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1
Klemo, Elios. "SYMLET AND GABOR WAVELET PREDICTION OF PRINT DEFECTS." UKnowledge, 2005. http://uknowledge.uky.edu/gradschool_theses/263.
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Recent studies have been done to create models that predict the response of the human visual system (HVS) based on how the HVS processes an image. The most widely known of these models is the Gabor model, since the Gabor patterns closely resemble the receptive filters in the human eye. The work of this thesis examines the use of Symlets to represent the HVS, since Symlets provide the benefit of orthogonality. One major problem with Symlets is that the energy is not stable in respective Symlet channels when the image patterns are translated spatially. This thesis addresses this problem by up sampling Symlets instead of down sampling, and thus creating shift invariant Symlets. This thesis then compares the representation of Gabor versus Symlet approach in predicting the response of the HVS to detecting print defect patterns such as banding and graining. In summary we noticed that Symlet prediction outperforms the Gabor prediction thus Symlets would be a good choice for HVS response prediction. We also concluded that for banding defect periodicity and size are important factors that affect the response of the HVS to the patterns. For graining defects we noticed that size does not greatly affect the response of the HVS to the defect patterns. We introduced our results using two set of performance metrics, the mean and median.
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Bishop, Shannon Renee Smith. "Gabor and wavelet analysis with applications to Schatten class integral operators." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/33976.
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This thesis addresses four topics in the area of applied harmonic analysis. First, we show that the affine densities of separable wavelet frames affect the frame properties. In particular, we describe a new relationship between the affine densities, frame bounds and weighted admissibility constants of the mother wavelets of pairs of separable wavelet frames. This result is also extended to wavelet frame sequences. Second, we consider affine pseudodifferential operators, generalizations of pseudodifferential operators that model wideband wireless communication channels. We find two classes of Banach spaces, characterized by wavelet and ridgelet transforms, so that inclusion of the kernel and symbol in appropriate spaces ensures the operator is Schatten p-class. Third, we examine the Schatten class properties of pseudodifferential operators. Using Gabor frame techniques, we show that if the kernel of a pseudodifferential operator lies in a particular mixed modulation space, then the operator is Schatten p-class. This result improves existing theorems and is sharp in the sense that larger mixed modulation spaces yield operators that are not Schatten class. The implications of this result for the Kohn-Nirenberg symbol of a pseudodifferential operator are also described. Lastly, Fourier integral operators are analyzed with Gabor frame techniques. We show that, given a certain smoothness in the phase function of a Fourier integral operator, the inclusion of the symbol in appropriate mixed modulation spaces is sufficient to guarantee that the operator is Schatten p-class.
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Grip, Niklas. "Wavelet and gabor frames and bases : approximation, sampling and applications." Doctoral thesis, Luleå, 2002. http://epubl.luth.se/1402-1544/2002/49.
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Peng, Pai. "Automated defect detection for textile fabrics using Gabor wavelet networks." View the Table of Contents & Abstract, 2006. http://sunzi.lib.hku.hk/hkuto/record/B38025966.
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Peng, Pai, and 彭湃. "Automated defect detection for textile fabrics using Gabor wavelet networks." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2006. http://hub.hku.hk/bib/B38766103.
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Subramaniam, Kumanan. "Vision based motion tracking and collision avoidance system for vehicle navigation." Thesis, University of Newcastle Upon Tyne, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.246656.
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Soares, João Vitor Baldini. "Segmentação de vasos sangüíneos em imagens de retina usando wavelets e classificadores estatísticos." Universidade de São Paulo, 2006. http://www.teses.usp.br/teses/disponiveis/45/45134/tde-24072007-174800/.
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Esta dissertação apresenta o desenvolvimento e avaliação de um método para a segmentação de vasos sangüíneos em imagens de retina, em que se usa a transformada wavelet contínua bidimensional combinada com classificação supervisionada. A segmentação dos vasos é a etapa inicial para a análise automática das imagens, cujo objetivo é auxiliar a comunidade médica na detecção de doenças. Entre outras doenças, as imagens podem revelar sinais da retinopatia diabética, uma das principais causas de cegueira em adultos, que pode ser prevenida se detectada em um diagnóstico precoce. A abordagem apresentada consiste na geração de segmentações pela classificação supervisionada de pixels nas classes \"vaso\" e \"não vaso\". As características usadas para classificação são obtidas através da transformada wavelet contínua bidimensional usando a wavelet de Gabor. Resultados são avaliados nos bancos públicos DRIVE e STARE de imagens coloridas através da análise ROC (\"receiver operating characteristic\", ou característica de operação do receptor). O método atinge áreas sob curvas ROC de 0.9614 e 0.9671 nos bancos DRIVE e STARE, respectivamente, ligeiramente superiores àquelas apresentadas por outros métodos do estado da arte. Apesar de bons resultados ROC, a análise visual revela algumas dificuldades do método, como falsos positivos ao redor do disco óptico e de patologias. A wavelet de Gabor mostra-se eficiente na detecção dos vasos, superando outros filtros lineares. Bons resultados e uma classificação rápida são obtidos usando o classificador bayesiano em que as funções de densidade de probabilidade condicionais às classes são descritas por misturas de gaussianas. A implementação do método está disponível na forma de \"scripts\" código aberto em MATLAB para pesquisadores interessados em detalhes de implementação, avaliação ou desenvolvimento de métodos.This dissertation presents the development and evaluation of a method for blood vessel segmentation in retinal images which combines the use of the two-dimensional continuous wavelet transform with supervised classification. Segmentation of the retinal vasculature is the first step towards automatic analysis of the images, aiming at helping the medical community in detecting diseases. Among other diseases, the images may reveal signs of diabetic retinopathy, a leading cause of adult blindness, which can be prevented if identified early enough. The presented approach produces segmentations by supervised classification of each image pixel as \"vessel\" or \"nonvessel\", with pixel features derived using the two-dimensional continuous Gabor wavelet transform. Results are evaluated on publicly available DRIVE and STARE color image databases using ROC (receiver operating characteristic) analysis. The method achieves areas under ROC curves of 0.9614 and 0.9671 on the DRIVE and STARE databases, respectively, being slightly superior than that presented by state-of-the-art approaches. Though good ROC results are presented, visual inspection shows some typical difficulties of the method, such as false positives on the borders of the optic disc and pathologies. The Gabor wavelet shows itself efficient for vessel enhancement, outperforming other linear filters. Good segmentation results and a fast classification phase are obtained using the Bayesian classifier with class-conditional probability density functions described as Gaussian mixtures. The method\'s implementation is available as open source MATLAB scripts for researchers interested in implementation details, evaluation, or development of methods.
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Hammarqvist, Ulf. "Audio editing in the time-frequency domain using the Gabor Wavelet Transform." Thesis, Uppsala universitet, Centrum för bildanalys, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-153634.
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Visualization, processing and editing of audio, directly on a time-frequency surface, is the scope of this thesis. More precisely the scalogram produced by a Gabor Wavelet transform is used, which is a powerful alternative to traditional techinques where the wave form is the main visual aid and editting is performed by parametric filters. Reconstruction properties, scalogram design and enhancements as well audio manipulation algorithms are investigated for this audio representation.The scalogram is designed to allow a flexible choice of time-frequency ratio, while maintaining high quality reconstruction. For this mean, the Loglet is used, which is observed to be the most suitable filter choice. Re-assignmentare tested, and a novel weighting function using partial derivatives of phase is proposed. An audio interpolation procedure is developed and shown to perform well in listening tests.The feasibility to use the transform coefficients directly for various purposes is investigated. It is concluded that Pitch shifts are hard to describe in the framework while noise thresh holding works well. A downsampling scheme is suggested that saves on operations and memory consumption as well as it speeds up real world implementations significantly. Finally, a Scalogram 'compression' procedure is developed, allowing the caching of an approximate scalogram.
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Porter, Robert Mark Stefan. "Texture classification and segmentation." Thesis, University of Bristol, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.389032.
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Dahale, Radhika. "Optoelectronic Multifractal Wavelet Analysis for Fast and Accurate Detection of Rainfall in Weather Radar Images." ScholarWorks@UNO, 2004. http://scholarworks.uno.edu/td/97.
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In this thesis we propose an automated process for the removal of non-precipitation echoes present in weather radar signals and accurate detection of rainfall. The process employs multifractal analysis using directional Gabor wavelets for accurate detection of the rain events. An optoelectronic joint transform correlator is proposed to provide ultra fast processing and wavelet analysis. Computer simulations of the proposed system show that the proposed algorithm is successful in the detecting rainfall accurately in radar images. The accuracy of the algorithms proposed are compared to accurate results that were generated under expert supervision. Results of the proposed system are also compared to results of QC algorithm for the ground validation software (GVS) used by TRMM ground validity Project and a previous QC algorithm. Several statistical measures computed for different reflectivity ranges show that the proposed algorithm gives accuracy as high as 98.95%, which exceed the 97.46% maximum accuracy for the GVS results. Also, the minimum error rate obtained by the proposed algorithm for different dB ranges decreases to 1.09% whereas the GVS results show a minimum error rate of 1.80%. The rain rate accumulation confirms the success of the proposed algorithm in the accurate removal of nonprecipitation echoes and a higher precision in rain accumulation estimates.
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Almeida, Osvaldo Cesar Pinheiro de. "Técnicas de processamento de imagens para localização e reconhecimento de faces." Universidade de São Paulo, 2006. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-22012007-160023/.
