Dissertations / Theses: 'Pattern recognition systems – Evaluation'

1

Codrescu, Lucian. "An evaluation of the Pica architecture for an object recognition application." Thesis, Georgia Institute of Technology, 1998. http://hdl.handle.net/1853/15483.

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Eakins, John Paul. "Design and evaluation of a shape retrieval system." Thesis, University of Newcastle Upon Tyne, 1990. http://hdl.handle.net/10443/2056.

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While automated storage and retrieval systems for textual and numeric data are now commonplace, the development of analogous systems for pictorial data has lagged behind - not through the lack of need for such systems, but because their development involves a number of significant problems. The aim of this project is to investigate these problems by designing and evaluating an information retrieval system for a specific class of picture, 2-dimensional engineering drawings. This involves consideration of the retrieval capabilities needed by such· a system, what storage structures it would require, how the salient features of each drawing should be represented, how query and stored shapes should be matched, what features were of greatest importance in retrieval, and the interfaces necessary to formulate queries and display results. A form of hierarchical boundary representation has been devised for stored shapes, in which each boundary can be viewed as a series of levels of steadily increasing complexity. A set of rules for boundary and segment ordering ensures that as far as possible, each shape has a unique representation. For each level at which each boundary can be viewed, a set of invariant shape features characterizing that level is extracted and added to the shape representation stored in the database. Two classes of boundary feature have been defmed; global features, characteristic of the boundary as a whole, and local features, corresponding to individual fragments of the boundary. To complete the shape description, position features are also computed and stored, to specify the pattern of inner boundaries within the overall shape. Six different tYPes of shape retrieval have been distinguished, although the prototype system can offer only three of these - exact shape matching, partial shape matching and similarity matching. Complete or incomplete query shapes can be built up at a terminal, and subjected to a feature extraction process similar to that for stored drawings, yielding a query fIle that can be matched against the shape database. A variety of matching techniques is provided, including similarity estimation using global or local features, tests for the existence of specified local features in stored drawings, and cumulative angle vs distance matching between query and stored shape boundaries. Results can be displayed in text or graphical form. The retrieval performance of the system in similarity matching mode has been evaluated by comparing its rankings of shapes retrieved in response to test queries with those obtained by a group of human subjects faced with the same task. Results, expressed as normalized recall and precision, are encouraging, particularly for similarity estimation using either global or local boundary features. While the detailed results are of limited significance until confrrmed with larger test collections, they appear sufficiently promising to warrant the development of a more advanced prototype capable of handling 3-D geometric models. Some design aspects of the system would appear to be applicable to a wider range of pictorial information systems.

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Pettersson, Johan. "Real-time Object Recognition on a GPU." Thesis, Linköping University, Department of Electrical Engineering, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-10238.

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Shape-Based matching (SBM) is a known method for 2D object recognition that is rather robust against illumination variations, noise, clutter and partial occlusion.

The objects to be recognized can be translated, rotated and scaled.

The translation of an object is determined by evaluating a similarity measure for all possible positions (similar to cross correlation).

The similarity measure is based on dot products between normalized gradient directions in edges.

Rotation and scale is determined by evaluating all possible combinations, spanning a huge search space.

A resolution pyramid is used to form a heuristic for the search that then gains real-time performance.

For SBM, a model consisting of normalized edge gradient directions, are constructed for all possible combinations of rotation and scale.

We have avoided this by using (bilinear) interpolation in the search gradient map, which greatly reduces the amount of storage required.

SBM is highly parallelizable by nature and with our suggested improvements it becomes much suited for running on a GPU.

This have been implemented and tested, and the results clearly outperform those of our reference CPU implementation (with magnitudes of hundreds).

It is also very scalable and easily benefits from future devices without effort.

An extensive evaluation material and tools for evaluating object recognition algorithms have been developed and the implementation is evaluated and compared to two commercial 2D object recognition solutions.

The results show that the method is very powerful when dealing with the distortions listed above and competes well with its opponents.

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Orwin, Claire Nicola. "An evaluation of the performance of an optical measurement system for the three-dimensional capture of the shape and dimensions of the human body." Thesis, De Montfort University, 2000. http://hdl.handle.net/2086/4908.

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As the clothing industry moves away from traditional models of mass production there has been increased interest towards customised clothing. The technology to produce cost effective customised clothing is already in place however the prerequisite to customised clothing is accurate body dimensional data. In response, image capture systems have been developed which are capable of recording a three-dimensional image of the body, from which measurements and shape information may be extracted. The use of these systems for customised clothing has, to date, been limited due to issues of inaccuracy, cost and portability. To address the issue of inaccuracy a diagnostic procedure has been developed through the performance evaluation of an image capture system. By systematically evaluating physical and instrumental parameters the more relevant sources of potential error were identified and quantified and subsequently corrected to form a `closed loop' experimental procedure. A systematic test procedure is therefore presented which may be universally applied to image capture systems working on the same principle. The methodology was based upon the isolation and subsequent testing of variables that were thought to be potential sources of error. The process therefore included altering the physical parameters of the target object in relation to the image capture system and amending the configuration and calibration settings within the system. From the evaluation the most relevant sources of error were identified as the cosine effect, measurement point displacement, the dimensional differences between views and the influence of the operator in measurement. The test procedure proved to be effective in both evaluating the performance of the system under investigation and in enabling the quantification of errors. Both random and systematic errors were noted which may be quantified or corrected to enable improved accuracy in the measured results. Recommendations have been made for the improvement of the performance of the current image capture system these include the integration of a cosine effect correction algorithm and suggestions for the automation of the image alignment process. The limitations of the system such as its reliance on manual intervention for both the measurement and stitching processes, are discussed, as is its suitability for providing dimensional information for bespoke clothing production. Recommendations are also made for the creation of an automated test procedure for testing the performance of alternative image capture systems, which involves evaluating the accuracy of object replication both for multiple and single image capture units using calibration objects which combine a range of surfaces.

