Dissertations / Theses: 'Cluster analysis Pattern recognition systems'

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Frigui, Hichem. "New approaches for robust clustering and for estimating the optimal number of clusters /." free to MU campus, to others for purchase, 1997. http://wwwlib.umi.com/cr/mo/fullcit?p9842528.

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Zhang, Lin. "PATTERN RECOGNITION METHODS FOR THE ANALYSIS OF INFRARED IMAGING DATA AND MULTIVARIATE CALIBRATION STANDARDIZATION FOR NEAR-INFARED SPECTROSCOPY." Ohio : Ohio University, 2002. http://www.ohiolink.edu/etd/view.cgi?ohiou1013445546.

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Nagaraja, Adarsh. "Feature pruning for action recognition in complex environment." Master's thesis, University of Central Florida, 2011. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/4992.

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A significant number of action recognition research efforts use spatio-temporal interest point detectors for feature extraction. Although the extracted features provide useful information for recognizing actions, a significant number of them contain irrelevant motion and background clutter. In many cases, the extracted features are included as is in the classification pipeline, and sophisticated noise removal techniques are subsequently used to alleviate their effect on classification. We introduce a new action database, created from the Weizmann database, that reveals a significant weakness in systems based on popular cuboid descriptors. Experiments show that introducing complex backgrounds, stationary or dynamic, into the video causes a significant degradation in recognition performance. Moreover, this degradation cannot be fixed by fine-tuning the system or selecting better interest points. Instead, we show that the problem lies at the descriptor level and must be addressed by modifying descriptors.
ID: 030423225; System requirements: World Wide Web browser and PDF reader.; Mode of access: World Wide Web.; Thesis (M.S.)--University of Central Florida, 2011.; Includes bibliographical references (p. 40-41).
M.S.
Masters
Electrical Engineering and Computer Science
Engineering and Computer Science

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Hill, Evelyn June. "Applying statistical and syntactic pattern recognition techniques to the detection of fish in digital images." University of Western Australia. School of Mathematics and Statistics, 2004. http://theses.library.uwa.edu.au/adt-WU2004.0070.

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This study is an attempt to simulate aspects of human visual perception by automating the detection of specific types of objects in digital images. The success of the methods attempted here was measured by how well results of experiments corresponded to what a typical human’s assessment of the data might be. The subject of the study was images of live fish taken underwater by digital video or digital still cameras. It is desirable to be able to automate the processing of such data for efficient stock assessment for fisheries management. In this study some well known statistical pattern classification techniques were tested and new syntactical/ structural pattern recognition techniques were developed. For testing of statistical pattern classification, the pixels belonging to fish were separated from the background pixels and the EM algorithm for Gaussian mixture models was used to locate clusters of pixels. The means and the covariance matrices for the components of the model were used to indicate the location, size and shape of the clusters. Because the number of components in the mixture is unknown, the EM algorithm has to be run a number of times with different numbers of components and then the best model chosen using a model selection criterion. The AIC (Akaike Information Criterion) and the MDL (Minimum Description Length) were tested.The MDL was found to estimate the numbers of clusters of pixels more accurately than the AIC, which tended to overestimate cluster numbers. In order to reduce problems caused by initialisation of the EM algorithm (i.e. starting positions of mixtures and number of mixtures), the Dynamic Cluster Finding algorithm (DCF) was developed (based on the Dog-Rabbit strategy). This algorithm can produce an estimate of the locations and numbers of clusters of pixels. The Dog-Rabbit strategy is based on early studies of learning behaviour in neurons. The main difference between Dog-Rabbit and DCF is that DCF is based on a toroidal topology which removes the tendency of cluster locators to migrate to the centre of mass of the data set and miss clusters near the edges of the image. In the second approach to the problem, data was extracted from the image using an edge detector. The edges from a reference object were compared with the edges from a new image to determine if the object occurred in the new image. In order to compare edges, the edge pixels were first assembled into curves using an UpWrite procedure; then the curves were smoothed by fitting parametric cubic polynomials. Finally the curves were converted to arrays of numbers which represented the signed curvature of the curves at regular intervals. Sets of curves from different images can be compared by comparing the arrays of signed curvature values, as well as the relative orientations and locations of the curves. Discrepancy values were calculated to indicate how well curves and sets of curves matched the reference object. The total length of all matched curves was used to indicate what fraction of the reference object was found in the new image. The curve matching procedure gave results which corresponded well with what a human being being might observe.

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Li, Na. "MMD and Ward criterion in a RKHS : application to Kernel based hierarchical agglomerative clustering." Thesis, Troyes, 2015. http://www.theses.fr/2015TROY0033/document.

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La classification non supervisée consiste à regrouper des objets afin de former des groupes homogènes au sens d’une mesure de similitude. C’est un outil utile pour explorer la structure d’un ensemble de données non étiquetées. Par ailleurs, les méthodes à noyau, introduites initialement dans le cadre supervisé, ont démontré leur intérêt par leur capacité à réaliser des traitements non linéaires des données en limitant la complexité algorithmique. En effet, elles permettent de transformer un problème non linéaire en un problème linéaire dans un espace de plus grande dimension. Dans ce travail, nous proposons un algorithme de classification hiérarchique ascendante utilisant le formalisme des méthodes à noyau. Nous avons tout d’abord recherché des mesures de similitude entre des distributions de probabilité aisément calculables à l’aide de noyaux. Parmi celles-ci, la maximum mean discrepancy a retenu notre attention. Afin de pallier les limites inhérentes à son usage, nous avons proposé une modification qui conduit au critère de Ward, bien connu en classification hiérarchique. Nous avons enfin proposé un algorithme itératif de clustering reposant sur la classification hiérarchique à noyau et permettant d’optimiser le noyau et de déterminer le nombre de classes en présence
Clustering, as a useful tool for unsupervised classification, is the task of grouping objects according to some measured or perceived characteristics of them and it has owned great success in exploring the hidden structure of unlabeled data sets. Kernel-based clustering algorithms have shown great prominence. They provide competitive performance compared with conventional methods owing to their ability of transforming nonlinear problem into linear ones in a higher dimensional feature space. In this work, we propose a Kernel-based Hierarchical Agglomerative Clustering algorithms (KHAC) using Ward’s criterion. Our method is induced by a recently arisen criterion called Maximum Mean Discrepancy (MMD). This criterion has firstly been proposed to measure difference between different distributions and can easily be embedded into a RKHS. Close relationships have been proved between MMD and Ward's criterion. In our KHAC method, selection of the kernel parameter and determination of the number of clusters have been studied, which provide satisfactory performance. Finally an iterative KHAC algorithm is proposed which aims at determining the optimal kernel parameter, giving a meaningful number of clusters and partitioning the data set automatically

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Zhu, Tao. "Extended cluster weighted modeling methods for transient recognition control." Diss., Montana State University, 2006. http://etd.lib.montana.edu/etd/2006/zhu/ZhuT0806.pdf.