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A biometria é a ciência que estuda a mensuração dos seres vivos. Muitos trabalhos exploram as características dos seres humanos tais como, impressão digital, íris e face, a fim de desenvolver sistemas biométricos, utilizados em diversas aplicações (monitoramento de segurança, computação ubíqua, robótica). O reconhecimento de faces é uma das técnicas biométricas mais investigadas, por ser bastante intuitiva e menos invasiva que as demais. Alguns trabalhos envolvendo essa técnica se preocupam apenas em localizar a face de um indivíduo (fazer a contagem de pessoas), enquanto outros tentam identificá-lo a partir de uma imagem. Este trabalho propõe uma abordagem capaz de identificar faces a partir de quadros de vídeo e, posteriormente, reconhecê-las por meio de técnicas de análise de imagens. Pode-se dividir o trabalho em dois módulos principais: (1) - Localização e rastreamento de faces em uma seqüência de imagens ( frames), além de separar a região rastreada da imagem; (2) - Reconhecimento de faces, identificando a qual pessoa pertence. Para a primeira etapa foi implementado um sistema de análise de movimento (baseado em subtração de quadros) que possibilitou localizar, rastrear e captar imagens da face de um indivíduo usando uma câmera de vídeo. Para a segunda etapa foram implementados os módulos de redução de informações (técnica Principal Component Analysis - PCA), de extração de características (transformada wavelet de Gabor), e o de classificação e identificação de face (distância Euclidiana e Support Vector Machine - SVM). Utilizando-se duas bases de dados de faces (FERET e uma própria - Própria), foram realizados testes para avaliar o sistema de reconhecimento implementado. Os resultados encontrados foram satisfatórios, atingindo 91,92% e 100,00% de taxa de acertos para as bases FERET e Própria, respectivamente.Biometry is the science of measuring and analyzing biomedical data. Many works in this field have explored the characteristics of human beings, such as digital fingerprints, iris, and face to develop biometric systems, employed in various aplications (security monitoring, ubiquitous computation, robotic). Face identification and recognition are very apealing biometric techniques, as it it intuitive and less invasive than others. Many works in this field are only concerned with locating the face of an individual (for counting purposes), while others try to identify people from faces. The objective of this work is to develop a biometric system that could identify and recognize faces. The work can be divided into two major stages: (1) Locate and track in a sequence of images (frames), as well as separating the tracked region from the image; (2) Recognize a face as belonging to a certain individual. In the former, faces are captured from frames of a video camera by a motion analysis system (based on substraction of frames), capable of finding, tracking and croping faces from images of individuals. The later, consists of elements for data reductions (Principal Component Analysis - PCA), feature extraction (Gabor wavelets) and face classification (Euclidean distance and Support Vector Machine - SVM). Two faces databases have been used: FERET and a \"home-made\" one. Tests have been undertaken so as to assess the system\'s recognition capabilities. The experiments have shown that the technique exhibited a satisfactory performance, with success rates of 91.97% and 100% for the FERET and the \"home-made\" databases, respectively.
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Ravikumar, Rahul. "Multi-scale texture analysis of remote sensing images using gabor filter banks and wavelet transforms." Thesis, [College Station, Tex. : Texas A&M University, 2008. http://hdl.handle.net/1969.1/ETD-TAMU-3175.
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Sari, Huseyin. "Motion Estimation Using Complex Discrete Wavelet Transform." Master's thesis, METU, 2003. http://etd.lib.metu.edu.tr/upload/1223205/index.pdf.
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The estimation of optical flow has become a vital research field in image sequence
analysis especially in past two decades, which found applications in many fields such as
stereo optics, video compression, robotics and computer vision. In this thesis, the complex
wavelet based algorithm for the estimation of optical flow developed by Magarey and
Kingsbury is implemented and investigated.
The algorithm is based on a complex version of the discrete wavelet transform (CDWT),
which analyzes an image through blocks of filtering with a set of Gabor-like kernels with
different scales and orientations. The output is a hierarchy of scaled and subsampled
orientation-tuned subimages. The motion estimation algorithm is based on the relationship
between translations in image domain and phase shifts in CDWT domain, which is satisfied
by the shiftability and interpolability property of CDWT. Optical flow is estimated by using
this relationship at each scale, in a coarse-to-fine (hierarchical) manner, where information
from finer scales is used to refine the estimates from coarser scales.
The performance of the motion estimation algorithm is investigated with various image
sequences as input and the effects of the options in the algorithm like curvature-correction,
interpolation kernel between levels and some parameter values like confidence threshold
iv
maximum number of CDWT levels and minimum finest level of detail are also experimented
and discussed.
The test results show that the method is superior to other well-known algorithms in
estimation accuracy, especially under high illuminance variations and additive noise.
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He, Chao. "Advanced wavelet application for video compression and video object tracking." Connect to resource, 2005. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1125659908.
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Thesis (Ph. D.)--Ohio State University, 2005.Title from first page of PDF file. Document formatted into pages; contains xvii, 158 p.; also includes graphics (some col.). Includes bibliographical references (p. 150-158). Available online via OhioLINK's ETD Center
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Grip, Niklas. "Hilbert space frames and bases : a comparison of Gabor and wavelet frames and applications to multicarrier digital communications." Licentiate thesis, Luleå tekniska universitet, Matematiska vetenskaper, 2000. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-18012.
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Several signal processing applications today are based on the use of different transforms. The signals under consideration are written as a linear combination (or series) of some predefined set of functions. Traditionally, orthogonal bases have been used for this purpose, for example, in the discrete Fourier transform. The theory for orthogonal bases for Hilbert spaces can, however, be generalized to other sequences of functions, called frames. The first part of this thesis begins with an application-oriented introduction to the theory of frames and bases for separable Hilbert spaces. We explain similarities with and differences from the theory of orthogonal bases. Special attention is given to the relatively new theory of Gabor and Wavelet frames. We explain how they can be used for so-called time-frequency analysis. The main emphasis is on explaining fundamental similarities and differences between Gabor and wavelet frames. We also give an example of an application (OFDM) related to the second part of the thesis, for which nonorthogonal Gabor frames are superior to any orthogonal basis. The second part of this thesis concerns the current development of a standard for very high speed digital communication in ordinary telephone copper wires. It is the result of a cooperation with the Division of Signal Processing and Telia Research. We present a novel duplex method for Very high bitrate Digital Subscriber Lines (VDSL), called Zipper. It is intended to provide bit rates up to 52 Mbit per second, about 1000 times faster than the most common modems today. Zipper is based on Discrete Multi Tone (DMT) modulation. It uses an orthogonal basis of Gabor type for the signal transmission. Certain cyclical extensions are used to ensure the orthogonality between the basis functions. Zipper is proposed as a standard for VDSL to both the American National Standards Institute (ANSI) T1E1.4 group and in the European Telecommunication Standards Institute (ETSI) TM6 group. It will also be presented for the International Telecommunication Union (ITU). Telia Research is currently building a prototype together with ST Microelectronics (former SGS-Thomson), France. The first Zipper-VDSL modems are expected to be available on the mass market in the year 2001. The second part of this thext consists of a brief introduction to Zipper, an ANSI standard contribution and three conference papers. The standard contribution compares Zipper performance with competing standard proposals at that time: TDD and FDD. In the first conference paper we present a new and patented method for reducing the interference that the unshielded copper wires experience from radio transmissions. The two last conference papers present a low complexity method for reducing the so-called Peak to Average power Ratio (PAR) of the transmitted signal. PAR is a measure for the amount of rare but very high peaks in the signal. A reduced PAR allows for using a cheaper digital-to-analog converter and amplifier in the transmitter.Godkänd; 2000; 20070318 (ysko)
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Passeri, Mattia. "Analisi tempo-frequenza dei segnali: dalla trasformata di Fourier alla trasformata wavelet." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2020.
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L’obiettivo di questa tesi è lo studio dal punto di vista teorico dei principali strumenti matematici per l’analisi tempo-frequenza dei segnali: la trasformata di Fourier, la trasformata di Fourier a tempo breve (STFT) e la trasformata wavelet.
Il punto di partenza dello studio è l’analisi di Fourier, di cui vengono presentati i fondamenti e le proprietà. Poiché questo strumento ha il limite di fornire una rappresentazione spettrale di un segnale senza però localizzare nel tempo il suo contenuto in frequenza, si introduce l’analisi tempo-frequenza, proposta da Dennis Gabor nella seconda metà del Novecento come estensione dell’analisi di Fourier.
L'analisi tempo-frequenza si basa sull’idea di fare scorrere una finestra di dimensione fissa sul segnale, per analizzare l’evoluzione nel tempo del contenuto spettrale del segnale all'interno della finestra. Successivi studi matematici hanno dato origine all'analisi wavelet, che permette di utilizzare finestre temporali di analisi di dimensione variabile così da sfruttare sia finestre di grande durata, ideali per lo studio delle basse frequenze, sia finestre di breve durata, maggiormente adatte a rilevare le alte frequenze.
Nell’affrontare questi argomenti si è scelto di privilegiare gli aspetti teorici e formali alla base di ciascuno di essi, senza tralasciare però le intuizioni che hanno portato al loro sviluppo. Infine, sono stati presentati alcuni esempi di applicazione pratica di questi strumenti e dove necessario ne sono stati esaminati i pregi, i difetti e le limitazioni.
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Haneberg-Diggs, Dominique Miguel. "Seismic attributes of the Clinton interval reservoir in the Dominion East Ohio Gabor gas storage field near North Canton, Ohio." Wright State University / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=wright1418759184.
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Ouyang, Dingxin. "Intelligent Road Control System Using Advanced Image Processing Techniques." University of Toledo / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1352749656.