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Kluever, Kurt Alfred. "Evaluating the usability and security of a video CAPTCHA /." Online version of thesis, 2008. http://hdl.handle.net/1850/7886.

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Avan, Selcuk Kazim. "Feature Set Evaluation For A Generic Missile Detection System." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/2/12608130/index.pdf.

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Missile Detection System (MDS) is one of the main components of a self-protection system developed against the threat of guided missiles for airborne platforms. The requirements such as time critical operation and high accuracy in classification performance make the &lsquo
Pattern Recognition&rsquo
problem of an MDS a hard task. Problem can be defined in two main parts such as &lsquo
Feature Set Evaluation&rsquo
(FSE) and &lsquo
Classifier&rsquo
designs. The main goal of feature set evaluation is to employ a dimensionality reduction process for the input data set, while not disturbing the classification performance in the result. In this thesis study, FSE approaches are investigated for the pattern recognition problem of a generic MDS. First, synthetic data generation is carried out in software environment by employing generic models and assumptions in order to reflect the nature of a realistic problem environment. Then, data sets are evaluated in order to draw a baseline for further feature set evaluation approaches. Further, a theoretical background including the concepts of Class Separability, Feature Selection and Feature Extraction is given. Several widely used methods are assessed in terms of convenience for the problem by giving necessary justifications depending on the data set characteristics. Upon this background, software implementations are performed regarding several feature set evaluation techniques. Simulations are carried out in order to process dimensionality reduction. For the evaluation of the resulting data sets in terms of classification performance, software implementation of a classifier is realized. Resulting classification performances of the applied approaches are compared and evaluated.

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Befus, Chad R., and University of Lethbridge Faculty of Arts and Science. "Design and evaluation of dynamic feature-based segmentation on music." Thesis, Lethbridge, Alta. : University of Lethbridge, Dept. of Mathematics and Computer Science, c2010, 2010. http://hdl.handle.net/10133/2531.

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Segmentation is an indispensable step in the field of Music Information Retrieval (MIR). Segmentation refers to the splitting of a music piece into significant sections. Classically there has been a great deal of attention focused on various issues of segmentation, such as: perceptual segmentation vs. computational segmentation, segmentation evaluations, segmentation algorithms, etc. In this thesis, we conduct a series of perceptual experiments which challenge several of the traditional assumptions with respect to segmentation. Identifying some deficiencies in the current segmentation evaluation methods, we present a novel standardized evaluation approach which considers segmentation as a supportive step towards feature extraction in the MIR process. Furthermore, we propose a simple but effective segmentation algorithm and evaluate it utilizing our evaluation approach.
viii, 94 leaves : ill. ; 29 cm

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Ganapathy, Priya. "Development and Evaluation of a Flexible Framework for the Design of Autonomous Classifier Systems." Wright State University / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=wright1261335392.

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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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Ferreira, Edgar Ricardo. "Procedimentos automáticos para apoio na avaliação de pavimentos com o uso de imagens digitais." Universidade Federal de Viçosa, 2010. http://locus.ufv.br/handle/123456789/819.

