Tesis sobre el tema "Pattern recognition applications"
Crea una cita precisa en los estilos APA, MLA, Chicago, Harvard y otros
Consulte los 50 mejores tesis para su investigación sobre el tema "Pattern recognition applications".
Junto a cada fuente en la lista de referencias hay un botón "Agregar a la bibliografía". Pulsa este botón, y generaremos automáticamente la referencia bibliográfica para la obra elegida en el estilo de cita que necesites: APA, MLA, Harvard, Vancouver, Chicago, etc.
También puede descargar el texto completo de la publicación académica en formato pdf y leer en línea su resumen siempre que esté disponible en los metadatos.
Explore tesis sobre una amplia variedad de disciplinas y organice su bibliografía correctamente.
Thompson, J. R. "Applications of pattern recognition in medicine". Thesis, Open University, 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.377939.
Texto completoRobinson, 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.
Texto completoPAOLANTI, MARINA. "Pattern Recognition for challenging Computer Vision Applications". Doctoral thesis, Università Politecnica delle Marche, 2018. http://hdl.handle.net/11566/252904.
Texto completoPattern Recognition is the study of how machines can observe the environment, learn to distinguish patterns of interest from their background, and make sound and reasonable decisions about the patterns categories. Nowadays, the application of Pattern Recognition algorithms and techniques is ubiquitous and transversal. With the recent advances in computer vision, we now have the ability to mine such massive visual data to obtain valuable insight about what is happening in the world. The availability of affordable and high resolution sensors (e.g., RGB-D cameras, microphones and scanners) and data sharing have resulted in huge repositories of digitized documents (text, speech, image and video). Starting from such a premise, this thesis addresses the topic of developing next generation Pattern Recognition systems for real applications such as Biology, Retail, Surveillance, Social Media Intelligence and Digital Cultural Heritage. The main goal is to develop computer vision applications in which Pattern Recognition is the key core in their design, starting from general methods, that can be exploited in more fields, and then passing to methods and techniques addressing specific problems. The privileged focus is on up-to-date applications of Pattern Recognition techniques to real-world problems, and on interdisciplinary research, experimental and/or theoretical studies yielding new insights that advance Pattern Recognition methods. The final ambition is to spur new research lines, especially within interdisciplinary research scenarios. Faced with many types of data, such as images, biological data and trajectories, a key difficulty was to nd relevant vectorial representations. While this problem had been often handled in an ad-hoc way by domain experts, it has proved useful to learn these representations directly from data, and Machine Learning algorithms, statistical methods and Deep Learning techniques have been particularly successful. The representations are then based on compositions of simple parameterized processing units, the depth coming from the large number of such compositions. It was desirable to develop new, efficient data representation or feature learning/indexing techniques, which can achieve promising performance in the related tasks. The overarching goal of this work consists of presenting a pipeline to select the model that best explains the given observations; nevertheless, it does not prioritize in memory and time complexity when matching models to observations. For the Pattern Recognition system design, the following steps are performed: data collection, features extraction, tailored learning approach and comparative analysis and assessment. The proposed applications open up a wealth of novel and important opportunities for the machine vision community. The newly dataset collected as well as the complex areas taken into exam, make the research challenging. In fact, it is crucial to evaluate the performance of state of the art methods to demonstrate their strength and weakness and help identify future research for designing more robust algorithms. For comprehensive performance evaluation, it is of great importance developing a library and benchmark to gauge the state of the art because the methods design that are tuned to a specic problem do not work properly on other problems. Furthermore, the dataset selection is needed from different application domains in order to offer the user the opportunity to prove the broad validity of methods. Intensive attention has been drawn to the exploration of tailored learning models and algorithms, and their extension to more application areas. The tailored methods, adopted for the development of the proposed applications, have shown to be capable of extracting complex statistical features and efficiently learning their representations, allowing it to generalize well across a wide variety of computer vision tasks, including image classication, text recognition and so on.
Hayes, William S. "Pattern recognition and signal detection in gene finding". Diss., Georgia Institute of Technology, 1998. http://hdl.handle.net/1853/25420.