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Vedantham, Vikram. "In-situ temperature and thickness characterization for silicon wafers undergoing thermal annealing." Thesis, Texas A&M University, 2003. http://hdl.handle.net/1969.1/1181.
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Nano scale processing of IC chips has become the prime production technique as the microelectronic industry aims towards scaling down product dimensions while increasing accuracy and performance. Accurate control of temperature and a good monitoring mechanism for thickness of the deposition layers during epitaxial growth are critical parameters influencing a good yield. The two-fold objective of this thesis is to establish the feasibility of an alternative to the current pyrometric and ellipsometric techniques to simultaneously measure temperature and thickness during wafer processing. TAP-NDE is a non-contact, non-invasive, laser-based ultrasound technique that is employed in this study to contemporarily profile the thermal and spatial characteristics of the wafer. The Gabor wavelet transform allows the wave dispersion to be unraveled and the group velocity of individual frequency components to be extracted from the experimentally acquired time waveform. The thesis illustrates the formulation of a theoretical model that is used to identify the frequencies sensitive to temperature and thickness changes. The group velocity of the corresponding frequency components is determined and their corresponding changes with respect to temperature for different thickness are analytically modeled. TAP-NDE is then used to perform an experimental analysis on Silicon wafers of different thickness to determine the maximum possible resolution of TAP-NDE towards temperature sensitivity, and to demonstrate the ability to differentiate between wafers of different deposition layer thickness at temperatures up to 600?C. Temperature resolution is demonstrated for ?10?C resolution and for ?5?C resolution; while thickness differentiation is carried out with wafers carrying 4000? and 8000? of aluminum deposition layer. The experimental group velocities of a set of selected frequency components extracted using the Gabor Wavelet time-frequency analysis as compared to their corresponding theoretical group velocities show satisfactory agreement. As a result of this work, it is seen that TAP-NDE is a suitable tool to identify and characterize thickness and temperature changes simultaneously during thermal annealing that can replace the current need for separate characterization of these two important parameters in semiconductor manufacturing.
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Leach, Sandie Patricia. "Density conditions on Gabor frames." Thesis, Available online, Georgia Institute of Technology, 2004:, 2003. http://etd.gatech.edu/theses/available/etd-04082004-180257/unrestricted/leach%5Fsandie%5Fp%5F200312%5Fms.pdf.
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Sampath, Hemalatha. "Automated ventricular measurements using Gabor wavelets." Morgantown, W. Va. : [West Virginia University Libraries], 2007. https://eidr.wvu.edu/etd/documentdata.eTD?documentid=5511.
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Thesis (M.S.)--West Virginia University, 2007.Title from document title page. Document formatted into pages; contains ix, 76 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 71-76).
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Leite, Gladeston da Costa. "AnÃlise de campos de ventos oceÃnicos em imagens SAR." Universidade Federal do CearÃ, 2011. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=7498.
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FundaÃÃo Cearense de Apoio ao Desenvolvimento Cientifico e TecnolÃgicoEsta tese introduz uma nova metodologia para determinar a direÃÃo do vento sobre a superfÃcie dos oceanos utilizando tÃcnicas de processamento das imagens de Radar de Abertura SintÃtica (SAR, do inglÃs Synthetic Aperture Radar). A literatura relacionada demonstra um crescente interesse no processamento dessas imagens para detecÃÃo de alvos, classificaÃÃo de regiÃes, extraÃÃo de campos de ventos, monitoramento de derrames de Ãleo, aplicaÃÃes geofÃsicas e meteorolÃgicas. A extraÃÃo de campos de ventos em imagens SAR à uma tarefa desafiadora devido à contaminaÃÃo das mesmas por um ruÃdo oriundo do sistema de aquisiÃÃo, denominado speckle, que dificulta tarefas de processamento e interpretaÃÃo das mesmas. Portanto, esta tese propÃe metodologias de extraÃÃo da direÃÃo do vento por transformada de Fourier, transformadas wavelets e mÃtodos baseados em textura. As transformadas wavelets utilizadas para esta tarefa sÃo Gabor, ChapÃu Mexicano e o algoritmo à trous. Com relaÃÃo à anÃlise de textura utilizada, esta se baseia na informaÃÃo espacial da matriz de co-ocorrÃncia dos nÃveis de cinza para estimar a direÃÃo de padrÃes lineares em imagens contaminadas com speckle. Os experimentos foram realizados em imagens de textura sintÃticas, imagens do Ãlbum de Brodatz e imagens SAR sintÃticas e reais. Foi observado que os mÃtodos propostos foram capazes de estimar direÃÃes de padrÃes lineares e extrair campos de streaks de vento visÃveis em imagens SAR reais. As principais contribuiÃÃes desta tese sÃo: o mÃtodo proposto para estimaÃÃo de direÃÃo de ventos na superfÃcie do oceano e a extensÃo de tÃcnica jà existente na literatura, possibilitando assim a estimaÃÃo da velocidade dos ventos na faixa de 4 a 10 m/s. Os melhores resultados obtidos nesta tese foram alcanÃados utilizando o mÃtodo proposto que combina transformada wavelet e anÃlise de textura.
This thesis introduces a new methodology to determine the wind direction over the ocean surface using image processing techniques on SAR (Synthetic Aperture Radar) images. Related literature demonstrates a growing interest in processing these images for target detection, region classification, wind field extraction, oil spill monitoring, geophysical and meteorological applications. Wind field extraction in SAR images is a challenging task due to contamination acquisition system by speckle noise, which makes difficult processing and interpretation tasks. Thus, this thesis proposes methods for wind direction estimation by applying image transforms, such as Fourier and wavelets and furthermore texture-based methods. The wavelet transforms used for this task are Gabor, Mexican Hat and the à trous algorithm. Concerning the texture approach, it is based on the co-occurrence matrix to estimate direction of linear patterns in speckled images. The experiments were performed on synthetic texture, Brodatz album, synthetic and real SAR images. It was observed that the proposed methods were able to estimate directions of linear patterns and extract wind fields from visible wind-induced streaks on SAR images. The main contributions of this thesis are: to propose methods for wind direction estimation on the ocean surface and to extend existing techniques in the literature in order to provide wind vector estimation in the range of 4 to 10 m/s. The best results of this tese were achieved with the proposed method that combines wavelet transform and texture analysis.
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Naouai, Mohamed. "Localisation et reconstruction du réseau routier par vectorisation d'image THR et approximation des contraintes de type "NURBS"." Phd thesis, Université de Strasbourg, 2013. http://tel.archives-ouvertes.fr/tel-00994333.
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Ce travail de thèse vise à mettre en place un système d'extraction de réseau routier en milieu urbain à partir d'image satellite à très haute résolution. Dans ce contexte, nous avons proposé deux méthodes de localisation de routes. La première approche est fondée sur la procédure de conversion de l'image vers un format vectoriel. L'originalité de cette approche réside dans l'utilisation d'une méthode géométrique pour assurer le passage vers une représentation vectorielle de l'image d'origine et la mise en place d'un formalisme logique fondé sur un ensemble de critères perceptifs permettant le filtrage de l'information inutile et l'extraction des structures linéaires. Dans la deuxième approche, nous avons proposé un algorithme fondé sur la théorie des ondelettes, il met particulièrement en évidence les deux volets multi-résolution et multi-direction. Nous proposons donc une approche de localisation des routes mettant en jeux l'information fréquentielle multi directionnelle issue de la transformée en ondelette Log-Gabor. Dans l'étape de localisation, nous avons présenté deux détecteurs de routes qui exploitent l'information radiométrique, géométrique et fréquentielle. Cependant, ces informations ne permettent pas un résultat exact et précis. Pour remédier à ce problème, un algorithme de suivi s'avère nécessaire. Nous proposons la reconstruction de réseaux routiers par des courbes NURBS. Cette approche est basée sur un ensemble de points de repères identifiés dans la phase de localisation. Elle propose un nouveau concept, que nous avons désigné par NURBSC, basé sur les contraintes géométriques des formes à approximer. Nous connectons les segments de route identifiés afin d'obtenir des tracés continus propres aux routes.
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Shen, LinLin. "Recognizing faces : an approach based on Gabor wavelets." Thesis, University of Nottingham, 2005. http://eprints.nottingham.ac.uk/10177/.
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As a hot research topic over the last 25 years, face recognition still seems to be a difficult and largely problem. Distortions caused by variations in illumination, expression and pose are the main challenges to be dealt with by researchers in this field. Efficient recognition algorithms, robust against such distortions, are the main motivations of this research. Based on a detailed review on the background and wide applications of Gabor wavelet, this powerful and biologically driven mathematical tool is adopted to extract features for face recognition. The features contain important local frequency information and have been proven to be robust against commonly encountered distortions. To reduce the computation and memory cost caused by the large feature dimension, a novel boosting based algorithm is proposed and successfully applied to eliminate redundant features. The selected features are further enhanced by kernel subspace methods to handle the nonlinear face variations. The efficiency and robustness of the proposed algorithm is extensively tested using the ORL, FERET and BANCA databases. To normalize the scale and orientation of face images, a generalized symmetry measure based algorithm is proposed for automatic eye location. Without the requirement of a training process, the method is simple, fast and fully tested using thousands of images from the BioID and BANCA databases. An automatic user identification system, consisting of detection, recognition and user management modules, has been developed. The system can effectively detect faces from real video streams, identify them and retrieve corresponding user information from the application database. Different detection and recognition algorithms can also be easily integrated into the framework.
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Amin, Md Ashraful. "Gabor wavelets for human biometrics = Gaibo xiao bo zai ren ti shi bie zhong de ying yong /." access full-text access abstract and table of contents, 2009. http://libweb.cityu.edu.hk/cgi-bin/ezdb/thesis.pl?phd-ee-b23749489f.pdf.