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Made available in DSpace on 2015-03-26T12:34:08Z (GMT). No. of bitstreams: 1 texto completo.pdf: 5587368 bytes, checksum: 8953571965e3894399a67c5d550889e4 (MD5) Previous issue date: 2010-02-09
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
Pavements surface distresses are discontinuities on the road which affect the safety and comfort of the user, as well as to possible needs for interventions on the road. The knowledge concerning the pavement layers also works, along with other structural parameters, analyzing further actions on the track. This thesis proposes the use of digital pavement images to detect surface distresses and to find out the pavement layers. This study aims a modern alternative against the current traditional technique of pavement surface defects inspections in Brazil. Firstly, under the spectroradiometer, it was analyzed several patterns relative to the conditions of the asphalt pavement surface in order to define the spectral range that best discriminate against those patterns. By the technique of automatic pattern recognition, it was analyzed the orbital images of high resolution and ground images from the asphalt pavement, using as a rule of decision the algorithm Maximum Likelihood and Artificial Neural Networks. Regarding the features discrimination it applied spectral data to the multispectral images and textural information to the monochrome images. It was worked with a Ground Penetration Radar (GPR) with the purpose of describing the pavement layers determination, thus it acquires the the subsurface profile, resulting in a continuous image of the pavement layers and the automatic classification of images, and in the automatic classification of those images, it was also used textural information to get characteristics as well as the algorithm of the Maximum Likelihood and Artificial Neural Networks as rule of decision. Subsequently, the data obtained in the classification of images, the pavement distresses and its layers in order, set out to ascertain the possibility of using them in a trade Pavement Management System (PMS). The results of classification using orbital images of high resolution were not encouraging, however, when it worked with ground images, the results were surprisingly good, so it indicated a promising possibility in this way. In the classification of the subsurface image pavement, the results were so good so far pretty satisfactory, but lower than the defects classification. Regarding the use of results in the images classification and their use as data in a trade Management System Commercial, it had no major difficulties in this mentioned proceeding.
Defeitos nas superfícies de pavimentos asfálticos são descontinuidades na pista de rolamento que afetam a segurança e o conforto do usuário, além de indicativos da necessidade de intervenções na via. O conhecimento das camadas do pavimento também é utilizado, juntamente com outros parâmetros estruturais, na análise de uma futura intervenção na via. Nesta tese, propõe-se o uso de imagens digitais do pavimento para detectar defeitos superficiais e para identificarr as camadas do pavimento. Quanto aos defeitos superficiais, a motivação é a possibilidade de se obter uma alternativa à técnica de levantamento tradicional de defeitos superficiais em uso no Brasil. Inicialmente, com o auxílio de um espectrorradiômetro, analisaram-se vários padrões correspondentes às condições da superfície do pavimento asfáltico, com o intuito de definir a faixa espectral que melhor discrimine aqueles padrões. Usando-se a técnica de reconhecimento automático de padrões, analisaram-se imagens orbitais de altíssima resolução e terrestre do pavimento asfáltico, empregando como regra de decisão o algoritmo da Máxima Verossimilhança e Redes Neurais Artificiais. Para a fase de discriminação das características, utilizou-se, nas imagens multiespectrais, dados espectrais e nas imagens monocromáticas, informações texturais. Para a determinação das camadas do pavimento usou-se um Ground Penetration Radar (GPR) na aquisição do perfil subsuperficial, obtendo-se uma imagem contínua das camadas do pavimento, e na classificação automática dessas imagens, usou-se, também, informações texturais para extrair características, o algoritmo da Máxima Verossimilhança e Redes Neurais Artificiais como regra de decisão. Posteriormente, com os dados obtidos na classificação sobre as imagens, ou seja, os defeitos superficiais do pavimento e suas camadas, verifica-se a possibilidade de usá-los em um Sistema de Gerência de Pavimentos (SGP) comercial. Os resultados da classificação com o uso de imagem orbital não foram animadores. No entanto, quando se usou imagens terrestres para a classificação, os resultados foram surpreendentemente bons, indicando ser uma possibilidade bastante promissora. Na classificação da imagem subsuperficial do pavimento, os resultados foram razoavelmente satisfatórios, porém, inferiores ao da classificação dos defeitos. Quanto ao uso dos resultados obtidos na classificação das imagens e sua utilização como dados em um Sistema de Gerência de Pavimentos comercial, verificou-se, no sistema comercial analisado, não haver grandes dificuldades neste procedimento.

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11

Gerdes, Mike. "Health Monitoring for Aircraft Systems using Decision Trees and Genetic Evolution." Diss., Aircraft Design and Systems Group (AERO), Department of Automotive and Aeronautical Engineering, Hamburg University of Applied Sciences, 2019. http://d-nb.info/1202830382.

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Reducing unscheduled maintenance is important for aircraft operators. There are significant costs if flights must be delayed or cancelled, for example, if spares are not available and have to be shipped across the world. This thesis describes three methods of aircraft health condition monitoring and prediction; one for system monitoring, one for forecasting and one combining the two other methods for a complete monitoring and prediction process. Together, the three methods allow organizations to forecast possible failures. The first two use decision trees for decision-making and genetic optimization to improve the performance of the decision trees and to reduce the need for human interaction. Decision trees have several advantages: the generated code is quickly and easily processed, it can be altered by human experts without much work, it is readable by humans, and it requires few resources for learning and evaluation. The readability and the ability to modify the results are especially important; special knowledge can be gained and errors produced by the automated code generation can be removed. A large number of data sets is needed for meaningful predictions. This thesis uses two data sources: first, data from existing aircraft sensors, and second, sound and vibration data from additionally installed sensors. It draws on methods from the field of big data and machine learning to analyse and prepare the data sets for the prediction process.

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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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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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Ruta, Dymitr. "Classifier diversity in combined pattern recognition systems." Thesis, University of the West of Scotland, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.398320.

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15

Lee, Yuchun. "Classifiers : adaptive modules in pattern recognition systems." Thesis, Massachusetts Institute of Technology, 1989. http://hdl.handle.net/1721.1/14496.

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16

Yao, Xiaoqiang. "Pattern-recognition scheduling." Ohio : Ohio University, 1996. http://www.ohiolink.edu/etd/view.cgi?ohiou1177698616.

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Cavalin, Paulo Rodrigo. "Adaptive systems for hidden Markov model-based pattern recognition systems." Mémoire, École de technologie supérieure, 2011. http://espace.etsmtl.ca/976/1/CAVALIN_Paulo_Rodrigo.pdf.