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Dannenberg, Matthew. "Pattern Recognition in High-Dimensional Data." Scholarship @ Claremont, 2016. https://scholarship.claremont.edu/hmc_theses/76.

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Vast amounts of data are produced all the time. Yet this data does not easily equate to useful information: extracting information from large amounts of high dimensional data is nontrivial. People are simply drowning in data. A recent and growing source of high-dimensional data is hyperspectral imaging. Hyperspectral images allow for massive amounts of spectral information to be contained in a single image. In this thesis, a robust supervised machine learning algorithm is developed to efficiently perform binary object classification on hyperspectral image data by making use of the geometry of Grassmann manifolds. This algorithm can consistently distinguish between a large range of even very similar materials, returning very accurate classification results with very little training data. When distinguishing between dissimilar locations like crop fields and forests, this algorithm consistently classifies more than 95 percent of points correctly. On more similar materials, more than 80 percent of points are classified correctly. This algorithm will allow for very accurate information to be extracted from these large and complicated hyperspectral images.

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Evans, Fiona H. "Syntactic models with applications in image analysis /." [Perth, W.A.] : [University of W.A.], 2006. http://theses.library.uwa.edu.au/adt-WU2007.0001.

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Dobie, Mark Ralph. "Motion analysis in multimedia systems." Thesis, University of Southampton, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.359240.

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Chang, Charles Chung 1962. "Partial discharge pattern analysis." Monash University, Dept. of Electrical and Computer Systems Engineering, 2001. http://arrow.monash.edu.au/hdl/1959.1/8400.

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Lennartsson, Mattias. "Object Recognition with Cluster Matching." Thesis, Linköping University, Department of Electrical Engineering, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-51494.

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Within this thesis an algorithm for object recognition called Cluster Matching has been developed, implemented and evaluated. The image information is sampled at arbitrary sample points, instead of interest points, and local image features are extracted. These sample points are used as a compact representation of the image data and can quickly be searched for prior known objects. The algorithm is evaluated on a test set of images and the result is surprisingly reliable and time efficient.

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Wu, Zhili. "Kernel based learning methods for pattern and feature analysis." HKBU Institutional Repository, 2004. http://repository.hkbu.edu.hk/etd_ra/619.

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Hobbs, Mike. "Genetic algorithms for spatial data analysis in geographical information systems." Thesis, University of Kent, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.262636.

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Jantan, Adznan Bin. "A comparative study of various analysis techniques for use in speech recognition systems." Thesis, Swansea University, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.292473.

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Hosseini, Habib Mir Mohamad. "Analysis and recognition of Persian and Arabic handwritten characters /." Title page, contents and abstract only, 1997. http://web4.library.adelaide.edu.au/theses/09PH/09phh8288.pdf.

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MICHNIUK, KAROLINA. "PATTERN RECOGNITION APPLIED TO CHART ANALYSIS. EVIDENCE FROM INTRADAY INTERNATIONAL STOCK MARKETS." Doctoral thesis, Universitat Politècnica de València, 2017. http://hdl.handle.net/10251/78837.

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Technical analysis as a sophisticated form of forecasting technique has a varying popularity in the academic and business world. In the past, users were sceptical about technical trading rules and their performance. This is substantiated by the acceptance of the Efficient Market Hypothesis and mixed empirical findings about technical analysis in widely cited studies. The flag pattern is seen as one of the most significant spread chart patterns amongst stock market charting analysts. The present research validates a trading rule based on the further development of flag pattern recognition. The research question concentrates on whether technical analysis applying the flag pattern can outperform international stock markets indices and prove the inefficiency of these markets. The markets observed are represented by the corresponding indices DAX (Germany), DJIA (United States) and IBEX (Spain). The design of the trading rule presents several changes with respect to previous academic works: The wide sample used when considering intraday data, together with the confiuration of some of the variables and the consideration of risk, concludes that the trading rule provides greater positive risk-adjusted returns than the buy-and-hold strategy which is used as a benchmark. The reported positive results strengthen the robustness of the conclusions reached by other researchers.
El análisis técnico es una forma sofisticada de técnica de predicción cuya popularidad ha ido variando en el mundo académico y de los negocios. En el pasado, los usuarios eran bastante escépticos respecto de las reglas técnicas de trading y su performance. Todo esto, se encuentra sustentado por la aceptación de la hipótesis del mercado eficiente y descubrimientos empíricos mixtos sobre el análisis técnico, que se mencionan en un número amplio de estudios. El patrón bandera es visto como uno de los patrones gráficos más significativo y difundido entre los analistas técnicos de mercado. El presente estudio valida una regla de trading basada en el desarrollo futuro del reconocimiento gráfico del patrón bandera. La pregunta de investigación se centra en si el análisis técnico basado en el patrón bandera puede batir los índices internacionales de mercado y probar, de esta manera, la ineficiencia de dichos mercados. Los mercados observados son representados por los correspondientes índices DAX (Alemania), DJIA (Estados Unidos) e IBEX (España). El diseño de la regla de trading presenta varios cambios y novedades con respecto a trabajos académicos previos. La amplia muestra usada al considerar los datos intradía, junto con la configuración de algunas variables y la consideración del riesgo, confirman que la regla de trading proporciona mejores, y más ajustadas al riesgo, rentabilidades positivas que la estrategia de buy-and-hold que se utiliza como referencia. Los resultados positivos corroboran la robustez de las conclusiones a las que también se llegan en otros trabajos.
L'anàlisi tècnica és una forma sofisticada de tècnica de predicció, la popularitat de la qual ha anat variant al món acadèmic i dels negocis. En el passat, els usuaris eren bastant escèptics respecte de les regles tècniques de trading i la seva performance. Tot això, es troba sustentat per l'acceptació de la hipòtesi del mercat eficient i descobriments empírics mixts sobre l'anàlisi tècnica, que s'esmenten en un nombre ampli d'estudis. El patró bandera és vist com un dels patrons gràfics més significatiu i difós entre els analistes tècnics de mercat. El present estudi valida una regla de trading basada en el desenvolupament futur del reconeixement gràfic del patró bandera. La pregunta de recerca se centra en si l'anàlisi tècnica basada en el patró bandera pot batre els índexs internacionals de mercat i provar, d'aquesta manera, la ineficiència d'aquests mercats. Els mercats observats són representats pels corresponents índexs DAX (Alemanya), *DJIA (Estats Units) i IBEX (Espanya). El disseny de la regla de trading presenta diversos canvis i novetats pel que fa a treballs acadèmics previs. L'àmplia mostra usada en considerar les dades intradia, juntament amb la configuració d'algunes variables i la consideració del risc, confirmen que la regla de trading proporciona millors, i més ajustades al risc, rendibilitats positives que l'estratègia de buy-and-hold que s'utilitza com a referència. Els resultats positius corroboren la robustesa de les conclusions a les quals també s'arriben en altres treballs.
Michniuk, K. (2017). PATTERN RECOGNITION APPLIED TO CHART ANALYSIS. EVIDENCE FROM INTRADAY INTERNATIONAL STOCK MARKETS [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/78837
TESIS