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Thesis (Ph.D.)--City University of Hong Kong, 2009."Submitted to the Department of Electronic Engineering in partial fulfillment of the requirements for the degree of Doctor of Philosophy." Includes bibliographical references.
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Ferrari, Ricardo José. "Detecção computacional de assimetrias entre mamogramas." Universidade de São Paulo, 2002. http://www.teses.usp.br/teses/disponiveis/18/18133/tde-30092015-141016/.
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Neste trabalho foram propostas técnicas para a segmentação automática de mamogramas e para a detecção de assimetrias entre mamogramas esquerdo e direito. A segmentação é realizada através de três técnicas computacionais para a identificação de três importantes regiões anatômicas nos mamogramas: borda da mama, músculo peitoral e disco fibro-glandular. O primeiro método focaliza a identificação da borda da mama através do uso de um modelo de contorno ativo especialmente projetado para esse propósito. Neste estágio, a borda da mama é automaticamente demarcada, todos os artefatos fora dessa região são eliminados, e a região de interesse usada para a detecção do músculo peitoral é definida. No próximo estágio, a borda do músculo peitoral é determinada usando uma técnica multiresolução baseada na representação Gabor wavelets. Finalmente, um modelo de densidades da mama, baseado no modelo da mistura finita de Gaussianas, é proposto para a representação de quatro categorias de tecidos mamários com diferentes densidades. O disco fibro-glandular é identificado através da aplicação de um limiar sob as classes de densidades determinadas no modelo. Os métodos propostos foram aplicados em 84 imagens de mamogramas de projeções médio-laterais oblíqüas da base de dados Mini-MIAS (\"Mammographic Image Analysis Society\", London, UK). A avaliação dos resultados dos procedimentos de segmentação da borda da mama e borda do músculo peitoral foi realizada com base no percentual de pixels falso-positivos (FPs) e falso-negativos (FNs) determinados por comparação entre os contornos verdadeiros e os contornos automaticamente identificados. As taxas médias de FPs e FNs para as bordas da mama e do músculo peitoral foram, respectivamente, de 0,41% e 0,58%, e 1,78% e 5,77%. A segmentação dos discos fibro-glandulares foi subjetivamente classificada por radiologistas e os resultados indicaram que em mais de 80% dos casos a segmentação foi ) considerada aceitável para o uso em sistemas de auxílio ao diagnóstico. A detecção de assimetrias foi realizada usando informações direcionais, obtidas a partir da representação multiresolução Gabor wavelets, e de informações de forma e densidade, extraídas dos discos fibro-glandulares dos mamogramas esquerdo e direito. No procedimento de análise direcional, uma representação wavelet formada por filtros de Gabor bidimensionais com variação em freqüência e orientação, especialmente projetadas para reduzir a redundância na representação, é aplicada para uma dada imagem. As respostas dos filtros para diferentes escalas e orientações são analisadas através da transformada de Karhunen-Loève (KL) e pelo método de limiarização de Otsu. A transformada KL é aplicada para selecionar os componentes principais das respostas dos filtros, preservando apenas os elementos direcionais mais relevantes que aparecem em todas as escalas. Os componentes principais selecionados e limiarizados pela técnica de Otsu são usados para obter as imagens de magnitude e fase dos componentes direcionais da imagem. Medidas estatísticas extraídas dos diagramas de rosa calculados a partir das imagens de fase são usadas para a análise quantitativa e qualitativa dos padrões orientados. Um total de 11 atributos é extraído dos discos fibro-glandulares segmentados dos mamogramas esquerdo e direito, e a diferença calculada para cada par de atributos é usada como uma medida para a detecção de assimetrias. Um total de 88 imagens (22 casos normais, 14 casos de densidades assimétricas e 8 casos de distorções de arquitetura) da base de dados Mini-MIAS foram usadas para avaliar o método proposto. A combinação exaustiva dos atributos juntamente com a análise de componentes principais foi usada para selecionar o melhor subgrupo de atributos. A classificação foi realizada através de classificadores de Bayes (linear e quadrático) ) e usando o método \"leave-one-out\". Uma taxa de classificação correta de 84,44% foi alcançada.In this work, techniques are proposed for the automatic segmentation of mammograms and detection of asymmetries between left and right mammograms. The segmentation is performed by using three computational techniques for the identification of three important anatomical regions of mammograms: the skin-air boundary, the pectoral muscle, and the fibro-glandular disc. The first method focuses on the identification of the skin-air boundary by using an active contour model algorithm specially tailored for this purpose. In this stage, the skin-air boundary is demarcated, all artefacts outside the breast region are eliminated, and the region of interest for detection of the pectoral muscle is defined. In the next stage, the edge of the pectoral muscle is determined by using a multiresolution technique based upon a Gabor wavelets representation. Finally, a density breast model based upon a Gaussian mixture model is proposed for the representation of four categories of different density tissues in the breast. The fibro-glandular disc is identified by thresholding the density categories of the model. The methods proposed were applied to 84 images of medio-lateral oblique mammograms from the Mini-MIAS (Mammographic Image Analysis Society, London, U.K.) database. The evaluation of the skin-air boundary and the pectoral muscle edge were performed based upon the percentage of false-positive (FP) and false-negative (FN) pixels determined by comparison between the true contours and the contours automatically identified. The FP and FN average rates for the skin-air boundary and the pectoral muscle edge were, respectively, 0.41% and 0.58%, and 1.78% and 5.77%. Two radiologists subjectively rated the segmentation of the fibro-glandular disc and the results indicate that in more than 80% of the cases, the segmentation was considered acceptable for a Computer Aided Diagnosis purposes. Detection of asymmetries (continua) (continuação) is performed by using directional information, obtained from a multiresolution Gabor wavelets representation, and shape and density information, extracted from the fibro-glandular discs of left and right mammograms. In the directional procedure, a particular wavelet scheme with 2-D Gabor filters as elementary functions with varying tuning frequency and orientation, specifically designed in order to reduce the redundancy in the wavelet-based representation, is applied to the given image. The filter responses for different scales and orientation are analyzed by using the Karhunen-Loève (KL) transform and Otsu\'s method of thresholding. The KL transform is applied to select the principal components of the filter responses, preserving only the most relevant directional elements appearing at all scales. The selected principal components are thresholded by using Otsu\'s method and used to obtain the magnitude and phase of the image directional components. Rose diagrams computed from the phase images and statistical measures computed thereof are used for quantitative and qualitative analysis of the oriented patterns. A total of 11 features are also extracted from the segmented fibro-glandular discs of left-right mammograms, and the difference of each feature pair is used as a measure for detecting asymmetries. A total of 88 images from 22 normal cases, 14 asymmetric cases, and 8 architectural distortion cases from the Mini-MIAS database were used to evaluate the scheme. An exhaustive combination of the features along with the principal components analysis was used to select the best feature set. The classification was performed by using two Bayes\' classifiers (linear and quadratic) and the leave-one-out methodology. Average classification accuracy up to 84.44% was achieved.
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Maruniak, Lukáš. "Software pro biometrické rozpoznávání duhovky lidského oka." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2015. http://www.nusl.cz/ntk/nusl-235000.
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In my thesis, I focus on the task of recognizing human iris from an image.In the beginning, the work deals with a question of biometrics, its importance and basic concepts, which are necessary for use in following text. Subsequently process of human Iris detection is described together with theory of evolution algorithms. In the implementation part, is described the design of implemented solution, which uses evolution algorithms, where is emphasis on correct pupil and iris boundary detection.
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Sena, Emanuel Dario Rodrigues. "Multilinear technics in face recognition." Universidade Federal do CearÃ, 2014. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=13381.
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CoordenaÃÃo de AperfeiÃoamento de NÃvel SuperiorIn this dissertation, the face recognition problem is investigated from the standpoint of multilinear algebra, more specifically the tensor decomposition, and by making use of Gabor wavelets. The feature extraction occurs in two stages: first the Gabor wavelets are applied holistically in feature selection; Secondly facial images are modeled as a higher-order tensor according to the multimodal factors present. Then, the HOSVD is applied to separate the multimodal factors of the images. The proposed facial recognition approach exhibits higher average success rate and stability when there is variation in the various multimodal factors such as facial position, lighting condition and facial expression. We also propose a systematic way to perform cross-validation on tensor models to estimate the error rate in face recognition systems that explore the nature of the multimodal ensemble. Through the random partitioning of data organized as a tensor, the mode-n cross-validation provides folds as subtensors extracted of the desired mode, featuring a stratified method and susceptible to repetition of cross-validation with different partitioning.
Nesta dissertaÃÃo o problema de reconhecimento facial à investigado do ponto de vista da Ãlgebra multilinear, mais especificamente por meio de decomposiÃÃes tensoriais fazendo uso das wavelets de Gabor. A extraÃÃo de caracterÃsticas ocorre em dois estÃgios: primeiramente as wavelets de Gabor sÃo aplicadas de maneira holÃstica na seleÃÃo de caracterÃsticas; em segundo as imagens faciais sÃo modeladas como um tensor de ordem superior de acordo com o fatores multimodais presentes. Com isso aplicamos a decomposiÃÃo tensorial Higher Order Singular Value Decomposition (HOSVD) para separar os fatores que influenciam na formaÃÃo das imagens. O mÃtodo de reconhecimento facial proposto possui uma alta taxa de acerto e estabilidade quando hà variaÃÃo nos diversos fatores multimodais, tais como, posiÃÃo facial, condiÃÃo de iluminaÃÃo e expressÃo facial. Propomos ainda uma maneira sistemÃtica para realizaÃÃo da validaÃÃo cruzada em modelos tensoriais para estimaÃÃo da taxa de erro em sistemas de reconhecimento facial que exploram a natureza multilinear do conjunto de imagens. AtravÃs do particionamento aleatÃrio dos dados organizado como um tensor, a validaÃÃo cruzada modo-n proporciona a criaÃÃo de folds extraindo subtensores no modo desejado, caracterizando um mÃtodo estratificado e susceptÃvel a repetiÃÃes da validaÃÃo cruzada com diferentes particionamentos.
