examined with chosen problem from eld area. The results of experiment would be confront with other possible methods used for the problem.
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Kleyko, Denis. "Pattern Recognition with Vector Symbolic Architectures." Licentiate thesis, Luleå tekniska universitet, Datavetenskap, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-17439.
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Pattern recognition is an area constantly enlarging its theoretical and practical horizons. Applications of pattern recognition and machine learning can be found in many areas of the present day world including health-care, robotics, manufacturing, economics, automation, transportation, etc. Despite some success in many domains pattern recognition algorithms are still far from being close to their biological vis-a-vis – human brain. New possibilities in the area of pattern recognition may be achieved by application of biologically inspired approaches. This thesis presents the usage of a bio-inspired method of representing concepts and their meaning – Vector Symbolic Architectures – in the context of pattern recognition with possible applications in intelligent transportation systems, automation systems, and language processing. Vector Symbolic Architectures is an approach for encoding and manipulating distributed representations of information. They have previously been used mainly in the area of cognitive computing for representing and reasoning upon semantically bound information. First, it is shown that Vector Symbolic Architectures are capable of pattern classification of temporal patterns. With this approach, it is possible to represent, learn and subsequently classify vehicles using measurements from vibration sensors.Next, an architecture called Holographic Graph Neuron for one-shot learning of patterns of generic sensor stimuli is proposed. The architecture is based on implementing the Hierarchical Graph Neuron approach using Vector Symbolic Architectures. Holographic Graph Neuron shows the previously reported performance characteristics of Hierarchical Graph Neuron while maintaining the simplicity of its design. The Holographic Graph Neuron architecture is applied in two domains: fault detection and longest common substrings search. In the area of fault detection the architecture showed superior performance compared to classical methods of artificial intelligence while featuring zero configuration and simple operations. The application of the architecture for longest common substrings search showed its ability to robustly solve the task given that the length of a common substring is longer than 4% of the longest pattern. Furthermore, the required number of operations on binary vectors is equal to the suffix trees approach, which is the fastest traditional algorithm for this problem. In summary, the work presented in this thesis extends understanding of the performance proprieties of distributed representations and opens the way for new applications.Godkänd; 2016; 20160207 (denkle); Nedanstående person kommer att hålla licentiatseminarium för avläggande av teknologie licentiatexamen. Namn: Denis Kleyko Ämne: Kommunikations- och beräkningssystem / Dependable Communication and Computation Systems Uppsats: Pattern Recognition with Vector Symbolic Architectures Examinator: Professor Evgeny Osipov Institutionen för system- och rymdteknik, Avdelning: Datavetenskap, Luleå tekniska universitet. Diskutant: Associate Professor Okko Räsänen, Aalto University, Department of Signal Processing and Acoustics, Finland. Tid: Måndag 21 mars, 2016 kl 10.00 Plats: A109, Luleå tekniska universitet
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Wang, Qun. "Bootstrap techniques for statistical pattern recognition." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape3/PQDD_0027/MQ52407.pdf.
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Sherrah, Jamie. "Automatic feature extraction for pattern recognition /." Title page, contents and abstract only, 1998. http://web4.library.adelaide.edu.au/theses/09PH/09phs553.pdf.
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Thesis (Ph. D.)--University of Adelaide, Dept. of Electrical and Electronic Engineering, 1999.CD-ROM in back pocket comprises experimental results and executables. Includes bibliographical references (p. 251-261).
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Surkov, David. "Inductive confidence machine for pattern recognition." Thesis, Royal Holloway, University of London, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.412337.
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Elliffe, Martin C. M. "Neural networks for Invariant pattern recognition." Thesis, University of Oxford, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.302530.
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Ragothaman, Pradeep. "EFFICIENT ALGORITHMS FOR CORRELATION PATTERN RECOGNITION." Doctoral diss., University of Central Florida, 2007. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2132.