Texto completoPrendergast, David Jeremy. "Applications of statistical pattern recognition in medical imaging". Thesis, University of Manchester, 1993. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.629772.
Texto completoEvans, 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.
Texto completoYan, Wing-fai. "Eye movement measurement for clinical applications using pattern recognition /". [Hong Kong : University of Hong Kong], 1988. http://sunzi.lib.hku.hk/hkuto/record.jsp?B12434024.
Texto completoMa, Chengyuan. "A detection-based pattern recognition framework and its applications". Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/33889.
Texto completo甄榮輝 y Wing-fai Yan. "Eye movement measurement for clinical applications using pattern recognition". Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1988. http://hub.hku.hk/bib/B31209026.
Texto completoLopez-Bonilla, Roman Ernesto. "Object recognition in three-dimensions for robotic applications". Thesis, University of Bradford, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.305752.
Texto completoChen, Guangyi. "Applications of wavelet transforms in pattern recognition and de-noising". Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape7/PQDD_0006/MQ43552.pdf.
Texto completoXu, Yun. "Chemometrics pattern recognition with applications to genetic and metabolomics data". Thesis, University of Bristol, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.435733.
Texto completoOtte, Sebastian [Verfasser]. "Recurrent Neural Networks for Sequential Pattern Recognition Applications / Sebastian Otte". München : Verlag Dr. Hut, 2017. http://d-nb.info/1149579382/34.
Texto completoWu, Jianfei. "Vector-Item Pattern Mining Algorithms and their Applications". Diss., North Dakota State University, 2011. https://hdl.handle.net/10365/28841.
Texto completoAgaiby, Hany. "Word boundary detection for engineering applications". Thesis, University of the West of Scotland, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.265933.
Texto completoHui, Colin Chiu Wing. "VLSI architectures for digital television applications". Thesis, Queen's University Belfast, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.387928.
Texto completoVia, Cinzia Da. "Semiconductor pixel detectors for imaging applications". Thesis, University of Glasgow, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.362937.
Texto completoStacey, Duncan T. B. "Advances and applications in broadband imaging microspectroscopy". Thesis, University of Liverpool, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.386823.
Texto completoMa, Liying. "Constructive neural networks with applications to image compression and pattern recognition". Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp05/NQ63990.pdf.
Texto completoRohen, V. E. "Applications of statistical pattern recognition techniques to the analysis of ballistocardiograms". Thesis, University of Cambridge, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.235284.
Texto completoChatterjee, Shiladitya. "Applications of Pattern Recognition Entropy (PRE) and Informatics to Data Analysis". BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/8826.
Texto completoMa, Jinhua. "Dependency modeling for information fusion with applications in visual recognition". HKBU Institutional Repository, 2013. https://repository.hkbu.edu.hk/etd_ra/1522.
Texto completoBulas, Cruz Jose Afonso Moreno. "Image processing applications using a transputer-based system". Thesis, University of Bristol, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.294371.
Texto completoCanuto, Anne Magaly de Paula. "Combining neural networks and fuzzy logic for applications in character recognition". Thesis, University of Kent, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.344107.
Texto completoMurnion, Shane D. "Neural and genetic algorithm applications in GIS and remote sensing". Thesis, Queen's University Belfast, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.337024.
Texto completoThomson, Andrew Richard. "A CAM-based processor array for real-time image applications". Thesis, University of Bristol, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.386072.
Texto completoBalakrishnan, Sreeram Viswanath. "Solving combinatorial optimization problems using neural networks with applications in speech recognition". Thesis, University of Cambridge, 1992. https://www.repository.cam.ac.uk/handle/1810/283679.
Texto completoKundakcioglu, O. Erhun. "Combinatorial and nonlinear optimization techniques in pattern recognition with applications in healthcare". [Gainesville, Fla.] : University of Florida, 2009. http://purl.fcla.edu/fcla/etd/UFE0024768.
Texto completoKwok, Kwok Sai. "Algorithms for image segmentation and their applications to video signal processing". Thesis, Imperial College London, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.244298.
Texto completoTravis, Clive Hathaway. "The inverse problem and applications to optical and eddy current imaging". Thesis, University of Surrey, 1989. http://epubs.surrey.ac.uk/804869/.