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Sukittanon, Somsak. "Modulation scale analysis : theory and application for nonstationary signal classification /." Thesis, Connect to this title online; UW restricted, 2004. http://hdl.handle.net/1773/5875.

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Liu, Chang. "Human motion detection and action recognition." HKBU Institutional Repository, 2010. http://repository.hkbu.edu.hk/etd_ra/1108.

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Djouadi, Abdelhamid. "Analysis of the performance of a parametric and nonparametric classification system : an application to feature selection and extraction in radar target identification /." The Ohio State University, 1987. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487324944214317.

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Zhu, Jia Jun. "A language for financial chart patterns and template-based pattern classification." Thesis, University of Macau, 2018. http://umaclib3.umac.mo/record=b3950603.

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Sidiropoulos, Konstantinos. "Pattern recognition systems design on parallel GPU architectures for breast lesions characterisation employing multimodality images." Thesis, Brunel University, 2014. http://bura.brunel.ac.uk/handle/2438/9190.

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The aim of this research was to address the computational complexity in designing multimodality Computer-Aided Diagnosis (CAD) systems for characterising breast lesions, by harnessing the general purpose computational potential of consumer-level Graphics Processing Units (GPUs) through parallel programming methods. The complexity in designing such systems lies on the increased dimensionality of the problem, due to the multiple imaging modalities involved, on the inherent complexity of optimal design methods for securing high precision, and on assessing the performance of the design prior to deployment in a clinical environment, employing unbiased system evaluation methods. For the purposes of this research, a Pattern Recognition (PR)-system was designed to provide highest possible precision by programming in parallel the multiprocessors of the NVIDIA’s GPU-cards, GeForce 8800GT or 580GTX, and using the CUDA programming framework and C++. The PR-system was built around the Probabilistic Neural Network classifier and its performance was evaluated by a re-substitution method, for estimating the system’s highest accuracy, and by the external cross validation method, for assessing the PR-system’s unbiased accuracy to new, “unseen” by the system, data. Data comprised images of patients with histologically verified (benign or malignant) breast lesions, who underwent both ultrasound (US) and digital mammography (DM). Lesions were outlined on the images by an experienced radiologist, and textural features were calculated. Regarding breast lesion classification, the accuracies for discriminating malignant from benign lesions were, 85.5% using US-features alone, 82.3% employing DM-features alone, and 93.5% combining US and DM features. Mean accuracy to new “unseen” data for the combined US and DM features was 81%. Those classification accuracies were about 10% higher than accuracies achieved on a single CPU, using sequential programming methods, and 150-fold faster. In addition, benign lesions were found smoother, more homogeneous, and containing larger structures. Additionally, the PR-system design was adapted for tackling other medical problems, as a proof of its generalisation. These included classification of rare brain tumours, (achieving 78.6% for overall accuracy (OA) and 73.8% for estimated generalisation accuracy (GA), and accelerating system design 267 times), discrimination of patients with micro-ischemic and multiple sclerosis lesions (90.2% OA and 80% GA with 32-fold design acceleration), classification of normal and pathological knee cartilages (93.2% OA and 89% GA with 257-fold design acceleration), and separation of low from high grade laryngeal cancer cases (93.2% OA and 89% GA, with 130-fold design acceleration). The proposed PR-system improves breast-lesion discrimination accuracy, it may be redesigned on site when new verified data are incorporated in its depository, and it may serve as a second opinion tool in a clinical environment.

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Li, Jun. "Image texture decomposition and application in food quality analysis /." free to MU campus, to others for purchase, 2001. http://wwwlib.umi.com/cr/mo/fullcit?p3036842.

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Hatem, Iyad. "Hybrid multivariate classification technique and its application in tissue image analysis /." free to MU campus, to others for purchase, 2003. http://wwwlib.umi.com/cr/mo/fullcit?p3091929.

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Fredriksson, Tomas, and Rickard Svensson. "Analysis of machine learning for human motion pattern recognition on embedded devices." Thesis, KTH, Mekatronik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-246087.