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The mathematical operation of correlation is a very simple concept, yet has a very rich history of application in a variety of engineering fields. It is essentially nothing but a technique to measure if and to what degree two signals match each other. Since this is a very basic and universal task in a wide variety of fields such as signal processing, communications, computer vision etc., it has been an important tool. The field of pattern recognition often deals with the task of analyzing signals or useful information from signals and classifying them into classes. Very often, these classes are predetermined, and examples (templates) are available for comparison. This task naturally lends itself to the application of correlation as a tool to accomplish this goal. Thus the field of Correlation Pattern Recognition has developed over the past few decades as an important area of research. From the signal processing point of view, correlation is nothing but a filtering operation. Thus there has been a great deal of work in using concepts from filter theory to develop Correlation Filters for pattern recognition. While considerable work has been to done to develop linear correlation filters over the years, especially in the field of Automatic Target Recognition, a lot of attention has recently been paid to the development of Quadratic Correlation Filters (QCF). QCFs offer the advantages of linear filters while optimizing a bank of these simultaneously to offer much improved performance. This dissertation develops efficient QCFs that offer significant savings in storage requirements and computational complexity over existing designs. Firstly, an adaptive algorithm is presented that is able to modify the QCF coefficients as new data is observed. Secondly, a transform domain implementation of the QCF is presented that has the benefits of lower computational complexity and computational requirements while retaining excellent recognition accuracy. Finally, a two dimensional QCF is presented that holds the potential to further save on storage and computations. The techniques are developed based on the recently proposed Rayleigh Quotient Quadratic Correlation Filter (RQQCF) and simulation results are provided on synthetic and real datasets.Ph.D.
School of Electrical Engineering and Computer Science
Engineering and Computer Science
Electrical Engineering PhD
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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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Choakjarernwanit, Naruetep. "Feature selection in statistical pattern recognition." Thesis, University of Surrey, 1992. http://epubs.surrey.ac.uk/843569/.
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This thesis addresses the problem of feature selection in pattern recognition. A detailed analysis and an experimental comparison of various search strategies for selecting a feature set of size d from D available measurements are presented. For a realistic problem, optimal search, even if performed using the branch and bound search method, is computationally prohibitive. The alternative is to use suboptimal search methods. Of these, there are four methods, namely the sequential forward selection (SFS), sequential backward selection (SBS), sequential forward floating selection (SFFS), and sequential backward floating selection (SBFS), which are relatively simple and require little computational time. It is suggested that the SFS method should be employed in the case of limited training sample size. Although the decision about including a particular measurements in the SFS method is made on the basis of statistical dependencies among features in spaces of monotonically increasing dimensionality, the approach has proved in practice to be more reliable. This is because the algorithm utilizes at the beginning only less complex mutual relations which using small sample sets are determined more reliably than the statistics required by the SBS method. Because both the SFS and SBS methods suffer from the nesting effect, if better solution is required then the SFFS and SBFS should be employed. As the first of the two main issues of the thesis, the possibility of developing feature selection techniques which rely only on the merit of individual features as well as pairs of features is investigated. This issue is considered very important because the computational advantage of such an algorithm exploiting only at most pairwise interactions of measurements would be very useful for solving feature selection problems of very high dimensionality. For this reason, a potentially very promising search method known as the Max-Min method is investigated. By means of a detailed analysis of the heuristic reasoning behind the method its weaknesses are identified. The first weakness is due to the use of upper limit on the error bound as a measure of effectiveness of a candidate feature. This strategy does not guarantee that selecting a candidate feature with the highest upper bound will yield the highest actual amount of additional information. The second weakness is that the method does not distinguish between a strong unconditional dependence and a poor performance of a feature which both manifest themselves by a near zero additional discriminatory information. Modifications aimed at overcoming the latter by favouring features which exhibit conditional dependence and on the other hand suppressing features which exhibit strong unconditional dependence have been proposed and tested but only with a limited success. For this reason the Max-Min method is subjected to a detailed theoretical analysis. It is found that the key assumption underlying the whole Max-Min algorithm is not justified and the algorithm itself is ill-founded, i.e. the actual increment of the criterion value (or decrease of the probability of error) can be bigger than the minimum of pairwise error probability reductions assumed by the Max-Min method. A necessary condition for invalidity of the key assumption of the Max-Min algorithm is derived, and a counter-example proving the lack of justification for the algorithm is presented. The second main issue of the thesis is the development of a new feature selection method for non-normal class conditional densities. For a given dimensionality the subset of selected features minimizes the Kullback-Leibler distance between the true and postulated class conditional densities. The algorithm is based on approximating unknown class conditional densities by a finite mixture of densities of a special type using the maximum likelihood approach. After the optimization ends, the optimal feature subset of required dimensionality is obtained immediately without the necessity to employ any search procedure. Successful experiments with both simulated and real data are also carried out to validate the proposed method.