Texto completoTivive, Fok Hing Chi. "A new class of convolutional neural networks based on shunting inhibition with applications to visual pattern recognition". Access electronically, 2006. http://www.library.uow.edu.au/adt-NWU/public/adt-NWU20061025.164437/index.html.
Texto completoBoussakta, Said. "Algorithms and development of the number theoretic and related fast transforms with applications". Thesis, University of Newcastle Upon Tyne, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.293568.
Texto completoLangford, Mitchel. "Some applications of digital image processing for automation in palynology". Thesis, University of Hull, 1988. http://hydra.hull.ac.uk/resources/hull:3098.
Texto completoKoubaroulis, D. A. "The multimodal neighbourhood signature for modelling object colour appearance and applications in computer vision". Thesis, University of Surrey, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.365142.
Texto completoGiovanini, Renato de Macedo [UNESP]. "SSVEP-EEG signal pattern recognition system for real-time brain-computer interfaces applications". Universidade Estadual Paulista (UNESP), 2017. http://hdl.handle.net/11449/151710.
Texto completoApproved for entry into archive by Monique Sasaki (sayumi_sasaki@hotmail.com) on 2017-09-27T20:24:55Z (GMT) No. of bitstreams: 1 giovanini_rm_me_ilha.pdf: 10453769 bytes, checksum: 7f7e2415a0912fae282affadea2685b8 (MD5)
Made available in DSpace on 2017-09-27T20:24:55Z (GMT). No. of bitstreams: 1 giovanini_rm_me_ilha.pdf: 10453769 bytes, checksum: 7f7e2415a0912fae282affadea2685b8 (MD5) Previous issue date: 2017-08-18
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
There are, nowadays, about 110 million people in the world who live with some type of severe motor disability. Specifically in Brazil, about 2.2% of the population are estimated to live with a condition of difficult locomotion. Aiming to help these people, a vast variety of devices, techniques and services are currently being developed. Among those, one of the most complex and challenging techniques is the study and development of Brain-Computer Interfaces (BCIs). BCIs are systems that allow the user to communicate with the external world controlling devices without the use of muscles or peripheral nerves, using only his decoded brain activity. To achieve this, there is a need to develop robust pattern recognition systems, that must be able to detect the user’s intention through electroencephalography (EEG) signals and activate the corresponding output with reliable accuracy and within the shortest possible processing time. In this work, different EEG signal processing techniques were studied, and it is presented the development of a EEG under visual stimulation (Steady-State Visual Evoked Potentials - SSVEP) pattern recognition system. Using only Open Source tools and Python programming language, modules to manage datasets, reduce noise, extract features and perform classification of EEG signals were developed, and a comparative study of different techniques was performed, using filter banks and Discrete Wavelet Transforms (DWT) as feature extraction approaches, and the classifiers K-Nearest Neighbors, Multilayer Perceptron and Random Forests. Using DWT approach with Random Forest and Multilayer Perceptron classifiers, high accuracy rates up to 92 % were achieved in deeper decomposition levels. Then, the small-size microcomputer Raspberry Pi was used to perform time processing evaluation, obtaining short processing times for every classifiers. This work is a preliminary study of BCIs at the Laboratório de Instrumentação e Engenharia Biomédica, and, in the future, the system here presented may be part of a complete SSVEP-BCI system.