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With an increased amount of connected devices and the recent surge of artificial intelligence, the two technologies need more attention to fully bloom as a useful tool for creating new and exciting products. As machine learning traditionally is implemented on computers and online servers this thesis explores the possibility to extend machine learning to an embedded environment. This evaluation of existing machine learning in embedded systems with limited processing capa-bilities has been carried out in the specific context of an application involving classification of basic human movements. Previous research and implementations indicate that it is possible with some limitations, this thesis aims to answer which hardware limitation is affecting clas-sification and what classification accuracy the system can reach on an embedded device. The tests included human motion data from an existing dataset and included four different machine learning algorithms on three devices. Support Vector Machine (SVM) are found to be performing best com-pared to CART, Random Forest and AdaBoost. It reached a classification accuracy of 84,69% between six different included motions with a clas-sification time of 16,88 ms per classification on a Cortex M4 processor. This is the same classification accuracy as the one obtained on the host computer with more computational capabilities. Other hardware and machine learning algorithm combinations had a slight decrease in clas-sification accuracy and an increase in classification time. Conclusions could be drawn that memory on the embedded device affect which al-gorithms could be run and the complexity of data that can be extracted in form of features. Processing speed is mostly affecting classification time. Additionally the performance of the machine learning system is connected to the type of data that is to be observed, which means that the performance of different setups differ depending on the use case.
Antalet uppkopplade enheter ökar och det senaste uppsvinget av ar-tificiell intelligens driver forskningen framåt till att kombinera de två teknologierna för att både förbättra existerande produkter och utveckla nya. Maskininlärning är traditionellt sett implementerat på kraftfulla system så därför undersöker den här masteruppsatsen potentialen i att utvidga maskininlärning till att köras på inbyggda system. Den här undersökningen av existerande maskinlärningsalgoritmer, implemen-terade på begränsad hårdvara, har utförts med fokus på att klassificera grundläggande mänskliga rörelser. Tidigare forskning och implemen-tation visar på att det ska vara möjligt med vissa begränsningar. Den här uppsatsen vill svara på vilken hårvarubegränsning som påverkar klassificering mest samt vilken klassificeringsgrad systemet kan nå på den begränsande hårdvaran. Testerna inkluderade mänsklig rörelsedata från ett existerande dataset och inkluderade fyra olika maskininlärningsalgoritmer på tre olika system. SVM presterade bäst i jämförelse med CART, Random Forest och AdaBoost. Den nådde en klassifikationsgrad på 84,69% på de sex inkluderade rörelsetyperna med en klassifikationstid på 16,88 ms per klassificering på en Cortex M processor. Detta är samma klassifikations-grad som en vanlig persondator når med betydligt mer beräknings-resurserresurser. Andra hårdvaru- och algoritm-kombinationer visar en liten minskning i klassificeringsgrad och ökning i klassificeringstid. Slutsatser kan dras att minnet på det inbyggda systemet påverkar vilka algoritmer som kunde köras samt komplexiteten i datan som kunde extraheras i form av attribut (features). Processeringshastighet påverkar mest klassificeringstid. Slutligen är prestandan för maskininlärningsy-stemet bunden till typen av data som ska klassificeras, vilket betyder att olika uppsättningar av algoritmer och hårdvara påverkar prestandan olika beroende på användningsområde.

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Wang, Joshua Kevin. "Identification, Analysis, and Control of Power System Events Using Wide-Area Frequency Measurements." Diss., Virginia Tech, 2009. http://hdl.handle.net/10919/26250.

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The power system has long been operated in a shroud of introspection. Only recently have dynamic, wide-area time synchronized grid measurements brought to light the complex relationships between large machines thousands of miles apart. These measurements are invaluable to understanding the health of the system in real time, for disturbances to the balance between generation and load are manifest in the propagation of electromechanical waves throughout the grid. The global perspective of wide-area measurements provides a platform from which the destructive effects of these disturbances can be avoided. Virginia Tech's distributed network of low voltage frequency monitors, FNET, is able to track these waves as they travel throughout the North American interconnected grids. In contrast to other wide-area measurement systems, the ability to easily measure frequency throughout the grid provides a way to identify, locate, and analyze disturbances with high dynamic accuracy. The unique statistical properties of wide-area measurements require robust tools in order to accurately understand the nature of these events. Expert systems and data conditioning can then be used to quantify the magnitude and location of these disturbances without requiring any knowledge of the system state or topology. Adaptive application of these robust methods form the basis for real-time situational awareness and control. While automated control of the power system rarely utilize wide-area measurements, global insight into grid behavior can only improve disturbance rejection.
Ph. D.

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Kaminskyj, Ian. "Automatic recognition of musical instruments using isolated monophonic sounds." Monash University, Dept. of Electrical and Computer Systems Engineering, 2004. http://arrow.monash.edu.au/hdl/1959.1/5212.

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Cazzanti, Luca. "Generative models of similarity-based classification /." Thesis, Connect to this title online; UW restricted, 2007. http://hdl.handle.net/1773/5905.

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Wang, Yongqiang. "A study on structured covariance modeling approaches to designing compact recognizers of online handwritten Chinese characters." Click to view the E-thesis via HKUTO, 2009. http://sunzi.lib.hku.hk/hkuto/record/B42664305.

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Kasselman, Pieter Retief. "Analysis and design of cryptographic hash functions." Pretoria : [s.n.], 2006. http://upetd.up.ac.za/thesis/available/etd-12202006-125340/.

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Koeppe, Ralf. "A comparative study of the performance of various image analysis methods for dimensional inspection with vision systems." PDXScholar, 1989. https://pdxscholar.library.pdx.edu/open_access_etds/3930.

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Dimensional inspection with Vision Systems requires a careful selection of image analysis methods in order to obtain accurate information about the geometry of the parts to be measured. The purpose of this project is to study, implement and compare different image evaluation methods and to show their strengths and weaknesses with respect to dimensional inspection. Emphasis is made on the inspection of circular features. The criteria of comparison for these methods are discussed. Using synthetically generated images, various analysis methods are compared and conclusions for their use are drawn. Results of the comparison show that the selection of a method has to be done with regard to the noise level of the measurement. Finally, a computationally fast calibration algorithm is studied and implemented .