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Cette thèse porte sur l’étude des systèmes adaptatifs pour la reconnaissance de formes. Habituellement les systèmes de reconnaissance reposent sur une connaissance statique du problème à résoudre et cela pour la durée de vie du système. Cependant il y a des circonstances où la connaissance du problème est partielle lors de l’apprentissage initial à l’étape de la conception. Pour cette raison, les systèmes de classification adaptatifs de nouvelle génération permettent au système de base de s’adapter à la fois en apprenant sur les nouvelles données et sont également capables de s’adapter à l’environnement lors de la généralisation. Cette thèse propose une nouvelle définition d’un système de reconnaissance adaptatif où les MMCs (Modèles de Markov Cachés) sont considérés comme étude de cas. La première partie de la thèse présente une évaluation des principaux algorithmes d’apprentissage incrémental utilisés pour l’estimation des paramètres des MMCs. L’objectif de cette étude est de dégager les stratégies d’apprentissage incrémental dont la performance en généralisation se rapproche de cette obtenue avec un apprentissage hors-ligne (batch). Les résultats obtenus sur le problème de la reconnaissance de chiffres et de lettres manuscrits montrent la supériorité des approches basées sur les ensembles de modèles. De plus, nous avons montré l’importance de conserver dans une mémoire à court terme des exemples utilisés en validation, ce qui permet d’obtenir un niveau de performance qui peut même dépasser celui obtenu en mode batch. La deuxième partie de cette thèse est consacrée à la formulation d’une nouvelle approche pour la sélection dynamique des ensembles de classifieurs. Inspiré du concept de fusion appelé « organisation multi-niveau » (multistage organizations), nous avons formulé une variante de ce concept appelé DMO (dynamic multistage organization - DMO) qui permet d’adapter la fonction de fusion dynamiquement pour chaque exemple de test à classer. De plus, le concept DMO a été intégré à la méthode DSA proposée par Dos Santos et al pour la sélection dynamique d’ensembles de classifieurs. Ainsi, deux nouvelles variantes, DSAm et DSAc, ont été proposées et évaluées. Dans le premier cas (DSAm), plusieurs fonctions de sélection permettent une généralisation de la structure DMO. Pour ce qui est de la variante DSAc, nous utilisons l’information contextuelle (représentée par les profils de décisions des classifieurs de base) acquise par le système et qui est associée à la base de validation conservée dans une mémoire à court terme. L’évaluation des deux approches sur des bases de données de petite et de grande échelle ont montré que la méthode DSAc domine DSAm sur la plupart des cas étudiés. Ce résultat montre que l’utilisation d’informations contextuelles permet une meilleure performance en généralisation comparées aux méthodes non informées. Une propriété importante de l’approche DSAc est qu’elle peut également servir pour apprendre de nouvelles données dans le temps, une propriété très importante pour la conception de systèmes de reconnaissance adaptatifs dans les environnements dynamiques caractérisés par un niveau important d’incertitude sur le problème à résoudre. Finalement, un nouveau framework appelé LoGID (Local and Global Incremental Learning for Dynamic Selection) est proposé pour la conception d’un système de reconnaissance adaptatif basé sur les MMC, et capable de s’adapter dans le temps durant les phases d’apprentissage de généralisation. Le système est composé d’un pool de classifieurs de base et l’adaptation durant la phase de généralisation est effectuée par la sélection dynamique des membres du pool les plus compétents pour classer chaque exemple de test. Le mécanisme de sélection dynamique est basé sur l’algorithme des K plus proches vecteurs de décision, tandis que l’adaptation durant la phase d’apprentissage consiste à la mise à jour et à l’ajout de classifieurs de base dans le système. Durant la phase d’apprentissage, deux stratégies sont proposées pour apprendre incrémentalement sur des nouvelles données: l’apprentissage local et l’apprentissage global. L’apprentissage incrémentale local implique la mise à jour du pool de classifieurs de base en ajoutant des nouveaux membres à cet ensemble. Les nouveaux membres sont générés avec l’algorithme Learn++. L’apprentissage incrémental global consiste à la mise à jour de la base de connaissances composée des vecteurs de décisions qui seront utilisés en généralisation pour la sélection dynamique des membres les plus compétents. Le système LoGID a été validé sur plusieurs bases de données et les résultats comparés à ceux publiés dans la littérature. En général, la méthode proposée domine les autres méthodes incluant les méthodes d’apprentissage hors-ligne. Enfin, le système LoGID évalué en mode adaptatif montre qu’il est en mesure d’apprendre de nouvelles connaissances dans le temps au moment où les nouvelles données sont disponibles. Cette faculté d’adaptation est très importante également lorsque les données disponibles pour l’apprentissage sont peu nombreuses.

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BruÌˆckner, JoÌˆrg. "Automatic pattern recognition and learning for information systems." Thesis, University of Sussex, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.262632.

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Schwier, Jason Montgomery. "Pattern recognition for command and control data systems." Connect to this title online, 2009. http://etd.lib.clemson.edu/documents/1252424695/.

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Xiao, Xiangye. "Efficient co-location pattern discovery /." View abstract or full-text, 2009. http://library.ust.hk/cgi/db/thesis.pl?CSED%202009%20XIAOX.

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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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Argialas, Demetre P. "A structural approach towards drainage pattern recognition /." The Ohio State University, 1985. http://rave.ohiolink.edu/etdc/view?acc_num=osu148726155305775.

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23

Leu, Cheng-Hua. "Evaluation of Spatially-Distributed Snowpack Estimation Using Pattern Recognition." DigitalCommons@USU, 1988. https://digitalcommons.usu.edu/etd/4847.

