Dissertations / Theses on the topic 'Feature extraction'
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Goodman, Steve. "Feature extraction and classification." Thesis, University of Sunderland, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.301872.
Full textLiu, Raymond. "Feature extraction in classification." Thesis, Imperial College London, 2013. http://hdl.handle.net/10044/1/23634.
Full textDhanjal, Charanpal. "Sparse Kernel feature extraction." Thesis, University of Southampton, 2008. https://eprints.soton.ac.uk/64875/.
Full textDrangel, Andreas. "Feature extraction from images with augmented feature inputs." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-219073.
Full textMaskininlärningsmodeller för uppgifter inom visuellt igenkännande så som bildigenkänning har på senaste tiden varit ett vanligt forskningsområde. Dock har inte mycket forskning fokuserats på att extrahera multipla särdrag från samma inmatning. Detta examensarbete syftar till att undersöka hur kunskap om ett särdrag influerar en modells prestanda som syftar till att klassificera ett annat särdrag, men även hur likhet och generalitet i särdragens datadistribution influerar modellprestanda. Integrering av förstärkande inmatning i form av extra särdragsinformation i bildklassificeringsmodeller visades ge olika resultat beroende på likhet och generalitet av distribution av särdragsdata. Hänsyn måste tas när förstärkande särdrag används för att de förstärkande särdragen inte ska bli helt redundanta eller helt ta över under träningsprocessen. Väljande av rimliga förstärkningssärdrag kan medföra önskade synergieffekter vilket påverkar modellprestandan till det bättre.
Kerr, Dermot. "Autonomous Scale Invariant Feature Extraction." Thesis, University of Ulster, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.502896.
Full textAlathari, Thamer. "Feature extraction in volumetric images." Thesis, University of Southampton, 2015. https://eprints.soton.ac.uk/379936/.
Full textSerce, Hakan. "Facial Feature Extraction Using Deformable Templates." Master's thesis, METU, 2003. http://etd.lib.metu.edu.tr/upload/1224674/index.pdf.
Full textSherrah, Jamie. "Automatic feature extraction for pattern recognition /." Title page, contents and abstract only, 1998. http://web4.library.adelaide.edu.au/theses/09PH/09phs553.pdf.
Full textCD-ROM in back pocket comprises experimental results and executables. Includes bibliographical references (p. 251-261).
Ljumić, Elvis. "Image feature extraction using fuzzy morphology." Diss., Online access via UMI:, 2007.
Find full textIncludes bibliographical references.
Daniušis, Povilas. "Feature extraction via dependence structure optimization." Doctoral thesis, Lithuanian Academic Libraries Network (LABT), 2012. http://vddb.laba.lt/obj/LT-eLABa-0001:E.02~2012~D_20121001_093645-66010.
Full textDaugelis praktiškai reikšmingu sistemu mokymo uždaviniu reikalauja gebeti panaudoti didelio matavimo, strukturizuotus, netiesinius duomenis. Vaizdu, teksto, socialiniu bei verslo ryšiu analize, ivairus bioinformatikos uždaviniai galetu buti tokiu uždaviniu pavyzdžiais. Todel požymiu išskyrimas dažnai yra pirmasis žingsnis, kuriuo pradedama duomenu analize ir nuo kurio priklauso galutinio rezultato sekme. Šio disertacinio darbo tyrimo objektas yra požymiu išskyrimo algoritmai, besiremiantys priklausomumo savoka. Darbe nagrinejamas priklausomumas, nusakytas kovariacinio operatoriaus Hilberto-Šmidto normos (HSIC mato) branduoliniu ivertiniu. Pasiulyti šiuo ivertiniu besiremiantys HBFE ir HSCA algoritmai leidžia dirbti su bet kokios strukturos duomenimis, bei yra formuluojami tikriniu vektoriu terminais (tai leidžia optimizavimui naudoti standartinius paketus), bei taikytini ne tik prižiurimo, bet ir dalinai prižiurimo mokymo imtims. Pastaruoju atveju HBFE ir HSCA modifikacijos remiasi Laplaso reguliarizacija. Eksperimentais su klasifikavimo bei daugiažymio klasifikavimo duomenimis parodyta, jog pasiulyti algoritmai leidžia pagerinti klasifikavimo efektyvuma lyginant su PCA ar LDA.