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Castañon, Cesar Armando Beltran. ""Recuperação de imagens por conteúdo através de análise multiresolução por Wavelets"." Universidade de São Paulo, 2003. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-29072004-194807/.
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Os sistemas de recuperação de imagens por conteúdo (CBIR -Content-based Image Retrieval) possuem a habilidade de retornar imagens utilizando como chave de busca outras imagens. Considerando uma imagem de consulta, o foco de um sistema CBIR é pesquisar no banco de dados as "n" imagens mais similares à imagem de consulta de acordo com um critério dado. Este trabalho de pesquisa foi direcionado na geração de vetores de características para um sistema CBIR considerando bancos de imagens médicas, para propiciar tal tipo de consulta. Um vetor de características é uma representação numérica sucinta de uma imagem ou parte dela, descrevendo seus detalhes mais representativos. O vetor de características é um vetor "n"-dimensional contendo esses valores. Essa nova representação da imagem pode ser armazenada em uma base de dados, e assim, agilizar o processo de recuperação de imagens. Uma abordagem alternativa para caracterizar imagens para um sistema CBIR é a transformação do domínio. A principal vantagem de uma transformação é sua efetiva caracterização das propriedades locais da imagem. Recentemente, pesquisadores das áreas de matemática aplicada e de processamento de sinais desenvolveram técnicas práticas de "wavelet" para a representação multiescala e análise de sinais. Estas novas ferramentas diferenciam-se das tradicionais técnicas de Fourier pela forma de localizar a informação no plano tempo-freqüência; basicamente, elas têm a capacidade de mudar de uma resolução para outra, o que faz delas especialmente adequadas para a análise de sinais não estacionários. A transformada "wavelet" consiste de um conjunto de funções base que representa o sinal em diferentes bandas de freqüência, cada uma com resoluções distintas correspondentes a cada escala. Estas foram aplicadas com sucesso na compressão, melhoria, análise, classificação, caracterização e recuperação de imagens. Uma das áreas beneficiadas, onde essas propriedades têm encontrado grande relevância, é a área médica, através da representação e descrição de imagens médicas. Este trabalho descreve uma abordagem para um banco de imagens médicas, que é orientada à extração de características para um sistema CBIR baseada na decomposição multiresolução de "wavelets" utilizando os filtros de Daubechies e Gabor. Essas novas características de imagens foram também testadas utilizando uma estrutura de indexação métrica "Slim-tree". Assim, pode-se aumentar o alcance semântico do sistema cbPACS (Content-Based Picture Archiving and Comunication Systems), atualmente em desenvolvimento conjunto entre o Grupo de Bases de Dados e Imagens do ICMC--USP e o Centro de Ciências de Imagens e Física Médica do Hospital das Clínicas de Riberão Preto-USP.Content-based image retrieval (CBIR) refers to the ability to retrieve images on the basis of the image content. Given a query image, the goal of a CBIR system is to search the database and return the "n" most similar (close) ones to the query image according to a given criteria. Our research addresses the generation of feature vectors of a CBIR system for medical image databases. A feature vector is a numeric representation of an image or part of it over its representative aspects. The feature vector is a "n"-dimensional vector organizing such values. This new image representation can be stored into a database and allow a fast image retrieval. An alternative for image characterization for a CBIR system is the domain transform. The principal advantage of a transform is its effective characterization for their local image properties. In the past few years, researches in applied mathematics and signal processing have developed practical "wavelet" methods for the multiscale representation and analysis of signals. These new tools differ from the traditional Fourier techniques by the way in which they localize the information in the time-frequency plane; in particular, they are capable of trading one type of resolution for the other, which makes them especially suitable for the analysis of non-stationary signals. The "wavelet" transform is a set of basis functions that represents signals in different frequency bands, each one with a resolution matching its scale. They have been successfully applied to image compression, enhancements, analysis, classifications, characterization and retrieval. One privileged area of application where these properties have been found to be relevant is medical imaging. In this work we describe an approach to CBIR for medical image databases focused on feature extraction based on multiresolution "wavelets" decomposition, taking advantage of the Daubechies and Gabor. Fundamental to our approach is how images are characterized, such that the retrieval procedure can bring similar images within the domain of interest, using a metric structure indexing, like the "Slim-tree". Thus, it increased the semantic capability of the cbPACS(Content-Based Picture Archiving and Comunication Systems), currently in joined developing between the Database and Image Group of the ICMC--USP and the Science Center for Images and Physical Medic of the Clinics Hospital of Riberão Preto--USP.
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Siqueira, Alexandre Fioravante de [UNESP]. "Estudos de imagens provenientes de membranas de borracha natural com aditivos metálicos utilizando técnicas de Fourier, Gabor e Wavelets." Universidade Estadual Paulista (UNESP), 2011. http://hdl.handle.net/11449/99708.
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siqueira_af_me_bauru.pdf: 2385810 bytes, checksum: c7d225594dbddd7edac58fe3ed5cdc6c (MD5)A pesquisa aqui apresentada visa o reconhecimento de padrões entre imagens de microscopias de diferentes tipos, adquiridas de membranas de borracha natural com aditivos metálicos. Estes estudos deram origem ao software WaveFPR (Wavelet and Fourier transforms for Pattern Recognition), criado para auxiliar na aplicação das ferramentas matemáticas envolvidas, a saber: as transformadas de Fourier e de Gabor, e também as wavelets de Haar e Daubechies. Para cada imagem processada pelo software, é gerado um conjunto de coeficientes correspondentes àquela imagem. Estes coeficientes são interprestados como uma assinatura digital da membrana; cada transformada retorna uma assinatura única para cada imagem. Estas assinaturas podem ser comparadas entre si, e esta comparação retorna informações relativas às membranas de borracha natural. O software criado oferece ainda uma interface para a utilização de técnica de emparelhamento (template matching) entre uma imagem-modelo de uma partícula metálica e uma imagem-alvo de borracha natural com aditivo metálico, com o uso dos coeficientes gerados. Do processamento de várias imagens foi construído um banco de dados com os coeficientes retirados destas imagens analisadas. Com este banco de dados e o uso da técnica de emparelhamento, são especificados os materiais constituintes de uma amostra, com o processamento da imagem pelo programa e a comparação dos resultados obtidos com os dados armazenados previamente
The research presented here aims at recognizing patterns between imagens of different types of micrsocopy, acquired from natural rubber membranes with metallic additives. These studies gave rise to the software WaveFPR (Wavelet and Fourier transforms for Pattern Recognition), created to assist in the application of the mathematical tools involved, namely: the Fourier and Gabor transforms, and also Haar and Daubechies wavelets. For each image processed by the softwary, a set of coefficients corresponding to that image is generated. These coefficients are interpreted as adigital signature of the membrane; each transform returns a unique signature to each image. These signatures can be compared to each other, and this comparison returns information about the natural rubber membranes. The software also offers an interface to use the template matching technique between a template image of a metallic particle and a target image of natural rubber with metallic additive, using the generated coefficients. A database was built with the coefficients taken from the analyzed images. This database contains information from several images. With this information and the templation matching technique, the constituent materials of a sample are specified, processing the image with the software and comparing the obtained results with the previously stores data
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Siqueira, Alexandre Fioravante de. "Estudos de imagens provenientes de membranas de borracha natural com aditivos metálicos utilizando técnicas de Fourier, Gabor e Wavelets /." Presidente Prudente, 2011. http://hdl.handle.net/11449/99708.
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Orientador: Aldo Eloizo JobCoorientador: Messias Meneguette Junior
Banca: Margarete Oliveira Domingues
Resumo: A pesquisa aqui apresentada visa o reconhecimento de padrões entre imagens de microscopias de diferentes tipos, adquiridas de membranas de borracha natural com aditivos metálicos. Estes estudos deram origem ao software "WaveFPR" (Wavelet and Fourier transforms for Pattern Recognition), criado para auxiliar na aplicação das ferramentas matemáticas envolvidas, a saber: as transformadas de Fourier e de Gabor, e também as wavelets de Haar e Daubechies. Para cada imagem processada pelo software, é gerado um conjunto de coeficientes correspondentes àquela imagem. Estes coeficientes são interprestados como uma "assinatura digital" da membrana; cada transformada retorna uma assinatura única para cada imagem. Estas assinaturas podem ser comparadas entre si, e esta comparação retorna informações relativas às membranas de borracha natural. O software criado oferece ainda uma interface para a utilização de técnica de emparelhamento (template matching) entre uma imagem-modelo de uma partícula metálica e uma imagem-alvo de borracha natural com aditivo metálico, com o uso dos coeficientes gerados. Do processamento de várias imagens foi construído um banco de dados com os coeficientes retirados destas imagens analisadas. Com este banco de dados e o uso da técnica de emparelhamento, são especificados os materiais constituintes de uma amostra, com o processamento da imagem pelo programa e a comparação dos resultados obtidos com os dados armazenados previamente
Abstract: The research presented here aims at recognizing patterns between imagens of different types of micrsocopy, acquired from natural rubber membranes with metallic additives. These studies gave rise to the software "WaveFPR" (Wavelet and Fourier transforms for Pattern Recognition), created to assist in the application of the mathematical tools involved, namely: the Fourier and Gabor transforms, and also Haar and Daubechies wavelets. For each image processed by the softwary, a set of coefficients corresponding to that image is generated. These coefficients are interpreted as a"digital signature" of the membrane; each transform returns a unique signature to each image. These signatures can be compared to each other, and this comparison returns information about the natural rubber membranes. The software also offers an interface to use the template matching technique between a template image of a metallic particle and a target image of natural rubber with metallic additive, using the generated coefficients. A database was built with the coefficients taken from the analyzed images. This database contains information from several images. With this information and the templation matching technique, the constituent materials of a sample are specified, processing the image with the software and comparing the obtained results with the previously stores data
Mestre
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Špiřík, Jan. "Modul pro generování "atomů" pro přeparametrizovanou reprezentaci signálu." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2010. http://www.nusl.cz/ntk/nusl-218234.