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A procedure for spatially-distributed snowpack estimate is proposed and evaluated herein. This procedure is divided into two parts, off-line and on-line parts. The off-line procedure uses Fuzzy c-Varieties (FCV) algorithms to perform the watershed classification based on Landsat TM data and a digital elevation model and estimate influence coefficient for all pixels in the watershed. Through the concept of influence coefficient, a set of prototypical points can be selected and located. The on-line procedure estimates snowpack properties at these prototypical points and then transforms them to estimates of snowpack properties at any point in the watershed. To effectively evaluate this new spatially-distributed snowpack estimation procedure, a set of evaluation issues were identified and evaluated. Through the evaluation of these issues, the proposed procedure was found to work quite successfully in estimating the spatial distribution of snowpack properties.

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YOUSSIF, ROSHDY S. "HYBRID INTELLIGENT SYSTEMS FOR PATTERN RECOGNITION AND SIGNAL PROCESSING." University of Cincinnati / OhioLINK, 2004. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1085714219.

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25

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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Chen, Dapeng. "Pattern recognition in software engineering trend adapting." Morgantown, W. Va. : [West Virginia University Libraries], 2001. http://etd.wvu.edu/templates/showETD.cfm?recnum=1892.

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Thesis (M.S.)--West Virginia University, 2001.
Title from document title page. Document formatted into pages; contains iii, 51 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 50-51).

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Tanawongsuwan, Rawesak. "Impact of speed variations in gait recognition." Diss., Available online, Georgia Institute of Technology, 2004:, 2003. http://etd.gatech.edu/theses/available/etd-04072004-180217/unrestricted/tanawongsuwan%5Frawesak%5F200312%5Fphd.pdf.

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Marsden, Christopher J. "Nonlinear dynamics of pattern recognition and optimization." Thesis, Loughborough University, 2012. https://dspace.lboro.ac.uk/2134/10694.

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We associate learning in living systems with the shaping of the velocity vector field of a dynamical system in response to external, generally random, stimuli. We consider various approaches to implement a system that is able to adapt the whole vector field, rather than just parts of it - a drawback of the most common current learning systems: artificial neural networks. This leads us to propose the mathematical concept of self-shaping dynamical systems. To begin, there is an empty phase space with no attractors, and thus a zero velocity vector field. Upon receiving the random stimulus, the vector field deforms and eventually becomes smooth and deterministic, despite the random nature of the applied force, while the phase space develops various geometrical objects. We consider the simplest of these - gradient self-shaping systems, whose vector field is the gradient of some energy function, which under certain conditions develops into the multi-dimensional probability density distribution of the input. We explain how self-shaping systems are relevant to artificial neural networks. Firstly, we show that they can potentially perform pattern recognition tasks typically implemented by Hopfield neural networks, but without any supervision and on-line, and without developing spurious minima in the phase space. Secondly, they can reconstruct the probability density distribution of input signals, like probabilistic neural networks, but without the need for new training patterns to have to enter the network as new hardware units. We therefore regard self-shaping systems as a generalisation of the neural network concept, achieved by abandoning the "rigid units - flexible couplings'' paradigm and making the vector field fully flexible and amenable to external force. It is not clear how such systems could be implemented in hardware, and so this new concept presents an engineering challenge. It could also become an alternative paradigm for the modelling of both living and learning systems. Mathematically it is interesting to find how a self shaping system could develop non-trivial objects in the phase space such as periodic orbits or chaotic attractors. We investigate how a delayed vector field could form such objects. We show that this method produces chaos in a class systems which have very simple dynamics in the non-delayed case. We also demonstrate the coexistence of bounded and unbounded solutions dependent on the initial conditions and the value of the delay. Finally, we speculate about how such a method could be used in global optimization.

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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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Azar, Jimmy. "Automated Tissue Image Analysis Using Pattern Recognition." Doctoral thesis, Uppsala universitet, Bildanalys och människa-datorinteraktion, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-231039.

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Automated tissue image analysis aims to develop algorithms for a variety of histological applications. This has important implications in the diagnostic grading of cancer such as in breast and prostate tissue, as well as in the quantification of prognostic and predictive biomarkers that may help assess the risk of recurrence and the responsiveness of tumors to endocrine therapy. In this thesis, we use pattern recognition and image analysis techniques to solve several problems relating to histopathology and immunohistochemistry applications. In particular, we present a new method for the detection and localization of tissue microarray cores in an automated manner and compare it against conventional approaches. We also present an unsupervised method for color decomposition based on modeling the image formation process while taking into account acquisition noise. The method is unsupervised and is able to overcome the limitation of specifying absorption spectra for the stains that require separation. This is done by estimating reference colors through fitting a Gaussian mixture model trained using expectation-maximization. Another important factor in histopathology is the choice of stain, though it often goes unnoticed. Stain color combinations determine the extent of overlap between chromaticity clusters in color space, and this intrinsic overlap sets a main limitation on the performance of classification methods, regardless of their nature or complexity. In this thesis, we present a framework for optimizing the selection of histological stains in a manner that is aligned with the final objective of automation, rather than visual analysis. Immunohistochemistry can facilitate the quantification of biomarkers such as estrogen, progesterone, and the human epidermal growth factor 2 receptors, in addition to Ki-67 proteins that are associated with cell growth and proliferation. As an application, we propose a method for the identification of paired antibodies based on correlating probability maps of immunostaining patterns across adjacent tissue sections. Finally, we present a new feature descriptor for characterizing glandular structure and tissue architecture, which form an important component of Gleason and tubule-based Elston grading. The method is based on defining shape-preserving, neighborhood annuli around lumen regions and gathering quantitative and spatial data concerning the various tissue-types.