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Zhendong, Wang. "Error Pattern Recognition Using Machine Learning." Thesis, Linköpings universitet, Statistik och maskininlärning, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-150589.
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Mobile networks use automated continuous integration to secure the new technologies, which must reach high quality and backwards compatibility. The machinery needs to be constantly improved to meet the high demands that exist today and will evolve in the future. When testing products in large scale in a telecommunication environment, many parameters may be causing the error. Machine learning can help to assign troubleshooting labels and identify problematic areas in the test environment. In this thesis project, different modeling approaches will be applied step-wise. First, both the TF-IDF (term frequency-inverse document frequency) method and Topic model- ing will be applied for constructing variables. Since the TF-IDF method generates high dimensional variables in this case, Principal component analysis (PCA) is considered as a regularization method to reduce the dimensions. The results of this part will be evaluated by using different criteria. After the variable construction, two semi-supervised models called Label propagation and Label spreading will be applied for the purpose of assigning troubleshooting labels. In both algorithms, one weight matrix for measuring the similarities between different cases needs to be constructed. Two different methods for building up the weight matrix will be tested separately: Gaussian kernel and the nearest-neighbor method. Different hyperparameters in these two algorithms will be experimented with, to select the one which will return the optimal results. After the optimal model is selected, the unlabeled data will be divided up in different proportions for fitting the model. This is to test if the proportions of unlabeled data will affect the result of semi-supervised learning in our case. The classification results from the modeling part will be examined using three classical measures: accuracy, precision and recall. In addition, random permutations cross- validation is applied for the evaluation.
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Lucas, Simon Mark. "Connectionist architectures for syntactic pattern recognition." Thesis, University of Southampton, 1991. https://eprints.soton.ac.uk/256263/.
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Kou, Yufeng. "Abnormal Pattern Recognition in Spatial Data." Diss., Virginia Tech, 2006. http://hdl.handle.net/10919/30145.
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In the recent years, abnormal spatial pattern recognition has received a great deal of attention from both industry and academia, and has become an important branch of data mining. Abnormal spatial patterns, or spatial outliers, are those observations whose characteristics are markedly different from their spatial neighbors. The identification of spatial outliers can be used to reveal hidden but valuable knowledge in many applications. For example, it can help locate extreme meteorological events such as tornadoes and hurricanes, identify aberrant genes or tumor cells, discover highway traffic congestion points, pinpoint military targets in satellite images, determine possible locations of oil reservoirs, and detect water pollution incidents.
Numerous traditional outlier detection methods have been developed, but they cannot be directly applied to spatial data in order to extract abnormal patterns. Traditional outlier detection mainly focuses on "global comparison" and identifies deviations from the remainder of the entire data set. In contrast, spatial outlier detection concentrates on discovering neighborhood instabilities that break the spatial continuity. In recent years, a number of techniques have been proposed for spatial outlier detection. However, they have the following limitations. First, most of them focus primarily on single-attribute outlier detection. Second, they may not accurately locate outliers when multiple outliers exist in a cluster and correlate with each other. Third, the existing algorithms tend to abstract spatial objects as isolated points and do not consider their geometrical and topological properties, which may lead to inexact results.
This dissertation reports a study of the problem of abnormal spatial pattern recognition, and proposes a suite of novel algorithms. Contributions include: (1) formal definitions of various spatial outliers, including single-attribute outliers, multi-attribute outliers, and region outliers; (2) a set of algorithms for the accurate detection of single-attribute spatial outliers; (3) a systematic approach to identifying and tracking region outliers in continuous meteorological data sequences; (4) a novel Mahalanobis-distance-based algorithm to detect outliers with multiple attributes; (5) a set of graph-based algorithms to identify point outliers and region outliers; and (6) extensive analysis of experiments on several spatial data sets (e.g., West Nile virus data and NOAA meteorological data) to evaluate the effectiveness and efficiency of the proposed algorithms.Ph. D.
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Tembe, Waibhav D. "Proximity Metrics for Contextual Pattern Recognition." University of Cincinnati / OhioLINK, 2004. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1096665126.