Existem, atualmente, cerca de 110 milhões de pessoas no mundo que vivem com algum tipo de deficiência motora severa. Especificamente no Brasil, é estimado que cerca de 2.2% da população conviva com alguma condição que dificulte a locomoção. Com o intuito de auxiliar tais pessoas, uma grande variedade de dispositivos, técnicas e serviços são atualmente desenvolvidos. Dentre elas, uma das técnicas mais complexas e desafiadoras é o estudo e o desenvolvimento de Interfaces Cérebro-Computador (ICMs). As ICMs são sistemas que permitem ao usuário comunicar-se com o mundo externo, controlando dispositivos sem o uso de músculos ou nervos periféricos, utilizando apenas sua atividade cerebral decodificada. Para alcançar isso, existe a necessidade de desenvolvimento de sistemas robustos de reconhecimento de padrões, que devem ser capazes de detectar as intenções do usuáro através dos sinais de eletroencefalografia (EEG) e ativar a saída correspondente com acurácia confiável e o menor tempo de processamento possível. Nesse trabalho foi realizado um estudo de diferentes técnicas de processamento de sinais de EEG, e o desenvolvimento de um sistema de reconhecimento de padrões de sinais de EEG sob estimulação visual (Potenciais Evocados Visuais de Regime Permanente - PEVRP). Utilizando apenas técnicas de código aberto e a linguagem Python de programação, foram desenvolvidos módulos para realizar o gerenciamento de datasets, redução de ruído, extração de características e classificação de sinais de EEG, e um estudo comparativo de diferentes técnicas foi realizado, utilizando-se bancos de filtros e a Transformada Wavelet Discreta (DWT) como abordagens de extração de características, e os classificadores K-Nearest Neighbors, Perceptron Multicamadas e Random Forests. Utilizando-se a DWT juntamente com Random Forests e Perceptron Multicamadas, altas taxas de acurácia de até 92 % foram obtidas nos níveis mais profundos de decomposição. Então, o computador Raspberry Pi, de pequenas dimensões, foi utilizado para realizar a avaliação do tempo de processamento, obtendo um baixo tempo de processamento para todos os classificadores. Este trabalho é um estudo preliminar em ICMs no Laboratório de Instrumentação e Engenharia Biomédica e, no futuro, pode ser parte de um sistema ICM completo.
Giovanini, Renato de Macedo. "SSVEP-EEG signal pattern recognition system for real-time brain-computer interfaces applications /". Ilha Solteira, 2017. http://hdl.handle.net/11449/151710.
Texto completoResumo: There are, nowadays, about 110 million people in the world who live with some type of severe motor disability. Specifically in Brazil, about 2.2% of the population are estimated to live with a condition of difficult locomotion. Aiming to help these people, a vast variety of devices, techniques and services are currently being developed. Among those, one of the most complex and challenging techniques is the study and development of Brain-Computer Interfaces (BCIs). BCIs are systems that allow the user to communicate with the external world controlling devices without the use of muscles or peripheral nerves, using only his decoded brain activity. To achieve this, there is a need to develop robust pattern recognition systems, that must be able to detect the user’s intention through electroencephalography (EEG) signals and activate the corresponding output with reliable accuracy and within the shortest possible processing time. In this work, different EEG signal processing techniques were studied, and it is presented the development of a EEG under visual stimulation (Steady-State Visual Evoked Potentials - SSVEP) pattern recognition system. Using only Open Source tools and Python programming language, modules to manage datasets, reduce noise, extract features and perform classification of EEG signals were developed, and a comparative study of different techniques was performed, using filter banks and Discrete Wavelet Transforms (DWT) as feature extraction approach... (Resumo completo, clicar acesso eletrônico abaixo)
Mestre
Siirtola, P. (Pekka). "Recognizing human activities based on wearable inertial measurements:methods and applications". Doctoral thesis, Oulun yliopisto, 2015. http://urn.fi/urn:isbn:9789526207698.