Elliott, Rodney Bruce. "Feature extraction techniques for grasp classification." Thesis, University of Canterbury. Mechanical Engineering, 1998. http://hdl.handle.net/10092/3447.
Full textGuo, Da 1976. "Automated feature extraction in oceanographic visualization." Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/33438.
Full textIncludes bibliographical references (leaves 141-147).
The ocean is characterized by a multitude of powerful, sporadic biophysical dynamical events; scientific research has reached the stage that their interpretation and prediction is now becoming possible. Ocean prediction, analogous to atmospheric weather prediction but combining biological, chemical and physical features is able to help us understand the complex coupled physics, biology and acoustics of the ocean. Applications of the prediction of the ocean environment include exploitation and management of marine resources, pollution control such as planning of maritime and naval operations. Given the vastness of ocean, it is essential for effective ocean prediction to employ adaptive sampling to best utilize the available sensor resources in order to minimize the forecast error. It is important to concentrate measurements to the regions where one can witness features of physical or biological significance in progress. Thus automated feature extraction in oceanographic visualization can facilitate adaptive sampling by presenting the physically relevant features directly to the operation planners. Moreover it could be used to help automate adaptive sampling. Vortices (eddies and gyres) and upwelling, two typical and important features of the ocean, are studied.
(cont.) A variety of feature extraction methods are presented, and those more pertinent to this study are implemented, including derived field generation and attribute set extraction. Detection results are evaluated in terms of accuracy, computational efficiency, clarity and usability. Vortices, a very important flow feature is the primary focus of this study. Several point-based and set-based vortex detection methods are reviewed. A set-based vortex core detection method based on geometric properties of vortices is applied to both classical vortex models and real ocean models. The direction spanning property, which is a geometric property, guides the detection of all the vortex core candidates, and the conjugate pair eigenvalue method is responsible for filtering out the false positives from the candidate set. Results show the new method to be analytically accurate and practically feasible, and superior to traditional point-based vortex detection methods. Detection methods of streamlines are also discussed. Using the novel cross method or the winding angle method, closed streamlines around vortex cores can be detected.
(cont.) Therefore, the whole vortex area, i.e., the combination of vortex core and surrounding streamlines, is detected. Accuracy and feasibility are achieved through automated vortex detection requiring no human inspection. The detection of another ocean feature, upwelling, is also discussed.
by Da Guo.
S.M.
Filho, Carlos André Braile Przewodowski. "Feature extraction from 3D point clouds." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-30072018-111718/.
Full textVisão computacional é uma área de pesquisa em que as imagens são o principal objeto de estudo. Um dos problemas abordados é o da descrição de formatos (em inglês, shapes). Classificação de objetos é um importante exemplo de aplicação que usa descritores de shapes. Classicamente, esses processos eram realizados em imagens 2D. Com o desenvolvimento em larga escala de novas tecnologias e o barateamento dos equipamentos que geram imagens 3D, a visão computacional se adaptou para este novo cenário, expandindo os métodos 2D clássicos para 3D. Entretanto, estes métodos são, majoritariamente, dependentes da variação de iluminação e de cor, enquanto os sensores 3D fornecem informações de profundidade, shape 3D e topologia, além da cor. Assim, foram estudados diferentes métodos de classificação de objetos e extração de atributos robustos, onde a partir destes são propostos e descritos novos métodos de extração de atributos a partir de dados 3D. Os resultados obtidos utilizando bases de dados 3D públicas conhecidas demonstraram a eficiência dos métodos propóstos e que os mesmos competem com outros métodos no estado-da-arte: o RPHSD (um dos métodos propostos) atingiu 85:4% de acurácia, sendo a segunda maior acurácia neste banco de dados; o COMSD (outro método proposto) atingiu 82:3% de acurácia, se posicionando na sétima posição do ranking; e o CNSD (outro método proposto) em nono lugar. Além disso, os métodos RPHSD têm uma complexidade de processamento relativamente baixa. Assim, eles atingem uma alta acurácia com um pequeno tempo de processamento.