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Zhu, Manli. "A study of the generalized eigenvalue decomposition in discriminant analysis." Columbus, Ohio : Ohio State University, 2006. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1152133627.

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Hymér, Pontus. "Extraction and Application of Secondary Crease Information in Fingerprint Recognition Systems." Thesis, Linköping University, Department of Science and Technology, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-2951.

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This thesis states that cracks and scars, referred to as Secondary Creases, in fingerprint images can be used as means for aiding and complementing fingerprint recognition, especially in cases where there is not enough clear data to use traditional methods such as minutiae based or correlation techniques. A Gabor filter bank is used to extract areas with linear patterns, where after the Hough Transform is used to identify secondary creases in a r, theta space. The methods proposed for Secondary Crease extraction works well, and provides information about what areas in an image contains usable linear pattern. Methods for comparison is however not as robust, and generates False Rejection Rate at 30% and False Acceptance Rate at 20% on the proposed dataset that consists of bad quality fingerprints. In short, our methods still makes it possible to make use of fingerprint images earlier considered unusable in fingerprint recognition systems.

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Jain, Raja P. "Extraction and interaction analysis of foreground objects in panning video /." Link to online version, 2006. https://ritdml.rit.edu/dspace/handle/1850/1879.

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Kong, Jun. "A Study of Computer Vision and Pattern Recognition in Medical Image Analysis: Digital Microscopy and Optical Coherent Tomography." Columbus, Ohio : Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1228075056.

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Koprnicky, Miroslav. "Towards a Versatile System for the Visual Recognition of Surface Defects." Thesis, University of Waterloo, 2005. http://hdl.handle.net/10012/888.

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Automated visual inspection is an emerging multi-disciplinary field with many challenges; it combines different aspects of computer vision, pattern recognition, automation, and control systems. There does not exist a large body of work dedicated to the design of generalized visual inspection systems; that is, those that might easily be made applicable to different product types. This is an important oversight, in that many improvements in design and implementation times, as well as costs, might be realized with a system that could easily be made to function in different production environments.

This thesis proposes a framework for generalizing and automating the design of the defect classification stage of an automated visual inspection system. It involves using an expandable set of features which are optimized along with the classifier operating on them in order to adapt to the application at hand. The particular implementation explored involves optimizing the feature set in disjoint sets logically grouped by feature type to keep search spaces reasonable. Operator input is kept at a minimum throughout this customization process, since it is limited only to those cases in which the existing feature library cannot adequately delineate the classes at hand, at which time new features (or pools) may have to be introduced by an engineer with experience in the domain.

Two novel methods are put forward which fit well within this framework: cluster-space and hybrid-space classifiers. They are compared in a series of tests against both standard benchmark classifiers, as well as mean and majority vote multi-classifiers, on feature sets comprised of just the logical feature subsets, as well as the entire feature sets formed by their union. The proposed classifiers as well as the benchmarks are optimized with both a progressive combinatorial approach and with an genetic algorithm. Experimentation was performed on true colour industrial lumber defect images, as well as binary hand-written digits.

Based on the experiments conducted in this work, it was found that the sequentially optimized multi hybrid-space methods are capable of matching the performances of the benchmark classifiers on the lumber data, with the exception of the mean-rule multi-classifiers, which dominated most experiments by approximately 3% in classification accuracy. The genetic algorithm optimized hybrid-space multi-classifier achieved best performance however; an accuracy of 79. 2%.

The numeral dataset results were less promising; the proposed methods could not equal benchmark performance. This is probably because the numeral feature-sets were much more conducive to good class separation, with standard benchmark accuracies approaching 95% not uncommon. This indicates that the cluster-space transform inherent to the proposed methods appear to be most useful in highly dependant or confusing feature-spaces, a hypothesis supported by the outstanding performance of the single hybrid-space classifier in the difficult texture feature subspace: 42. 6% accuracy, a 6% increase over the best benchmark performance.

The generalized framework proposed appears promising, because classifier performance over feature sets formed by the union of independently optimized feature subsets regularly met and exceeded those classifiers operating on feature sets formed by the optimization of the feature set in its entirety. This finding corroborates earlier work with similar results [3, 9], and is an aspect of pattern recognition that should be examined further.

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Du, Preez Hercule. "GrailKnights : an automaton mass manipulation package for enhanced pattern analysis." Thesis, Stellenbosch : Stellenbosch University, 2008. http://hdl.handle.net/10019.1/2902.

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Thesis (MSC (Mathematical Sciences. Computer Science))--Stellenbosch University, 2008.
This thesis describes the design and implementation of an application names GrailKnights that allows for the mass manipulation of automata, with added visual pattern analysis features. It comprises a database-driven backend for automata storage, and a graphical user interface that allows for filtering the automata selected from the database with visual interpretation of visible patterns over the resulting automata.

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Minnen, David. "Unsupervised discovery of activity primitives from multivariate sensor data." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/24623.

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Thesis (Ph.D.)--Computing, Georgia Institute of Technology, 2009.
Committee Chair: Thad Starner; Committee Member: Aaron Bobick; Committee Member: Bernt Schiele; Committee Member: Charles Isbell; Committee Member: Irfan Essa

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Rathi, Yogesh. "Filtering for Closed Curves." Diss., Georgia Institute of Technology, 2006. http://hdl.handle.net/1853/13996.

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This thesis deals with the problem of tracking highly deformable objects in the presence of noise, clutter and occlusions. The contributions of this thesis are threefold: A novel technique is proposed to perform filtering on an infinite dimensional space of curves for the purpose of tracking deforming objects. The algorithm combines the advantages of particle filter and geometric active contours to track deformable objects in the presence of noise and clutter. Shape information is quite useful in tracking deformable objects, especially if the objects under consideration get partially occluded. A nonlinear technique to perform shape analysis, called kernelized locally linear embedding, is proposed. Furthermore, a new algebraic method is proposed to compute the pre-image of the projection in the context of kernel PCA. This is further utilized in a parametric method to perform segmentation of medical images in the kernel PCA basis. The above mentioned shape learning methods are then incorporated into a generalized tracking algorithm to provide dynamic shape prior for tracking highly deformable objects. The tracker can also model image information like intensity moments or the output of a feature detector and can handle vector-valued (color) images.