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The aim of this master thesis is generating new "atoms'' for purposes of overcomplete signal representation for toolbox Frames in MATLAB. At first is described the principle of overcomplete systems and so-called frames. In the thesis is introduced the basic distribution of frames and conditions of their constructions. There is described the basic principle of finding the sparse solutions in overcomplete systems too. The main part is dealt with construction single functions for generating "atoms'', such as: Gabor function, B-splines, Bézier curves, Daubechies wavelets, etc. At last there is introduced an example of usage these functions for reconstruction signal in comparison with Fourier and wavelet transforms.
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Hong, Paul S. "Octave-band Directional Decompositions." Diss., Georgia Institute of Technology, 2005. http://hdl.handle.net/1853/7210.
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A new two-dimensional transform is derived and implemented that is able to discriminate with respect to angular and radial frequency. This octave-band directional filter bank (OBDFB) is maximally decimated, has a separable polyphase implmentation, provides perfect reconstruction, and can be implemented in a tree structure allowing for a somewhat arbitrary number of angular and radial divisions. This decomposition is based on the directional filter bank (DFB) and is compared to other transforms with similar properties. Additionally, the OBDFB is used in three applications. Texture segmentation results are provided with comparisons to both decimated and undecimated transforms. With hyperspectral data, the OBDFB is used to increase classification accuracy using texture augmentation and likelihood score combination. Finally, ultrasound despeckling is addressed with respect to real-time implementations, and subjective test results are presented. A non-uniform two-dimensional transform is also designed that is a modified version of the OBDFB. It is rationally sampled and maximally decimated, but it provides both angular and radial frequency passbands from the initial stage instead of making separate divisions like the OBDFB. It also does not create subband boundaries on the principal frequency axes and allows for further decomposition as well.
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Schupp-Omid, Daniel. "Characterization of active sonar targets." Thesis, University of Iowa, 2016. https://ir.uiowa.edu/etd/3184.
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The problem of characterization of active sonar target response has important applications in many fields, including the currently cost-prohibitive recovery of unexploded ordinance on the ocean floor. We present a method for recognizing these objects using a multidisciplinary approach that fuses machine learning, signal processing, and feature engineering. In short, by taking inspiration from other fields, we solve the problem of object recognition in shallow water in an inexpensive way. These techniques add to the body of explored knowledge in the field of active sonar processing and address real-world problems in the process.
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Cook, James Allen. "A decompositional investigation of 3D face recognition." Queensland University of Technology, 2007. http://eprints.qut.edu.au/16653/.
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Automated Face Recognition is the process of determining a subject's identity from digital imagery of their face without user intervention. The term in fact encompasses two distinct tasks; Face Verficiation is the process of verifying a subject's claimed identity while Face Identification involves selecting the most likely identity from a database of subjects. This dissertation focuses on the task of Face Verification, which has a myriad of applications in security ranging from border control to personal banking. Recently the use of 3D facial imagery has found favour in the research community due to its inherent robustness to the pose and illumination variations which plague the 2D modality. The field of 3D face recognition is, however, yet to fully mature and there remain many unanswered research questions particular to the modality. The relative expense and specialty of 3D acquisition devices also means that the availability of databases of 3D face imagery lags significantly behind that of standard 2D face images. Human recognition of faces is rooted in an inherently 2D visual system and much is known regarding the use of 2D image information in the recognition of individuals. The corresponding knowledge of how discriminative information is distributed in the 3D modality is much less well defined. This dissertations addresses these issues through the use of decompositional techniques. Decomposition alleviates the problems associated with dimensionality explosion and the Small Sample Size (SSS) problem and spatial decomposition is a technique which has been widely used in face recognition. The application of decomposition in the frequency domain, however, has not received the same attention in the literature. The use of decomposition techniques allows a map ping of the regions (both spatial and frequency) which contain the discriminative information that enables recognition. In this dissertation these techniques are covered in significant detail, both in terms of practical issues in the respective domains and in terms of the underlying distributions which they expose. Significant discussion is given to the manner in which the inherent information of the human face is manifested in the 2D and 3D domains and how these two modalities inter-relate. This investigation is extended to cover also the manner in which the decomposition techniques presented can be recombined into a single decision. Two new methods for learning the weighting functions for both the sum and product rules are presented and extensive testing against established methods is presented. Knowledge acquired from these examinations is then used to create a combined technique termed Log-Gabor Templates. The proposed technique utilises both the spatial and frequency domains to extract superior performance to either in isolation. Experimentation demonstrates that the spatial and frequency domain decompositions are complimentary and can combined to give improved performance and robustness.
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Kadlček, Filip. "Implementace obrazových klasifikátorů v FPGA." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2010. http://www.nusl.cz/ntk/nusl-237091.
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The thesis deals with image classifiers and their implementation using FPGA technology. There are discussed weak and strong classifiers in the work. As an example of strong classifiers, the AdaBoost algorithm is described. In the case of weak classifiers, basic types of feature classifiers are shown, including Haar and Gabor wavelets. The rest of work is primarily focused on LBP, LRP and LR classifiers, which are well suitable for efficient implementation in FPGAs. With these classifiers is designed pseudo-parallel architecture. Process of classifications is divided on software and hardware parts. The thesis deals with hardware part of classifications. The designed classifier is very fast and produces results of classification every clock cycle.
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Phang, Shiau Shing. "Investigating and developing a model for iris changes under varied lighting conditions." Queensland University of Technology, 2007. http://eprints.qut.edu.au/16672/.
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Biometric identification systems have several distinct advantages over other authentication technologies, such as passwords, in reliably recognising individuals. Iris based recognition is one such biometric recognition system. Unlike other biometrics such as fingerprints or face images, the distinct aspect of the iris comes from its randomly distributed features. The patterns of these randomly distributed features on the iris have been proved to be fixed in a person's lifetime, and are stable over time for healthy eyes except for the distortions caused by the constriction and dilation of the pupil. The distortion of the iris pattern caused by pupillary activity, which is mainly due changes in ambient lighting conditions, can be significant. One important question that arises from this is: How closely do two different iris images of the same person, taken at different times using different cameras, in different environments, and under different lighting conditions, agree with each other? It is also problematic for iris recognition systems to correctly identify a person when his/her pupil size is very different from the person's iris images, used at the time of constructing the system's data-base. To date, researchers in the field of iris recognition have made attempts to address this problem, with varying degrees of success. However, there is still a need to conduct in-depth investigations into this matter in order to arrive at more reliable solutions. It is therefore necessary to study the behaviour of iris surface deformation caused by the change of lighting conditions. In this thesis, a study of the physiological behaviour of pupil size variation under different normal indoor lighting conditions (100 lux ~ 1,200 lux) and brightness levels is presented. The thesis also presents the results of applying Elastic Graph Matching (EGM) tracking techniques to study the mechanisms of iris surface deformation. A study of the pupil size variation under different normal indoor lighting conditions was conducted. The study showed that the behaviour of the pupil size can be significantly different from one person to another under the same lighting conditions. There was no evidence from this study to show that the exact pupil sizes of an individual can be determined at a given illumination level. However, the range of pupil sizes can be estimated for a range of specific lighting conditions. The range of average pupil sizes under normal indoor lighting found was between 3 mm and 4 mm. One of the advantages of using EGM for iris surface deformation tracking is that it incorporates the benefit of the use of Gabor wavelets to encode the iris features for tracking. The tracking results showed that the radial stretch of the iris surface is nonlinear. However, the amount of extension of iris surface at any point on the iris during the stretch is approximately linear. The analyses of the tracking results also showed that the behaviour of iris surface deformation is different from one person to another. This implies that a generalised iris surface deformation model cannot be established for personal identification. However, a deformation model can be established for every individual based on their analysis result, which can be useful for personal verification using the iris. Therefore, analysis of the tracking results of each individual was used to model iris surface deformations for that individual. The model was able to estimate the movement of a point on the iris surface at a particular pupil size. This makes it possible to estimate and construct the 2D deformed iris image of a desired pupil size from a given iris image of another different pupil size. The estimated deformed iris images were compared with their actual images for similarity, using an intensitybased (zero mean normalised cross-correlation). The result shows that 86% of the comparisons have over 65% similarity between the estimated and actual iris image. Preliminary tests of the estimated deformed iris images using an open-source iris recognition algorithm have showed an improved personal verification performance. The studies presented in this thesis were conducted using a very small sample of iris images and therefore should not be generalised, before further investigations are conducted.
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Lee, Yu-Cheng, and 李祐丞. "Facial Image Aging Synthesis Based on Log-Gabor Wavelet." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/18385318855872841410.