Nash, Jason Mark. "Evidence gathering for dynamic feature extraction." Thesis, University of Southampton, 1999. https://eprints.soton.ac.uk/253031/.
Full textDirekoglu, Cem. "Feature extraction via heat flow analogy." Thesis, University of Southampton, 2009. https://eprints.soton.ac.uk/66595/.
Full textWärngård, Fredrik. "Feature Extraction from an SMT Problem." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-452233.
Full textRampally, Deepthi. "Iris recognition based on feature extraction." Manhattan, Kan. : Kansas State University, 2010. http://hdl.handle.net/2097/3647.
Full textPorter, Reid. "Evolution on FPGAs for feature extraction." Thesis, Queensland University of Technology, 2001.
Find full textCagli, Eleonora. "Feature Extraction for Side-Channel Attacks." Electronic Thesis or Diss., Sorbonne université, 2018. http://www.theses.fr/2018SORUS295.
Full textCryptographic integrated circuits may be vulnerable to attacks based on the observation of information leakages conducted during the cryptographic algorithms' executions, the so-called Side-Channel Attacks. Nowadays the presence of several countermeasures may lead to the acquisition of signals which are at the same time highly noisy, forcing an attacker or a security evaluator to exploit statistical models, and highly multi-dimensional, letting hard the estimation of such models. In this thesis we study preprocessing techniques aiming at reducing the dimension of the measured data, and the more general issue of information extraction from highly multi-dimensional signals. The first works concern the application of classical linear feature extractors, such as Principal Component Analysis and Linear Discriminant Analysis. Then we analyse a non-linear generalisation of the latter extractor, obtained through the application of a « Kernel Trick », in order to let such preprocessing effective in presence of masking countermeasures. Finally, further generalising the extraction models, we explore the deep learning methodology, in order to reduce signal preprocessing and automatically extract sensitive information from rough signal. In particular, the application of the Convolutional Neural Network allows us to perform some attacks that remain effective in presence of signal desynchronisation
DESHPANDE, SUSHILENDRA ARUN. "FEATURE EXTRACTION AND INTRA-FEATURE DESIGN ADVISOR FOR SHEET METAL PARTS." University of Cincinnati / OhioLINK, 2003. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1070392705.
Full textAl-Khafaji, Suhad. "Spectral-Spatial Feature Extraction for Hyperspectral Image Matching and Boundary Detection." Thesis, Griffith University, 2020. http://hdl.handle.net/10072/401445.
Full textThesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Info & Comm Tech
Science, Environment, Engineering and Technology
Full Text
Cetin, Melih. "Extraction Of Buildings In Satellite Images." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/3/12611944/index.pdf.
Full textAtar, Neriman. "Video Segmentation Based On Audio Feature Extraction." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/12610397/index.pdf.
Full textspeech&rdquo
, &ldquo
music&rdquo
, &ldquo
crowd&rdquo
and &ldquo
silence&rdquo
. The segments that do not belong to these regions are left as &ldquo
unclassified&rdquo
. For the silence segment detection, a simple threshold comparison method has been done on the short time energy feature of the embedded audio sequence. For the &ldquo
speech&rdquo
, &ldquo
music&rdquo
and &ldquo
crowd&rdquo
segment detection a multiclass classification scheme has been applied. For this purpose, three audio feature set have been formed, one of them is purely MPEG-7 audio features, other is the audio features that is used in [31] the last one is the combination of these two feature sets. For choosing the best feature a histogram comparison method has been used. Audio segmentation system was trained and tested with these feature sets. The evaluation results show that the Feature Set 3 that is the combination of other two feature sets gives better performance for the audio classification system. The output of the classification system is an XML file which contains MPEG-7 audio segment descriptors for the video sequence. An application scenario is given by combining the audio segmentation results with visual analysis results for getting audio-visual video segments.
SUNDHOLM, JOEL. "Feature Extraction for Anomaly Detection inMaritime Trajectories." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-155898.