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Shao, Wenbin. "Automatic annotation of digital photos." Access electronically, 2007. http://www.library.uow.edu.au/adt-NWU/public/adt-NWU20080403.120857/index.html.

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Wang, Yongqiang, and 王永強. "A study on structured covariance modeling approaches to designing compact recognizers of online handwritten Chinese characters." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2009. http://hub.hku.hk/bib/B42664305.

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Evans, Fiona H. "Syntactic models with applications in image analysis." University of Western Australia. Dept. of Mathematics and Statistics, 2007. http://theses.library.uwa.edu.au/adt-WU2007.0001.

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[Truncated abstract] The field of pattern recognition aims to develop algorithms and computer programs that can learn patterns from data, where learning encompasses the problems of recognition, representation, classification and prediction. Syntactic pattern recognition recognises that patterns may be hierarchically structured. Formal language theory is an example of a syntactic approach, and is used extensively in computer languages and speech processing. However, the underlying structure of language and speech is strictly one-dimensional. The application of syntactic pattern recognition to the analysis of images requires an extension of formal language theory. Thus, this thesis extends and generalises formal language theory to apply to data that have possibly multi-dimensional underlying structure and also hierarchic structure . . . As in the case for curves, shapes are modelled as a sequence of local relationships between the curves, and these are estimated using a training sample. Syntactic square detection was extremely successful – detecting 100% of squares in images containing only a single square, and over 50% of the squares in images containing ten squares highly likely to be partially or severely occluded. The detection and classification of polygons was successful, despite a tendency for occluded squares and rectangles to be confused. The algorithm also peformed well on real images containing fish. The success of the syntactic approaches for detecting edges, detecting curves and detecting, classifying and counting occluded shapes is evidence of the potential of syntactic models.

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Janmohammadi, Siamak. "Classifying Pairwise Object Interactions: A Trajectory Analytics Approach." Thesis, University of North Texas, 2015. https://digital.library.unt.edu/ark:/67531/metadc801901/.

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We have a huge amount of video data from extensively available surveillance cameras and increasingly growing technology to record the motion of a moving object in the form of trajectory data. With proliferation of location-enabled devices and ongoing growth in smartphone penetration as well as advancements in exploiting image processing techniques, tracking moving objects is more flawlessly achievable. In this work, we explore some domain-independent qualitative and quantitative features in raw trajectory (spatio-temporal) data in videos captured by a fixed single wide-angle view camera sensor in outdoor areas. We study the efficacy of those features in classifying four basic high level actions by employing two supervised learning algorithms and show how each of the features affect the learning algorithms’ overall accuracy as a single factor or confounded with others.

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Le, Faucheur Xavier Jean Maurice. "Statistical methods for feature extraction in shape analysis and bioinformatics." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/33911.

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The presented research explores two different problems of statistical data analysis. In the first part of this thesis, a method for 3D shape representation, compression and smoothing is presented. First, a technique for encoding non-spherical surfaces using second generation wavelet decomposition is described. Second, a novel model is proposed for wavelet-based surface enhancement. This part of the work aims to develop an efficient algorithm for removing irrelevant and noise-like variations from 3D shapes. Surfaces are encoded using second generation wavelets, and the proposed methodology consists of separating noise-like wavelet coefficients from those contributing to the relevant part of the signal. The empirical-based Bayesian models developed in this thesis threshold wavelet coefficients in an adaptive and robust manner. Once thresholding is performed, irrelevant coefficients are removed and the inverse wavelet transform is applied to the clean set of wavelet coefficients. Experimental results show the efficiency of the proposed technique for surface smoothing and compression. The second part of this thesis proposes using a non-parametric clustering method for studying RNA (RiboNucleic Acid) conformations. The local conformation of RNA molecules is an important factor in determining their catalytic and binding properties. RNA conformations can be characterized by a finite set of parameters that define the local arrangement of the molecule in space. Their analysis is particularly difficult due to the large number of degrees of freedom, such as torsion angles and inter-atomic distances among interacting residues. In order to understand and analyze the structural variability of RNA molecules, this work proposes a methodology for detecting repetitive conformational sub-structures along RNA strands. Clusters of similar structures in the conformational space are obtained using a nearest-neighbor search method based on the statistical mechanical Potts model. The proposed technique is a mostly automatic clustering algorithm and may be applied to problems where there is no prior knowledge on the structure of the data space, in contrast to many other clustering techniques. First, results are reported for both single residue conformations- where the parameter set of the data space includes four to seven torsional angles-, and base pair geometries. For both types of data sets, a very good match is observed between the results of the proposed clustering method and other known classifications, with only few exceptions. Second, new results are reported for base stacking geometries. In this case, the proposed classification is validated with respect to specific geometrical constraints, while the content and geometry of the new clusters are fully analyzed.

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Reyes, Estany Miguel. "Human Pose Analysis and Gesture Recognition from Depth Maps: Methods and Applications." Doctoral thesis, Universitat de Barcelona, 2017. http://hdl.handle.net/10803/403985.