Full textAbstract:
碩士淡江大學
電機工程學系碩士班
97
At present, there are many application of facial image synthesis by computer graphics such as visual entertainment, movie production, game, cosmetic examinations, and searches for missing persons, etc. For these reason, manipulation of different age on human face become a common problem and the most popular one nowadays. In this paper, we propose an approach for manipulating different age texture on human face base on face detection, and Log-Gabor wavelet. First of all, Adaboost algorithm is used to get the main facial features and to normalize them into the same size and same features position of each picture. Second, we find an appropriate target age image which is similar to subject’s image, and then we employ the properties of multi-resolution and multi-channel to extract the decomposition maps of age texture with different age on human face by using Log-Gabor wavelet. Finally, we can effectively manipulate the different age of human face through controlling the amount of decomposition map of target age images to cover on the subject’s faces image. Experimental results show that the aging synthesis of facial images can be generated well by using our proposed approach.
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Hsiang-LinWang and 王祥麟. "Gabor Wavelet Based Detection of Ventricular Tachycardia and Ventricular Fibrillation." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/3774v8.
Full textAbstract:
碩士國立成功大學
電機工程學系
105
Ventricular tachycardia (VT) and ventricular fibrillation (VF) are the most lethal ventricular arrhythmias in the world. Both of them will cause serious damage to the human’s cardiovascular system. If VT or VF has been detected, it should take quick response to rescue the patients. Proposed method is based on the ECG signals to discriminate between non-lethal rhythm (N), VT and VF. The technique in the algorithm is mainly focus on Gabor Wavelet Transform (GWT) to analyze normalized power spectrum density, amplitude irregularity, mean amplitude and undulation rate of ECG signals. We test the algorithm on MIT-BIH Malignant Ventricular Arrhythmia Database (VFDB) and obtain the result by five-fold cross validation, the sensitivity of N, VT/VF (lethal rhythm), VT and VF are 97.5 %, 97.3 %, 91.7 % and 82.6 % respectively, and the overall accuracy is 93.2 %. For the purpose of health, happiness and care, the proposed method is suitable to implement on health cloud cluster to take remote home-care, health-care of patient.In addition, the application could also help the doctor to monitor patient’s conditions, which would increase the response time for and treatment.
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Pan, Chun-Wei, and 潘俊瑋. "The Facial Image Aging Synthesis System integrated ASM with Log-Gabor Wavelet." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/73616121641260640878.
Full textAbstract:
碩士淡江大學
電機工程學系碩士班
98
In present years, applications of facial image synthesis become much popular, such as visual entertainment, animations, games, cosmetic examinations, searching for missing people, etc. And our research is about missing people searching. In this thesis, we propose a human face aging synthesis system which implements by ASM integrated with Log-Gabor Wavelet. First, we use the ASM algorithm to extract the feature set of human face, and normalize them by geometric properties. Then, we find out one target image which is similar to the test image from the data base(?). And take the human image to analysis age texture by Log-Gabor wavelet, we can get the decomposition maps of age texture with different age on human face. Finally, we can effectively manipulate different human face images which are different age by controlling the amount of decomposition map of target age images which cover on the test image. From the experimental results, it seems that we can get a better performance in facial synthesis.
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Chang, Chih Hsiang, and 張智翔. "Combining STFT and Gabor Wavelet Network In The Texture Defect Detection Application." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/bd2fe9.
Full textAbstract:
碩士國立臺灣科技大學
電機工程系
95
The recent rapid developments in DSP techniques, such as filter design and pattern recognition have made decent feature extraction possible. As a result, in many automatic-process based mass productions, combining machine vision and image processing have gradually replaced human in defect inspection. Gabor filters are known to have excellent feature extraction capability if they are designed and implemented carefully. In texture defect detection, with all possible different texture structures and possible rotation angles, even they are so slightly altered from one another, the well known general Gabor method must be adjusted accordingly to prevent from severe sensibility suffering. For this reason, in our research, a new technique, by considering the Gabor filter as the only neuron of a single layer GWN(Gabor Wavelet Network) has been developed. In order to reduce the training cost, STFT has been used in estimating the frequency and orientation characteristics of the texture roughly, and the results are used as the initial values of the GWN. The performance of this scheme has been evaluated on a variety of textures with various defects and orientations. The results have shown the effectiveness of this new technique.
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Chih-YuanChen and 陳智遠. "Dataflow Model Design of Gabor Wavelet Transform for ECG Feature Extraction System." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/40368668672037327263.
Full textAbstract:
碩士國立成功大學
電機工程學系
104
This paper presents a dataflow design of 1-D Gabor Wavelet Transform based on an Algorithm/Architecture Co-exploration (AAC) design methodology for an electrocardiogram (ECG) Gabor feature extraction system. Traditional design methodologies only focus on execution time which ignores the relationship between algorithm and architecture design; on the contrary, AAC considers overall complexity between algorithm and architecture, such as number of operations, data storage, data transfer and degree of parallelism, and these complexity can be extracted by dataflow with multiple abstract levels and various data granularities. Due to dataflow exploration as a bridge between algorithm and architecture, proposed dataflow achieve multiplication reduction with Gabor symmetry property, addition reduction with commonality, and critical path reduction with retiming; more efficient than related works, and verified by CAL, which is a high-level programming language for writing dataflow to model data-driven processing.
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Honnouvo, Gilbert. "Gabor analysis and wavelet-like transforms on some non-Euclidean 2-dimensional manifolds." Thesis, 2007. http://spectrum.library.concordia.ca/975285/1/NR30119.pdf.
Full textAbstract:
Many problems in physics require the crafting of suitable tools for the analysis of data emanating from various non-Euclidean manifolds. The main tools, currently employed for this purpose, are Gabor type frames or general frames, and wavelets. Given this backdrop, the primary objective of this thesis is the development of wavelet-like and time frequency type transforms on certain non-Euclidean manifolds. An immediate example of such a manifold (in the sense that it is homeomorphic to several other two-dimensional manifolds of revolution) is the two-dimensional infinite cylinder, for which we construct here Gabor type frames and wavelets. The two-dimensional cylinder, as a surface of revolution, is naturally homeomorphic to several other two-dimensional manifolds (themselves also surfaces of revolution). Examples are the one-sheeted hyperboloid, the paraboloid with its apex removed, the sphere with two points removed, the ellipsoid with two points removed, the plane with the origin removed, the upper sheet, of the two sheeted hyperboloid, with one point removed, and so on. Using this fact, in this thesis we build Gabor type frames and wavelets on these manifolds. We also present a method for constructing wavelet-like transforms on a large class of such surfaces of revolution using a group theoretic approach. Finally, as a beginning to a related but different sort of study, we construct some localization operators associated to group representations, using symbols (in the sense of pseudo-differential operators) which are operator valued functions on the group.
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Morton, Stuart M. "EARLY DETECTION OF OVARIAN CANCER USING GABOR WAVELET PHASE QUANTIZATION AND BINARY CODING." Thesis, 2009. http://hdl.handle.net/1805/1972.
Full textAbstract:
ix
ABSTRACT
Stuart Morton
EARLY DETECTION OF OVARIAN CANCER USING GABOR WAVELET PHASE QUANTIZATION AND BINARY CODING
Ovarian cancer is the 5th most common cancer in women, but it is the most difficult to detect in its early stages. Early detection and treatment of ovarian cancer has been shown to increase the five year survival rate of a woman from 12% if caught in stage four of the disease up to 92% if caught in stage one of the disease. Using signal processing, pattern classification and a learning algorithm, it is possible to identify patterns in high dimensionality mass spectrometry data that distinguishes between cancer and non-cancer ovarian samples. For our research, proteomic spectra were generated using SELDI-TOF mass spectrum data, which was composed of 162 ovarian cancer and 91 non-ovarian cancer samples. We introduce a Gabor filter on the mass spectrometry data and design a binary coding scheme for phase quantization encoding that is used for the pattern classification. This pattern will expose crucial features in the data that can be used to correctly classify unmasked samples for the presence or absence of ovarian cancer. Our proposed algorithm was able to successfully discriminate ovarian cancer and non-ovarian samples that yielded results with sensitivities, specificities and accuracies in the 90% to 100% range.
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Πρινόπουλος, Σαράντης. "Εύρεση σχεδιαστικών αποκλίσεων αντικειμένων με υφή." Thesis, 2009. http://nemertes.lis.upatras.gr/jspui/handle/10889/1572.
Full textAbstract:
Αυτή η εργασία μελετά την εφαρμογή προηγμένων τεχνικών επεξεργασίας εικόνας από υπολογιστές για την επίλυση του προβλήματος της ανίχνευσης ατελειών σε υφάσματα από τις βιομηχανίες παραγωγής υφασμάτων. Προτείνεται μία νέα μέθοδος ανίχνευσης ατελειών, η οποία αποτελείται από ένα περιττό συμμετρικό φίλτρο Gabor πραγματικών τιμών, ένα άρτιο συμμετρικό φίλτρο Gabor πραγματικών τιμών και ένα φίλτρο εξομάλυνσης. Κατά την ανάπτυξη της μεθόδου, τα φίλτρα Gabor σχεδιάζονται με βάση τα χαρακτηριστικά του texture που εξάγονται βέλτιστα από μία εικόνα ενός μη ελαττωματικού υφάσματος με τη χρήση ενός Gabor Wavelet Network (GWN). Η απόδοση της προτεινόμενης μεθόδου αξιολογείται με τη χρήση ενός σετ εικόνων υφασμάτων που προέρχονται από μία βάση δεδομένων που περιέχει μία μεγάλη ποικιλία εικόνων ομογενών υφασμάτων. Τα αποτελέσματα παρουσιάζουν ακρίβεια στην ανίχνευση ατελειών με πολύ λίγες λάθος ανιχνεύσεις, από όπου φαίνεται η αποτελεσματικότητα της προτεινόμενης μεθόδου. Τα πειραματικά αποτελέσματα επιβεβαίωσαν τις δυνατότητες της μεθόδου και ένας υπολογισμός του υπολογιστικού φορτίου που χρειάζεται για την υλοποίηση της έδειξε ότι μπορεί να χρησιμοποιηθεί ακόμα και σε συστήματα ανίχνευσης πραγματικού χρόνου.-
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Mihai, Valeriu. "Otimização de algoritmo de redução de ruído para sistema de avaliação de fluxo sanguíneo em artéria coronária." Master's thesis, 2017. http://hdl.handle.net/10400.1/10111.