Full textLim, Suryani. "Feature extraction, browsing and retrieval of images." Monash University, School of Computing and Information Technology, 2005. http://arrow.monash.edu.au/hdl/1959.1/9677.
Full textMashhadi-Farahani, Bahman. "Feature extraction and selection for speech recognition." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/nq38255.pdf.
Full textChilo, José. "Feature extraction for low-frequency signal classification /." Stockholm : Fysik, Physics, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-4661.
Full textTarcin, Serkan. "Fast Feature Extraction From 3d Point Cloud." Master's thesis, METU, 2013. http://etd.lib.metu.edu.tr/upload/12615659/index.pdf.
Full textFahimi, Farshad. "A feature extraction system for human surveillance." Thesis, University of Portsmouth, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.507157.
Full textJastram, Michael Oliver. "Inspection and feature extraction of marine propellers." Thesis, Massachusetts Institute of Technology, 1996. http://hdl.handle.net/1721.1/42632.
Full textJaggi, S. (Seema). "Multiscale geometric feature extraction and object recognition." Thesis, Massachusetts Institute of Technology, 1997. http://hdl.handle.net/1721.1/10764.
Full textIncludes bibliographical references (leaves 138-142).
by Seema Jaggi.
Ph.D.
Jolly, Alistair Duncan. "Feature extraction from millimetre wave radar images." Thesis, University of Central Lancashire, 1992. http://clok.uclan.ac.uk/19034/.
Full textTang, Yu. "Feature Extraction for the Cardiovascular Disease Diagnosis." Thesis, Mittuniversitetet, Avdelningen för informationssystem och -teknologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-33742.
Full textHurley, David J. "Force field feature extraction for ear biometrics." Thesis, University of Southampton, 2001. https://eprints.soton.ac.uk/256792/.
Full textRaffoul, Joseph Naim. "Blob Feature Extraction for Event Detection Cameras." University of Dayton / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1590165017029087.
Full textAkyildiz, Yeliz. "Feature extraction from synthetic aperture radar imagery." Connect to resource, 2000. http://rave.ohiolink.edu/etdc/view.cgi?acc%5Fnum=osu1258651629.
Full textAktaruzzaman, M. "FEATURE EXTRACTION AND CLASSIFICATION THROUGH ENTROPY MEASURES." Doctoral thesis, Università degli Studi di Milano, 2015. http://hdl.handle.net/2434/277947.
Full textAlaei, Fahimeh. "Texture Feature-based Document Image Retrieval." Thesis, Griffith University, 2019. http://hdl.handle.net/10072/385939.
Full textThesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Info & Comm Tech
Science, Environment, Engineering and Technology
Full Text
Masip, David. "Feature extraction in face recognition on the use of internal and external features." Saarbrücken VDM Verlag Dr. Müller, 2005. http://d-nb.info/989265706/04.
Full textJackson, Julie Ann. "Three-Dimensional Feature Models for Synthetic Aperture Radar and Experiments in Feature Extraction." The Ohio State University, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=osu1250608768.
Full textJakeš, Jan. "Visipedia - Embedding-driven Visual Feature Extraction and Learning." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2014. http://www.nusl.cz/ntk/nusl-236120.
Full textGraf, Arnulf B. A. "Classification and feature extraction in man and machine." [S.l. : s.n.], 2004. http://deposit.ddb.de/cgi-bin/dokserv?idn=972533508.
Full textWestin, Carl-Fredrik. "Feature extraction based on a tensor image description." Licentiate thesis, Linköping University, Linköping University, Computer Vision, 1991. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-54888.
Full textFeature extraction from a tensor based local image representation introduced by Knutsson in [37] is discussed. The tensor representation keeps statements of structure, certainty of statement and energy separate. Further processing for obtaining new features also having these three entities separate is achieved by the use of a new concept, tensor field filtering. Tensor filters for smoothing and for extraction of circular symmetries are presented and discussed in particular. These methods are used for corner detection and extraction of more global features such as lines in images. A novel method for grouping local orientation estimates into global line parameters is introduced. The method is based on a new parameter space, the Möbius Strip parameter space, which has similarities to the Hough transform. A local centroid clustering algorithm is used for classification in this space. The procedure automatically divides curves into line segments with appropriate lengths depending on the curvature. A linked list structure is built up for storing data in an efficient way.