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Mansjur, Dwi Sianto. "Statistical pattern recognition approaches for retrieval-based machine translation systems." Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/42821.

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This dissertation addresses the problem of Machine Translation (MT), which is defined as an automated translation of a document written in one language (the source language) to another (the target language) by a computer. The MT task requires various types of knowledge of both the source and target language, e.g., linguistic rules and linguistic exceptions. Traditional MT systems rely on an extensive parsing strategy to decode the linguistic rules and use a knowledge base to encode those linguistic exceptions. However, the construction of the knowledge base becomes an issue as the translation system grows. To overcome this difficulty, real translation examples are used instead of a manually-crafted knowledge base. This design strategy is known as the Example-Based Machine Translation (EBMT) principle. Traditional EBMT systems utilize a database of word or phrase translation pairs. The main challenge of this approach is the difficulty of combining the word or phrase translation units into a meaningful and fluent target text. A novel Retrieval-Based Machine Translation (RBMT) system, which uses a sentence-level translation unit, is proposed in this study. An advantage of using the sentence-level translation unit is that the boundary of a sentence is explicitly defined and the semantic, or meaning, is precise in both the source and target language. The main challenge of using a sentential translation unit is the limited coverage, i.e., the difficulty of finding an exact match between a user query and sentences in the source database. Using an electronic dictionary and a topic modeling procedure, we develop a procedure to obtain clusters of sensible variations for each example in the source database. The coverage of our MT system improves because an input query text is matched against a cluster of sensible variations of translation examples instead of being matched against an original source example. In addition, pattern recognition techniques are used to improve the matching procedure, i.e., the design of optimal pattern classifiers and the incorporation of subjective judgments. A high performance statistical pattern classifier is used to identify the target sentences from an input query sentence in our MT system. The proposed classifier is different from the conventional classifier in terms of the way it addresses the generalization capability. A conventional classifier addresses the generalization issue using the parsimony principle and may encounter the possibility of choosing an oversimplified statistical model. The proposed classifier directly addresses the generalization issue in terms of training (empirical) data. Our classifier is expected to generalize better than the conventional classifiers because our classifier is less likely to use over-simplified statistical models based on the available training data. We further improve the matching procedure by the incorporation of subjective judgments. We formulate a novel cost function that combines subjective judgments and the degree of matching between translation examples and an input query. In addition, we provide an optimization strategy for the novel cost function so that the statistical model can be optimized according to the subjective judgments.

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Kani, Bijan. "Enhanced logical adaptive systems for image processing and pattern recognition." Thesis, Brunel University, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.358406.

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Gonzalez, Adrian. "Spatial pattern recognition for crop-livestock systems using multispectral data." Thesis, University of Edinburgh, 2008. http://hdl.handle.net/1842/3790.

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Within the field of pattern recognition (PR) a very active area is the clustering and classification of multispectral data, which basically aims to allocate the right class of ground category to a reflectance or radiance signal. Generally, the problem complexity is related to the incorporation of spatial characteristics that are complementary to the nonlinearities of land surface process heterogeneity, remote sensing effects and multispectral features. The present research describes the application of learning machine methods to accomplish the above task by inducting a relationship between the spectral response of farms’ land cover, and their farming system typology from a representative set of instances. Such methodologies are not traditionally used in crop-livestock studies. Nevertheless, this study shows that its application leads to simple and theoretically robust classification models. The study has covered the following phases: a)geovisualization of crop-livestock systems; b)feature extraction of both multispectral and attributive data and; c)supervised farm classification. The first is a complementary methodology to represent the spatial feature intensity of farming systems in the geographical space. The second belongs to the unsupervised learning field, which mainly involves the appropriate description of input data in a lower dimensional space. The last is a method based on statistical learning theory, which has been successfully applied to supervised classification problems and to generate models described by implicit functions. In this research the performance of various kernel methods applied to the representation and classification of crop-livestock systems described by multispectral response is studied and compared. The data from those systems include linear and nonlinearly separable groups that were labelled using multidimensional attributive data. Geovisualization findings show the existence of two well-defined farm populations within the whole study area; and three subgroups in relation to the Guarico section. The existence of these groups was confirmed by both hierarchical and kernel clustering methods, and crop-livestock systems instances were segmented and labeled into farm typologies based on: a)milk and meat production; b)reproductive management; c)stocking rate; and d)crop-forage-forest land use. The minimum set of labeled examples to properly train the kernel machine was 20 instances. Models inducted by training data sets using kernel machines were in general terms better than those from hierarchical clustering methodologies. However, the size of the training data set represents one of the main difficulties to be overcome in permitting the more general application of this technique in farming system studies. These results attain important implications for large scale monitoring of crop-livestock system; particularly to the establishment of balanced policy decision, intervention plans formulation, and a proper description of target typologies to enable investment efforts to be more focused at local issues.