Full textAbstract:
Dissertação de mestrado, Engenharia Electrónica e Telecomunicações, Faculdade de Ciências e Tecnologia, Universidade do Algarve, 2017A ecografia assume-se como uma ferramenta de diagnóstico clínico de patologias cardiovasculares que utiliza as propriedades dos sinais de ultrassom Doppler. É frequentemente usada pelo facto de ser um meio económica, não invasiva e não ionizante. Os sinais de ultrassom Doppler são representados em forma de espectrograma o qual é afetado pela presença do ruído de tipo speckle, adicionando detalhes espúrios, afetando assim o contraste e o diagnóstico. Por isso, torna-se vital a pesquisa de metodologias de redução, e se possível, eliminação desse tipo de ruído. O objetivo desta tese de dissertação é desenvolver uma ferramenta computacional que possibilite a filtragem eficiente dos sinais Doppler de fluxo sanguíneo. As técnicas de filtragem abordadas nesta tese são a transformada de wavelet e a matching pursuit, em particular Batch-OMP, que vão ser comparadas com a técnica desenvolvida anteriormente sobre a mesma orientação denominada de NCTECH. A comparação destas técnicas é baseada na sua aplicação em sinais Doppler de fluxo sanguíneo simulados e em sinais Doppler de fluxo sanguíneo reais provenientes da artéria coronária. Para cada técnica foram testados vários tipos de parametrização para avaliar os que produziriam melhor desempenho. Aos sinais Doppler de ultrassom simulados foram adicionados vários níveis de ruído com o objetivo de possibilitar uma avaliação quantitativa do desempenho dos métodos de remoção de ruído, mediante o cálculo de métricas de qualidade. As métricas de qualidade consideradas foram as que permitiam quantificar a melhoria ocorrida ao nível da distorção do sinal, da preservação dos contornos e da melhoria do contraste. O sinal livre de ruído speckle foi usado como padrão de referência na comparação. Depois de inúmeros testes, verificou-se que o ruído speckle era reduzido em 34 dB a 11 dB conforme o nível de ruído era de 30 dB ou 5 dB respetivamente, quando o método de Batch-OMP era aplicado ao sinal. Estes valores de remoção de ruído são francamente melhores que os restantes métodos, em particular o NCTECH para o qual, perante os mesmos níveis de ruído se obtiveram remoções na gama de 32 dB a 9 dB. Nos casos onde não é possível garantir a origem nem a quantidade de ruído contido nos sinais, como é o caso dos sinais diretamente colhidos de pacientes, é usado como avaliador qualitativo a análise visual. A eficácia da metodologia de remoção de ruído desenvolvida e identificada com melhor performance na remoção de ruído de sinais simulados, foi validada em sinais clínicos de fluxo sanguíneo com origem em artéria descendente anterior e artéria marginal obtusa. Foi feita uma análise visual do espectro e, adicionalmente, do contorno do espectro após aplicação da referida metodologia. A metodologia proposta permitiu constatar a melhoria na nitidez do espectro e do contorno do espectro, proporcionando o melhor equilíbrio entre suavização e preservação da informação útil em comparação com as outras técnicas testadas. O algoritmo de remoção de ruído speckle proposto é constituído por três componentes: o pré-processamento; o Batch-OMP; e o pós-processamento. O pré-processamento consiste em criar o dicionário paramétrico de Gabor que contém ondas que se adaptam às estruturas locais do sinal e alem disso é calculado o valor do critério de paragem. A componente Batch-OMP consiste num algoritmo iterativo que calcula a representação esparsa do sinal usando o dicionário criado, permitindo assim obter o sinal aproximado livre de ruído. A parte do pós-processamento consiste em aproximar o sinal pós aplicação do algoritmo Batch-OMP e o sinal de entrada.
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Lin, Kuo-Chao, and 林國照. "Infrared Image and Optical Image Recognitions Based on Gabor Wavelets." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/65675944209323736468.
Full textAbstract:
博士國立清華大學
動力機械工程學系
95
Gabor wavelets have been successfully applied in various areas from image processing to pattern recognition. This success is mostly due to the fact that Gabor wavelets are equipped with a multi-channel filter capability such that a desired representation of the filter banks can be developed through selecting the parameters, i.e., the center frequency and the orientation of the filter. Although many types of image recognition based on Gabor wavelets have been studied and reported, there are still many valuable topics to be further investigated. The conventional image recognition based on Gabor wavelets is focusing on feature extraction in order to reduce the dimension of the training images. However, this feature extraction is only one of the processes in pattern recognition. For the recognition of the infrared image and optical image, it includes image representation, feature extraction or dimension reduction, classification technique, and the object character. The aim of this dissertation is to construct an image recognition system based on Gabor wavelets for infrared image and optical image. Therefore, An enhanced representation of the Gabor wavelets is proposed, in which the properties of Gaussian mask in Gabor wavelets is developed to enhance the enveloped function, and simultaneously the parameters of the filter based on Gabor wavelets is designed depending on the frequency response of the training images. In addition, the classification technique is important in the pattern recognition. In this dissertation, two classification methods are developed. The modified K-nearest neighbor rule is employed, in which the proposed modified rule combined with variance of the training images is used to find the optimal K value of the nearest distance between training images and testing images. For the neural network classification model, the self-adaptive radial basis function networks is employed, in which the self-adaptive networks has the property that during the training iteration the number of hidden neurons can be either increased or decreased according to the approximation error to prevent over fitting or under fitting. Furthermore, the practical application is also an important issue on the infrared image and optical image. Almost all of the reported Gabor-based image recognitions are applied on the face image. In order to consider the different kind of image, this dissertation proposes an image recognition method based on the Gabor wavelets, which can be applied on both the infrared image and optical image. Some experiments including infrared image and optical image recognitions are given. The good performances are verified through using the proposed scheme in this dissertation.
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Lai, Jian-Hao, and 賴健豪. "Automatic Age Estimation of Face Image based on Gabor Wavelets." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/88703553994603123104.
Full textAbstract:
碩士國立交通大學
資訊科學與工程研究所
99
In recent years, age estimation has become an important research topic in face recognition technology. Furthermore, age estimation is considered as a potential research which has lots of real-world potential applications such as multimedia communication, human computer interaction, and security. In this thesis, we present a novel and reliable framework for automatic age estimation. It exploits the whole new face feature based on the combination of Gabor wavelets and Orthogonal Locality Preserving Projections. In order to obtain more proper generalization ability with respect to sparse training samples, we use a support vector machine based classifier. Since this system can extract face aging features automatically in real-time, it has more potential in applications than other semi-automatic systems. The objective of this paper is to build a full automatic and real time age estimation system. The results obtained from this novel approach would provide better insight to operators in the field of age estimation to develop the real-world applications
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Chu, Kun-Long, and 褚坤龍. "Face Detection and Face Recognition Based on Gabor Wavelets and Locality Preserving Projection." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/evjyc7.
Full textAbstract:
碩士國立交通大學
電機學院電機產業專班
96
Face recognition has been a popular research topic for a long time because it can be widely used in many different fields, such as identity identification, content-based image retrieval, computer vision and human computer interaction. However, face detection, which serves as the preprocessing procedure, is equally important since it has to be done first before face recognition is taken. This thesis, therefore, proposes a method which takes the advantages of Gabor transformation and locality preserving projection to implement face recognition and face detection on a digital picture. The first step adopts face detection to find candidate face region. Second, Gabor wavelets transformation is adopted to extract face features of human face, and locality preserving projection is applied to project features of human face into lower dimension space. Afterwards, neural network is trained to decide whether candidate region is human face or not. Then, database is constructed manually according to the result of face detection. Finally, a neural network is trained by the faces is stored in the database. When a test picture is input the proposed method is able to identify the faces of the chosen people. According to the result of identification, pictures of the same person can be chosen from database and implement the identify-based image retrieval. The main contribution of this thesis is to employ the specialty of Gabor wavelets transformation, which is to maintain sufficient recognition rate in both time domain and frequency domain, to obtain face feature; moreover apply the strength of locality preserving projection, which preserves the local structure of the multidimensional structure, the immense feature vectors of Gabor wavelets transformation is lowered to minimum. The experiment results show the proposed method has good performance in both face detection and face recognition.
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Luhan, Vojtěch. "Samoopravné kódy a rozpoznávání podle duhovky." Master's thesis, 2013. http://www.nusl.cz/ntk/nusl-321435.
Full textAbstract:
Iris recognition constitutes one of the most powerful method for the iden- tification and authentication of people today. This thesis aims to describe the algorithms used in a sophisticated and mathematically correct way, while re- maining comprehensible. The description of these algorithms is not the only objective of this thesis; the reason they were chosen and potential improvements or substitutions are also discussed. The background of iris recognition, its use in cryptosystems, and the application of error-correcting codes are investigated as well.