Ogiltigt nummer / annan version: I publ. nr 290:s ISBN: 91-7870-815-X.
Tellioglu, Zafer Hasim. "Real Time 3d Surface Feature Extraction On Fpga." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612200/index.pdf.
Full textLorentzon, Matilda. "Feature Extraction for Image Selection Using Machine Learning." Thesis, Linköpings universitet, Datorseende, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-142095.
Full textDai, Guang. "Feature extraction VIA kernel weighted discriminant analysis methods /." View abstract or full-text, 2007. http://library.ust.hk/cgi/db/thesis.pl?CSED%202007%20DAI.
Full textPan, Wendy. "A simulated shape recognition system using feature extraction /." Online version of thesis, 1989. http://hdl.handle.net/1850/10496.
Full textYeu, Yeon. "Linear feature extraction." 2003. http://catalog.hathitrust.org/api/volumes/oclc/54114716.html.
Full textTypescript. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 58-60).
Wang, Wen-Hung, and 王文宏. "Research and Applications on Feature Extraction and Feature MatchingResearch and Applications on Feature Extraction and Feature Matching." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/19133507765828544532.
Full text華梵大學
資訊管理學系碩士班
95
In computer vision, there are lots of difficult problems such as object recognition, 3D reconstruction, object tracking, etc. And the basis of solving the above problems relies on image matching. In the work, we propose to modify the descriptors of SIFT (Scale Invariant Feature Transform) so the resulting invariant feature points can produce better image matching results. The SIFT algorithm can successfully extract the most descriptive feature points in given input images taken under different viewpoints. This work not only modifies the generation of the descriptors but also proposes to employ the Earth Mover's Distance (EMD) as the measurement of dissimilarity between two descriptors. The feature matching process is easily interfered by noise, lighting conditions, etc. We conduct experiments to test whether the proposed method is sensitive to interferences by examining the feature matching accuracy in computing the fundamental matrix.
Hsu, Yu-Cheng, and 許育誠. "Automatic Tongue Feature Extraction." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/28453769138666871838.
Full text國立中山大學
資訊工程學系研究所
98
In recent years, Chinese medicine in the medical profession in the West triggered a wave of new wave. Chinese medicine is based on four examinations which are listening and smelling examination, inquiry, and palpation to diagnose the patient. Tongue diagnosis is also the first of four diagnostic. The result of tongue diagnosis is based on features of tongue which are diagnosed by doctor. Observation of the tongue focuses on the tongue phenomenon which is structured by the shape of the tongue, and the substance of the tongue, and the coating of the tongue. Pathology of the tongue-shaped includes the medium, fat, lean and crooked, etc. In Pathology of the tongue-substanced, tongue color includes pale, closed to pale, reddish, red, dark red, dark purple, also have some features about ecchymosis, breaken line, tooth mark, and red dot. Pathology for the coating of the tongue includes white, yellow, black, greasy, thick, thin, peeling, or no, etc. Clinically, doctors mostly rely on their own knowledge and experience when determining major lesions of a patient by observing the coloration, overall modalities, and volume of salivary on different parts of the tongue. As a result, the diagnosis tends to be limited by knowledge, experience, train of thought, and diagnostic techniques.The subjective determination is likely to be affected by the doctor’s color sensitivity and interpretation.Different doctors may come to drastically different judgments on the same tongue presentation with little overlap. Therefore, it is important to develop scientific methods that can help doctors diagnose based on standardized differentiation procedures and render reliable diagnoses in order to enhance the clinical application value of Chinese Medicine.The computerized automatic capture of characteristics shown on the images of the surface.At first, the captured image achieves brightness and color correction by brightness calibration and color calibration. Then, the original tongue images go through HSI color space conversion, detection of the control points inside and outside the surface of the tongue, curve smoothness modification and active contour model to capture images of the tongue. After that, the tongue shape, tongue fur, tongue body, and body fluid are captured from the image of the tongue.