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Villegas, Santamaría Mauricio. "Contributions to High-Dimensional Pattern Recognition." Doctoral thesis, Universitat Politècnica de València, 2011. http://hdl.handle.net/10251/10939.
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This thesis gathers some contributions to statistical pattern recognition particularly targeted
at problems in which the feature vectors are high-dimensional. Three pattern recognition
scenarios are addressed, namely pattern classification, regression analysis and score fusion.
For each of these, an algorithm for learning a statistical model is presented. In order to
address the difficulty that is encountered when the feature vectors are high-dimensional,
adequate models and objective functions are defined. The strategy of learning simultaneously
a dimensionality reduction function and the pattern recognition model parameters is shown to
be quite effective, making it possible to learn the model without discarding any discriminative
information. Another topic that is addressed in the thesis is the use of tangent vectors as
a way to take better advantage of the available training data. Using this idea, two popular
discriminative dimensionality reduction techniques are shown to be effectively improved. For
each of the algorithms proposed throughout the thesis, several data sets are used to illustrate
the properties and the performance of the approaches. The empirical results show that the
proposed techniques perform considerably well, and furthermore the models learned tend to
be very computationally efficient.Villegas Santamaría, M. (2011). Contributions to High-Dimensional Pattern Recognition [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/10939
Palancia
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Siddique, Nahian A. "PATTERN RECOGNITION IN CLASS IMBALANCED DATASETS." VCU Scholars Compass, 2016. http://scholarscompass.vcu.edu/etd/4480.
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Class imbalanced datasets constitute a significant portion of the machine learning problems of interest, where recognizing the ‘rare class’ is the primary objective for most applications. Traditional linear machine learning algorithms are often not effective in recognizing the rare class. In this research work, a specifically optimized feed-forward artificial neural network (ANN) is proposed and developed to train from moderate to highly imbalanced datasets.
The proposed methodology deals with the difficulty in classification task in multiple stages—by optimizing the training dataset, modifying kernel function to generate the gram matrix and optimizing the NN structure. First, the training dataset is extracted from the available sample set through an iterative process of selective under-sampling. Then, the proposed artificial NN comprises of a kernel function optimizer to specifically enhance class boundaries for imbalanced datasets by conformally transforming the kernel functions. Finally, a single hidden layer weighted neural network structure is proposed to train models from the imbalanced dataset. The proposed NN architecture is derived to effectively classify any binary dataset with even very high imbalance ratio with appropriate parameter tuning and sufficient number of processing elements.
Effectiveness of the proposed method is tested on accuracy based performance metrics, achieving close to and above 90%, with several imbalanced datasets of generic nature and compared with state of the art methods. The proposed model is also used for classification of a 25GB computed tomographic colonography database to test its applicability for big data. Also the effectiveness of under-sampling, kernel optimization for training of the NN model from the modified kernel gram matrix representing the imbalanced data distribution is analyzed experimentally. Computation time analysis shows the feasibility of the system for practical purposes. This report is concluded with discussion of prospect of the developed model and suggestion for further development works in this direction.
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Suliman, Ayman, and Joakim Bäverlind. "Experiments With Four Pattern Recognition Algorithms." Thesis, KTH, Skolan för elektro- och systemteknik (EES), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-214730.
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A big part of computer vision concerns the issue ofhow well images can be classified into their corresponding classes.Image classification is a big part of reducing the gap betweenhuman and AI performance. Images are classified by first usinga dataset of images together with their given classifications totrain the system with machine learning. This can then be usedon images without any classification to test and see how well thealgorithm can classify an unknown image. However, this can bedone in many different ways and methods. The aim of the projectis to compare the performance of four algorithms: MultivariateGaussian Distribution Model, Least Square Regression (LS),Kernel Regression (KR) and Extreme Learning Machine (ELM).By using three different databases, the performance of the fouralgorithms varied. The algorithms were tested by reducing thedimensions of the images it was processing in order to analyzehow well the algorithms worked with different quality of images.The results show that some algorithms work best when moreimages are used for training, while some algorithms strugglewith more images and/or classes. This report concluded that theaccuracy of the algorithms varied depending on the structureof the database. By analyzing the structure of the database,the optimal classification algorithm can be chosen for bestperformance.