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The visual analysis of humans is one of the most active research topics in Computer Vision. Several approaches for body pose recovery have been recently presented, allowing for better generalization of gesture recognition systems. The evaluation of human behaviour patterns in different environments has been a problem studied in social and cognitive sciences, but now it is raised as a challenging approach to computer science because of the complexity of data extraction and its analysis. The main difficulties of visual analysis in n RGB data is the discrimination of shapes, textures, background objects, changes in lighting conditions and viewpoint. In contrast to common RGB images used in Computer Vision, range images provide additional information about the 3-D world, allowing to capture the depth information of each pixel in the image. Furthermore, the use of depth maps is of increasing interest after the advent of cheap multisensor devices based on structured light, or Time of Flight (ToF) technology. In this work we deal with the problem of analyzing human pose and motion in RGB-Depth images, and in particular: 1) human pose recovery, 2) hand pose description, and 3) gesture recognition. We will treated these three areas by using RGB-Depth data in order to take profit from visual representation and 3-D geometric information. Using both channels of information improves the efficiency of human pose and motion analysis methods. We also present efficient use of the proposed methods in real areas of application, such as eHealth and human computer interaction (HCI). Principal objectives are establish the viability of depth map usage in human hand and body pose estimation and, in other hand, for gesture recognition. The presented research is also applied on real high impact applications
El análisis visual de personas es uno de los temas de investigación más activos en Visión Computacional. Varios enfoques para la recuperación de la postura corporal se han presentado recientemente, que permiten una mejor generalización de los sistemas de reconocimiento de gestos. La evaluación de los patrones de comportamiento humano en diferentes ambientes ha sido un problema de estudio en las ciencias sociales y cognitivas, pero actualmente se presenta como un reto para las ciencias informáticas, dada la complejidad de la extracción de datos y su análisis. Entre las principales dificultades del análisis visual de los datos n RGB está la discriminación de las formas, texturas, objetos de fondo, cambios en las condiciones de iluminación y puntos de vista. En contraste con las imágenes RGB comunes utilizadas en Visión Computacional, imágenes de rango aportan información adicional sobre mundo 3-D, lo que permite capturar la información de profundidad de cada pixel en la imagen. Además, el uso de mapas de profundidad es de creciente interés después de la llegada de los dispositivos multisensor baratos basados en luz estructurada, o la tecnología de Tiempo de Vuelo (TOF, por sus siglas en inglés). En este trabajo analizaremos el problema de la postura y el movimiento humano en imágenes RGB con profundidad, y en particular: 1) la actitud humana de recuperación de la postura, 2) descripción de posiciones de la mano, y 3) el reconocimiento de gestos. Vamos a tratar estas tres áreas mediante el uso de los datos RGB-Profundos con el fin de sacar provecho de la representación visual y la información geométrica en 3-D. El uso de los dos canales de información mejora la eficiencia de los métodos de análisis de movimiento y postura humanos. También presentamos un uso eficiente de los métodos propuestos en campos de aplicación real, como la salud y la interacción persona-ordenador (HCI). Nuestros principales objetivos son establecer la viabilidad del uso de mapa de profundidad en la estimación de pose de la mano y el cuerpo humano y, por otro lado, para el reconocimiento de gestos. Adicionalmente se presenta el impacto de éstas en aplicaciones reales con alto impacto social.

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Cheng, Guangchun. "Video Analytics with Spatio-Temporal Characteristics of Activities." Thesis, University of North Texas, 2015. https://digital.library.unt.edu/ark:/67531/metadc799541/.

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As video capturing devices become more ubiquitous from surveillance cameras to smart phones, the demand of automated video analysis is increasing as never before. One obstacle in this process is to efficiently locate where a human operator’s attention should be, and another is to determine the specific types of activities or actions without ambiguity. It is the special interest of this dissertation to locate spatial and temporal regions of interest in videos and to develop a better action representation for video-based activity analysis. This dissertation follows the scheme of “locating then recognizing” activities of interest in videos, i.e., locations of potentially interesting activities are estimated before performing in-depth analysis. Theoretical properties of regions of interest in videos are first exploited, based on which a unifying framework is proposed to locate both spatial and temporal regions of interest with the same settings of parameters. The approach estimates the distribution of motion based on 3D structure tensors, and locates regions of interest according to persistent occurrences of low probability. Two contributions are further made to better represent the actions. The first is to construct a unifying model of spatio-temporal relationships between reusable mid-level actions which bridge low-level pixels and high-level activities. Dense trajectories are clustered to construct mid-level actionlets, and the temporal relationships between actionlets are modeled as Action Graphs based on Allen interval predicates. The second is an effort for a novel and efficient representation of action graphs based on a sparse coding framework. Action graphs are first represented using Laplacian matrices and then decomposed as a linear combination of primitive dictionary items following sparse coding scheme. The optimization is eventually formulated and solved as a determinant maximization problem, and 1-nearest neighbor is used for action classification. The experiments have shown better results than existing approaches for regions-of-interest detection and action recognition.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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Kakarlapudi, Swarna. "APPLICATION OF IMAGE ANALYSIS TECHNIQUES IN FORWARD LOOKING SYNTHETIC VISION SYSTEM INTEGRITY MONITORS." Ohio University / OhioLINK, 2004. http://www.ohiolink.edu/etd/view.cgi?ohiou1090265512.

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Agarwal, Virat. "Algorithm design on multicore processors for massive-data analysis." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/34839.

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Analyzing massive-data sets and streams is computationally very challenging. Data sets in systems biology, network analysis and security use network abstraction to construct large-scale graphs. Graph algorithms such as traversal and search are memory-intensive and typically require very little computation, with access patterns that are irregular and fine-grained. The increasing streaming data rates in various domains such as security, mining, and finance leaves algorithm designers with only a handful of clock cycles (with current general purpose computing technology) to process every incoming byte of data in-core at real-time. This along with increasing complexity of mining patterns and other analytics puts further pressure on already high computational requirement. Processing streaming data in finance comes with an additional constraint to process at low latency, that restricts the algorithm to use common techniques such as batching to obtain high throughput. The primary contributions of this dissertation are the design of novel parallel data analysis algorithms for graph traversal on large-scale graphs, pattern recognition and keyword scanning on massive streaming data, financial market data feed processing and analytics, and data transformation, that capture the machine-independent aspects, to guarantee portability with performance to future processors, with high performance implementations on multicore processors that embed processorspecific optimizations. Our breadth first search graph traversal algorithm demonstrates a capability to process massive graphs with billions of vertices and edges on commodity multicore processors at rates that are competitive with supercomputing results in the recent literature. We also present high performance scalable keyword scanning on streaming data using novel automata compression algorithm, a model of computation based on small software content addressable memories (CAMs) and a unique data layout that forces data re-use and minimizes memory traffic. Using a high-level algorithmic approach to process financial feeds we present a solution that decodes and normalizes option market data at rates an order of magnitude more than the current needs of the market, yet portable and flexible to other feeds in this domain. In this dissertation we discuss in detail algorithm design challenges to process massive-data and present solutions and techniques that we believe can be used and extended to solve future research problems in this domain.