Texto completoTiivistelmä Liikettä mittaavista antureista, kuten kiihtyvyysantureista, saatavaa tietoa voidaan käyttää ihmisten liikkeiden mittaamiseen kiinnittämällä ne johonkin kohtaan ihmisen kehoa. Väitöskirjassani tavoitteena on opettaa tähän tietoon perustuvia käyttäjäriippumattomia malleja, joiden avulla voidaan tunnistaa ihmisten toimia, kuten käveleminen ja juokseminen. Näiden mallien toimivuus perustuu seuraavaan kahteen oletukseen: (1) koska henkilöiden liikkeet eri toimissa ovat erilaisia, myös niistä mitattava anturitieto on erilaista, (2) useamman henkilön liikkeet samassa toimessa ovat niin samanlaisia, että liikkeistä mitatun anturitiedon perusteella nämä liikkeet voidaan päätellä kuvaavan samaa toimea. Tässä väitöskirjassa käyttäjäriippumaton ihmisten toimien tunnistus perustuu hahmontunnistusmenetelmiin ja tunnistusta on sovellettu kahteen eri asiayhteyteen: arkitoimien tunnistamiseen älypuhelimella sekä toimintojen tunnistamiseen teollisessa ympäristössä. Molemmilla sovellusalueilla on omat erityisvaatimuksensa ja -haasteensa. Älypuhelimien liikettä mittaavien antureihin perustuva tunnistus on haastavaa esimerkiksi siksi, että puhelimen asento ja paikka voivat vaihdella. Se voi olla esimerkiksi laukussa tai taskussa, lisäksi se voi olla missä tahansa asennossa. Myös puhelimen akun rajallinen kesto luo omat haasteensa. Tämän vuoksi tunnistus tulisi tehdä mahdollisimman kevyesti ja vähän virtaa kuluttavalla tavalla. Teollisessa ympäristössä haasteet ovat toisenlaisia. Kun tarkoituksena on tunnistaa esimerkiksi työvaiheiden oikea suoritusjärjestys kokoamislinjastolla, yksikin virheellinen tunnistus voi aiheuttaa suuren vahingon. Teollisessa ympäristössä tavoitteena onkin tunnistaa toimet mahdollisimman tarkasti välittämättä siitä kuinka paljon virtaa ja tehoa tunnistus vaatii. Väitöskirjassani kerrotaan kuinka nämä erityisvaatimukset ja -haasteet voidaan ottaa huomioon suunniteltaessa malleja ihmisten toimien tunnistamiseen. Väitöskirjassani esiteltyjä uusia menetelmiä on sovellettu ihmisten toimien tunnistamiseen. Samoja menetelmiä voidaan kuitenkin käyttää monissa muissa hahmontunnistukseen liittyvissä ongelmissa, erityisesti sellaisissa, joissa analysoitava tieto on aikasarjamuotoista
Horn, Alastair N. "Iterated function systems, the parallel progressive synthesis of fractal tiling structures and their applications to computer graphics". Thesis, University of Oxford, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.257907.
Texto completoOnescu, Mircea Marian. "Adaptive measures of similarity - fuzzy hamming distance - and its applications to pattern recognition problems". Cincinnati, Ohio : University of Cincinnati, 2006. http://www.ohiolink.edu/etd/view.cgi?acc%5Fnum=ucin1163708478.
Texto completoTitle from electronic thesis title page (viewed Jan.27, 2007). Includes abstract. Keywords: Fuzzy Hamming Distance, artificial intelligence, fuzzy, image retrieval system Includes bibliographical references.
Al-aqeeli, Abdulqadir. "Reconfigurable wavelet-based architecture for pattern recognition applications using a field programmable gate array". Ohio : Ohio University, 1998. http://www.ohiolink.edu/etd/view.cgi?ohiou1177008904.
Texto completoAl-aqeeli, Abulqadir. "Reconfigurable wavelet-based architecture for pattern recognition applications using a field programmable gate array". Ohio University / OhioLINK, 1998. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1177008904.
Texto completoIONESCU, MIRCEA MARIAN. "ADAPTIVE MEASURES OF SIMILARITY - FUZZY HAMMING DISTANCE - AND ITS APPLICATIONS TO PATTERN RECOGNITION PROBLEMS". University of Cincinnati / OhioLINK, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1163708478.
Texto completoTESFAYE, YONATAN TARIKU. "Applications of a graph theoretic based clustering framework in computer vision and pattern recognition". Doctoral thesis, Università IUAV di Venezia, 2018. http://hdl.handle.net/11578/282321.
Texto completoPrasad, Saurabh. "MULTI-CLASSIFIERS AND DECISION FUSION FOR ROBUST STATISTICAL PATTERN RECOGNITION WITH APPLICATIONS TO HYPERSPECTRAL CLASSIFICATION". MSSTATE, 2008. http://sun.library.msstate.edu/ETD-db/theses/available/etd-11052008-125134/.
Texto completoJaafar, Mohd Zuli. "Chemometrics and pattern recognition methods with applications to environmental and quantitative structure-activity relationship studies". Thesis, University of Bristol, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.541608.