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He, Yu. "Statistical mechanics of cellular automata and related dynamical systems." Connect to resource, 1986. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1173062557.

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Ni, Zhibo, and 倪志博. "Artificial training samples for the improvement of pattern recognitionsystems." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2012. http://hub.hku.hk/bib/B47849642.

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Pattern recognition is the assignment of some sort of label to a given input value or instance, according to some specific learning algorithm. The recognition performance is directly linked with the quality and size of the training data. However, in many real pattern recognition implementations, it is difficult or not so convenient to collect as many samples as possible for training up the classifier, such as face recognition or Chinese character recognition. In view of the shortage of training samples, the main object of our research is to investigate the generation and use of artificial samples for improving the recognition performance. Besides enhancing the learning, artificial samples are also used in a novel way such that a conventional Chinese character recognizer can read half or combined Chinese character segments. It greatly simplifies the segmentation procedure as well as reduces the error introduced by segmentation. Two novel generation models have been developed to evaluate the effectiveness of supplementing artificial samples in the training. One model generates artificial faces with various facial expressions or lighting conditions by morphing and warping two given sample faces. We tested our face generation model in three popular 2D face databases, which contain both gray scale and color images. Experiments show the generated faces look quite natural and they improve the recognition rates by a large margin. The other model uses stroke and radical information to build new Chinese characters. Artificial Chinese characters are produced by Bezier curves passing through some specified points. This model is more flexible in generating artificial handwritten characters than merely distorting the genuine real samples, with both stroke level and radical level variations. Another feature of this character generation model is that it does not require any real handwritten character sample at hand. In other words, we can train the conventional character classifier and perform character recognition tasks without collecting handwritten samples. Experiment results have validated its possibility and the recognition rate is still acceptable. Besides tackling the small sample size problem in face recognition and isolated character recognition, we improve the performance of bank check legal amount recognizer by proposing character segments recognition and applying Hidden Markov Model (HMM). It is hoped that this thesis can provide some insights for future researches in artificial sample generation, face morphing, Chinese character segmentation and text recognition or some other related issues.
published_or_final_version
Electrical and Electronic Engineering
Doctoral
Doctor of Philosophy

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Pershits, Edward. "Recognition of Face Images." Thesis, University of North Texas, 1994. https://digital.library.unt.edu/ark:/67531/metadc277785/.

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The focus of this dissertation is a methodology that enables computer systems to classify different up-front images of human faces as belonging to one of the individuals to which the system has been exposed previously. The images can present variance in size, location of the face, orientation, facial expressions, and overall illumination. The approach to the problem taken in this dissertation can be classified as analytic as the shapes of individual features of human faces are examined separately, as opposed to holistic approaches to face recognition. The outline of the features is used to construct signature functions. These functions are then magnitude-, period-, and phase-normalized to form a translation-, size-, and rotation-invariant representation of the features. Vectors of a limited number of the Fourier decomposition coefficients of these functions are taken to form the feature vectors representing the features in the corresponding vector space. With this approach no computation is necessary to enforce the translational, size, and rotational invariance at the stage of recognition thus reducing the problem of recognition to the k-dimensional clustering problem. A recognizer is specified that can reliably classify the vectors of the feature space into object classes. The recognizer made use of the following principle: a trial vector is classified into a class with the greatest number of closest vectors (in the sense of the Euclidean distance) among all vectors representing the same feature in the database of known individuals. A system based on this methodology is implemented and tried on a set of 50 pictures of 10 individuals (5 pictures per individual). The recognition rate is comparable to that of most recent results in the area of face recognition. The methodology presented in this dissertation is also applicable to any problem of pattern recognition where patterns can be represented as a collection of black shapes on the white background.

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Wong, Pak-kwong. "Multifont printed Chinese character recognition system /." [Hong Kong : University of Hong Kong], 1991. http://sunzi.lib.hku.hk/hkuto/record.jsp?B13068556.

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Bairaktaris, Dimitrios. "Adaptive pattern recognition in a real-world environment." Thesis, University of St Andrews, 1991. http://hdl.handle.net/10023/9261.

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This thesis introduces and explores the notion of a real-world environment with respect to adaptive pattern recognition and neural network systems. It then examines the individual properties of a real-world environment and proposes Continuous Adaptation, Persistence of information and Context-sensitive recognition to be the major design criteria a neural network system in a real-world environment should satisfy. Based on these criteria, it then assesses the performance of Hopfield networks and Associative Memory systems and identifies their operational limitations. This leads to the introduction of Randomized Internal Representations, a novel class of neural network systems which stores information in a fully distributed way yet is capable of encoding and utilizing context. It then assesses the performance of Competitive Learning and Adaptive Resonance Theory systems and again having identified their operational weakness, it describes the Dynamic Adaptation Scheme which satisfies all three design criteria for a real-world environment.

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Palancioglu, Haci Mustafa. "Extracting Movement Patterns Using Fuzzy and Neuro-fuzzy Approaches." Fogler Library, University of Maine, 2003. http://www.library.umaine.edu/theses/pdf/PalanciogluHM2003.pdf.

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Sun, Bowen. "Named entity recognition : Evaluation of Existing Systems." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for datateknikk og informasjonsvitenskap, 2010. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-11223.