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El, Ghawalby Heyayda. "Spectral geometry for structural pattern recognition." Thesis, University of York, 2011. http://etheses.whiterose.ac.uk/1525/.
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Graphs are used pervasively in computer science as representations of data with a network or relational structure, where the graph structure provides a flexible representation such that there is no fixed dimensionality for objects. However, the analysis of data in this form has proved an elusive problem; for instance, it suffers from the robustness to structural noise. One way to circumvent this problem is to embed the nodes of a graph in a vector space and to study the properties of the point distribution that results from the embedding. This is a problem that arises in a number of areas including manifold learning theory and graph-drawing. In this thesis, our first contribution is to investigate the heat kernel embedding as a route to computing geometric characterisations of graphs. The reason for turning to the heat kernel is that it encapsulates information concerning the distribution of path lengths and hence node affinities on the graph. The heat kernel of the graph is found by exponentiating the Laplacian eigensystem over time. The matrix of embedding co-ordinates for the nodes of the graph is obtained by performing a Young-Householder decomposition on the heat kernel. Once the embedding of its nodes is to hand we proceed to characterise a graph in a geometric manner. With the embeddings to hand, we establish a graph characterization based on differential geometry by computing sets of curvatures associated with the graph nodes, edges and triangular faces. The second contribution comes from the need to solve the problem that arise in the processing of a noisy data over a graph. The Principal difficulty of this task, is how to preserve the geometrical structures existing in the initial data. Bringing together several, distinct concepts that have received some independent recent attention in machine learning; we propose a framework to regularize real-valued or vector-valued functions on weighted graphs of arbitrary topology. The first of these is deduced from the concepts of the spectral graph theory that have been applied to a wide range of clustering and classification tasks over the last decades taking in consideration the properties of the graph \(p\)-Laplacian as a nonlinear extension of the usual graph Laplacian. The second one is the geometric point of view comes from the heat kernel embedding of the graph into a manifold. In these techniques we use the geometry of the manifold by assuming that it has the geometric structure of a Riemannian manifold. The third important conceptual framework comes from the manifold regularization which extends the classical framework of regularization in the sense of reproducing Hilbert Spaces to exploit the geometry of the embedded set of points. The proposed framework, based on the \(p\)-Laplacian operators considering minimizing a weighted sum of two energy terms: a regularization one and an additional approximation term which helps to avoid the shrinkage effects obtained during the regularization process. The data are structured by functions depending on data features, the curvature attributes associated with the geometric embedding of the graph. The third contribution is inspired by the concepts and techniques of the graph calculus of partial differential functions. We propose a new approach for embedding graphs on pseudo-Riemannian manifolds based on the wave kernel which is the solution of the wave equation on the edges of a graph. The eigensystem of the wave-kernel is determined by the eigenvalues and the eigenfunctions of the normalized adjacency matrix and can be used to solve the edge-based wave equation. By factorising the Gram-matrix for the wave-kernel, we determine the embedding co-ordinates for nodes under the wave-kernel. The techniques proposed through this thesis are investigated as a means of gauging the similarity of graphs. We experiment on sets of graphs representing the proximity of image features in different views of different objects in three different datasets namely, the York model house, the COIL-20 and the TOY databases.
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Dannenberg, Matthew. "Pattern Recognition in High-Dimensional Data." Scholarship @ Claremont, 2016. https://scholarship.claremont.edu/hmc_theses/76.
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Vast amounts of data are produced all the time. Yet this data does not easily equate to useful information: extracting information from large amounts of high dimensional data is nontrivial. People are simply drowning in data. A recent and growing source of high-dimensional data is hyperspectral imaging. Hyperspectral images allow for massive amounts of spectral information to be contained in a single image. In this thesis, a robust supervised machine learning algorithm is developed to efficiently perform binary object classification on hyperspectral image data by making use of the geometry of Grassmann manifolds. This algorithm can consistently distinguish between a large range of even very similar materials, returning very accurate classification results with very little training data. When distinguishing between dissimilar locations like crop fields and forests, this algorithm consistently classifies more than 95 percent of points correctly. On more similar materials, more than 80 percent of points are classified correctly. This algorithm will allow for very accurate information to be extracted from these large and complicated hyperspectral images.