Texto completoLemanska, Agnieszka. "Chemometrics and pattern recognition applications to high-shear wet granulation process monitoring and metabolomic data". Thesis, University of Bristol, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.551294.
Texto completoDE, GIORGI ANDREA. "Novel pattern recognition methods for classification and detection in remote sensing and power generation applications". Doctoral thesis, Università degli studi di Genova, 2018. http://hdl.handle.net/11567/930347.
Texto completoHuang, X. (Xiaohua). "Methods for facial expression recognition with applications in challenging situations". Doctoral thesis, Oulun yliopisto, 2014. http://urn.fi/urn:isbn:9789526206561.
Texto completoTiivistelmä Kasvonilmeiden tunnistamisesta on viime vuosina tullut tietokoneille hyödyllinen tapa ymmärtää affektiivisesti ihmisen tunnetilaa. Kasvojen esittäminen ja kasvonilmeiden tunnistaminen rajoittamattomissa ympäristöissä ovat olleet kaksi kriittistä ongelmaa kasvonilmeitä tunnistavien järjestelmien kannalta. Tämä väitöskirjatutkimus myötävaikuttaa kasvonilmeitä tunnistavien järjestelmien tutkimukseen ja kehittymiseen kahdesta näkökulmasta: piirteiden irrottamisesta kasvonilmeiden tunnistamista varten ja kasvonilmeiden tunnistamisesta haastavissa olosuhteissa. Työssä esitellään spatiaalisia ja temporaalisia piirteenirrotusmenetelmiä, jotka tuottavat tehokkaita ja erottelukykyisiä piirteitä kasvonilmeiden tunnistamiseen. Ensimmäisenä työssä esitellään spatiaalinen piirteenirrotusmenetelmä, joka parantaa paikallisia kvantisoituja piirteitä käyttämällä parannettua vektorikvantisointia. Menetelmä tekee myös tilastollisista malleista monikäyttöisiä ja tiiviitä. Seuraavaksi työssä esitellään kaksi spatiotemporaalista piirteenirrotusmenetelmää. Ensimmäinen näistä käyttää esikäsittelynä monogeenistä signaalianalyysiä ja irrottaa spatiotemporaaliset piirteet paikallisia binäärikuvioita käyttäen. Toinen menetelmä irrottaa harvoja spatiotemporaalisia piirteitä käyttäen harvoja kuusitahokkaita ja spatiotemporaalisia paikallisia binäärikuvioita. Molemmat menetelmät parantavat paikallisten binärikuvioiden erottelukykyä ajallisessa ulottuvuudessa. Piirteenirrotusmenetelmien pohjalta työssä tutkitaan kasvonilmeiden tunnistusta kolmessa käytännön olosuhteessa, joissa esiintyy vaihtelua valaistuksessa, okkluusiossa ja pään asennossa. Ensiksi ehdotetaan lähi-infrapuna kuvantamista hyödyntävää diskriminatiivistä komponenttipohjaista yhden piirteen kuvausta, jolla saavutetaan korkea suoritusvarmuus valaistuksen vaihtelun suhteen. Toiseksi ehdotetaan menetelmä okkluusion havainnointiin, jolla dynaamisesti havaitaan peittyneet kasvon alueet. Uudenlainen menetelmä on kehitetty käsittelemään kasvojen okkluusio tehokkaasti. Viimeiseksi työssä on kehitetty moninäkymäinen diskriminatiivisen naapuruston säilyttävään upottamiseen pohjautuva menetelmä käsittelemään pään asennon vaihtelut. Menetelmä kuvaa moninäkymäisen kasvonilmeiden tunnistamisen yleistettynä ominaisarvohajotelmana. Kokeelliset tulokset julkisilla tietokannoilla osoittavat tässä työssä ehdotetut menetelmät suorituskykyisiksi kasvonilmeiden tunnistamisessa
Dong, Ming. "A New Measure of Classifiability and its Applications". University of Cincinnati / OhioLINK, 2001. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1003516324.
Texto completoYu, Hua. "Pattern recognition methods for automated detection and quantification: applications to passive remote sensing and near infrared spectroscopy". Diss., University of Iowa, 2014. https://ir.uiowa.edu/etd/1522.
Texto completo