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Nowadays, one subfield of information extraction, Named Entity Recognition, becomes more and more important. It helps machine to recognize proper nouns (entities) in text and associating them with the appropriate types. Common types in NER systems are location, person name, date, address, etc. There are several NER systems in the world. Whats the main core technology of these systems? Which kind of system is better? How to improve this technology in the future? This master thesis will show the basic and detail knowledge about NER.Three existing NER systems will be choose to evaluate in this paper, GATE, CRFClassifier and LbjNerTagger. These systems are based different NER technology. They can stand for the most of NER existing systems in the world now. This paper will present and evaluate these three systems and try to find the advantage and disadvantage of each system.

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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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Atukorale, Don Ajantha Sanjeewa. "A hybrid learning system with a hierarchical architecture for pattern classification /." [St. Lucia, Qld.], 2002. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe16886.pdf.

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Li, Duwang. "Invariant pattern recognition algorithm using the Hough Transform." PDXScholar, 1989. https://pdxscholar.library.pdx.edu/open_access_etds/3899.

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A new algorithm is proposed which uses the Hough Transform to recognize two dimensional objects independent of their orientations, sizes and locations. The binary image of an object is represented by a set of straight lines. Features of the straight lines, namely the lengths and the angles of their normals, their lengths and the end point positions are extracted using the Hough Transform. A data structure for the extracted lines is constructed so that it is efficient to match the features of the lines of one object to those of another object, and determine if one object is a rotated and/or scaled version of the other. Finally a generalized Hough Transform is used to match the end points of the two sets of lines. The simulation experiments show good results for objects with significant linear features .

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Wanderley, Juliana Fernandes Camapum. "Colour-based recognition for remote sensing in environmental systems." Thesis, Coventry University, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.266844.

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Tabassian, Mahdi <1984&gt. "Developing Ultrasound-Based Computer-Aided Diagnostic Systems Through Statistical Pattern Recognition." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2016. http://amsdottorato.unibo.it/7635/.

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Computer-aided diagnosis (CAD) is the use of a computer software to help physicians having a better interpretation of medical images. CAD systems can be viewed as pattern recognition algorithms that identify suspicious signs on a medical image and complement physicians' judgments, by reducing inter-/intra-observer variability and subjectivity. The proposed CAD systems in this thesis have been designed based on the statistical approach to pattern recognition as the most successfully used technique in practice. The main focus of this thesis has been on designing (new) feature extraction and classification algorithms for ultrasound-based CAD purposes. Ultrasound imaging has a broad range of usage in medical applications because it is a safe device which does not use harmful ionizing radiations, it provides clinicians with real-time images, it is portable and relatively cheap. The thesis was concerned with developing new ultrasound-based systems for the diagnosis of prostate cancer (PCa) and myocardial infarction (MI) where these issues have been addressed in two separate parts. In the first part, 1) a new CAD system was designed for prostate cancer biopsy by focusing on handling uncertainties in labels of the ground truth data, 2) the appropriateness of the independent component analysis (ICA) method for learning features from radiofrequency (RF) signals, backscattered from prostate tissues, was examined and, 3) a new ensemble scheme for learning ICA dictionaries from RF signals, backscattered from a tissue mimicking phantom, was proposed. In the second part, 1) principal component analysis (PCA) was used for the statistical modeling of the temporal deformation patterns of the left ventricle (LV) to detect abnormalities in its regional function, 2) a spatio-temporal representation of LV function based on PCA parameters was proposed to detect MI and, 3) a local-to-global statistical shape model based on PCA was presented to detect MI.

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Beeravolu, Nagendrakumar. "Pattern Recognition of Power Systems Voltage Stability Using Real Time Simulations." ScholarWorks@UNO, 2010. http://scholarworks.uno.edu/td/1279.

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The basic idea deals with detecting the voltage collapse ahead of time to provide the operators a lead time for remedial actions and for possible prevention of blackouts. To detect cases of voltage collapse, we shall create methods using pattern recognition in conjunction with real time simulation of case studies and shall develop heuristic methods for separating voltage stable cases from voltage unstable cases that result in response to system contingencies and faults. Using Real Time Simulator in Entergy-UNO Power & Energy Research Laboratory, we shall simulate several contingencies on IEEE 39-Bus Test System and compile the results in two categories of stable and unstable voltage cases. The second stage of the proposed work mainly deals with the study of different patterns of voltage using artificial neural networks. The final stage deals with the training of the controllers in order to detect stability of power system in advance.

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Alphonso, Issac John. "Network training for continuous speech recognition." Master's thesis, Mississippi State : Mississippi State University, 2003. http://library.msstate.edu/etd/show.asp?etd=etd-10252003-105104.

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黃業新 and Yip-san Wong. "A two-level model-based object recognition technique." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1995. http://hub.hku.hk/bib/B31213807.

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Wong, Yip-san. "A two-level model-based object recognition technique /." Hong Kong : University of Hong Kong, 1995. http://sunzi.lib.hku.hk/hkuto/record.jsp?B14705552.

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Confer, William James. "The activity metric for low resource, on-line character recognition." Auburn, Ala., 2005. http://repo.lib.auburn.edu/2005%20Fall/Dissertation/CONFER_WILLIAM_33.pdf.

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Dissertations / Theses on the topic 'Pattern recognition systems – Evaluation'

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