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Oh, Sang Min. "Switching linear dynamic systems with higher-order temporal structure." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/29698.

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Thesis (Ph.D)--Computing, Georgia Institute of Technology, 2010.
Committee Chair: Dellaert, Frank; Committee Co-Chair: Rehg, James; Committee Member: Bobick, Aaron; Committee Member: Essa, Irfan; Committee Member: Smyth, Padhraic. Part of the SMARTech Electronic Thesis and Dissertation Collection.

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Kistner, Melissa. "Image texture analysis for inferential sensing in the process industries." Thesis, Stellenbosch : Stellenbosch University, 2013. http://hdl.handle.net/10019.1/85791.

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Thesis (MScEng)-- Stellenbosch University, 2013.
ENGLISH ABSTRACT: The measurement of key process quality variables is important for the efficient and economical operation of many chemical and mineral processing systems, as these variables can be used in process monitoring and control systems to identify and maintain optimal process conditions. However, in many engineering processes the key quality variables cannot be measured directly with standard sensors. Inferential sensing is the real-time prediction of such variables from other, measurable process variables through some form of model. In vision-based inferential sensing, visual process data in the form of images or video frames are used as input variables to the inferential sensor. This is a suitable approach when the desired process quality variable is correlated with the visual appearance of the process. The inferential sensor model is then based on analysis of the image data. Texture feature extraction is an image analysis approach by which the texture or spatial organisation of pixels in an image can be described. Two texture feature extraction methods, namely the use of grey-level co-occurrence matrices (GLCMs) and wavelet analysis, have predominated in applications of texture analysis to engineering processes. While these two baseline methods are still widely considered to be the best available texture analysis methods, several newer and more advanced methods have since been developed, which have properties that should theoretically provide these methods with some advantages over the baseline methods. Specifically, three advanced texture analysis methods have received much attention in recent machine vision literature, but have not yet been applied extensively to process engineering applications: steerable pyramids, textons and local binary patterns (LBPs). The purpose of this study was to compare the use of advanced image texture analysis methods to baseline texture analysis methods for the prediction of key process quality variables in specific process engineering applications. Three case studies, in which texture is thought to play an important role, were considered: (i) the prediction of platinum grade classes from images of platinum flotation froths, (ii) the prediction of fines fraction classes from images of coal particles on a conveyor belt, and (iii) the prediction of mean particle size classes from images of hydrocyclone underflows. Each of the five texture feature sets were used as inputs to two different classifiers (K-nearest neighbours and discriminant analysis) to predict the output variable classes for each of the three case studies mentioned above. The quality of the features extracted with each method was assessed in a structured manner, based their classification performances after the optimisation of the hyperparameters associated with each method. In the platinum froth flotation case study, steerable pyramids and LBPs significantly outperformed the GLCM, wavelet and texton methods. In the case study of coal fines fractions, the GLCM method was significantly outperformed by all four other methods. Finally, in the hydrocyclone underflow case study, steerable pyramids and LBPs significantly outperformed GLCM and wavelet methods, while the result for textons was inconclusive. Considering all of these results together, the overall conclusion was drawn that two of the three advanced texture feature extraction methods, namely steerable pyramids and LBPs, can extract feature sets of superior quality, when compared to the baseline GLCM and wavelet methods in these three case studies. The application of steerable pyramids and LBPs to further image analysis data sets is therefore recommended as a viable alternative to the traditional GLCM and wavelet texture analysis methods.
AFRIKAANSE OPSOMMING: Die meting van sleutelproseskwaliteitsveranderlikes is belangrik vir die doeltreffende en ekono-miese werking van baie chemiese– en mineraalprosesseringsisteme, aangesien hierdie verander-likes gebruik kan word in prosesmonitering– en beheerstelsels om die optimale prosestoestande te identifiseer en te handhaaf. In baie ingenieursprosesse kan die sleutelproseskwaliteits-veranderlikes egter nie direk met standaard sensors gemeet word nie. Inferensiële waarneming is die intydse voorspelling van sulke veranderlikes vanaf ander, meetbare prosesveranderlikes deur van ‘n model gebruik te maak. In beeldgebaseerde inferensiële waarneming word visuele prosesdata, in die vorm van beelde of videogrepe, gebruik as insetveranderlikes vir die inferensiële sensor. Hierdie is ‘n gepaste benadering wanneer die verlangde proseskwaliteitsveranderlike met die visuele voorkoms van die proses gekorreleer is. Die inferensiële sensormodel word dan gebaseer op die analise van die beelddata. Tekstuurkenmerkekstraksie is ‘n beeldanalisebenadering waarmee die tekstuur of ruimtelike organisering van die beeldelemente beskryf kan word. Twee tekstuurkenmerkekstraksiemetodes, naamlik die gebruik van grysskaalmede-aanwesigheidsmatrikse (GSMMs) en golfie-analise, is sterk verteenwoordig in ingenieursprosestoepassings van tekstuuranalise. Alhoewel hierdie twee grondlynmetodes steeds algemeen as die beste beskikbare tekstuuranalisemetodes beskou word, is daar sedertdien verskeie nuwer en meer gevorderde metodes ontwikkel, wat beskik oor eienskappe wat teoreties voordele vir hierdie metodes teenoor die grondlynmetodes behoort te verskaf. Meer spesifiek is daar drie gevorderde tekstuuranalisemetodes wat baie aandag in onlangse masjienvisieliteratuur geniet het, maar wat nog nie baie op ingenieursprosesse toegepas is nie: stuurbare piramiedes, tekstons en lokale binêre patrone (LBPs). Die doel van hierdie studie was om die gebruik van gevorderde tekstuuranalisemetodes te vergelyk met grondlyntekstuuranaliesemetodes vir die voorspelling van sleutelproseskwaliteits-veranderlikes in spesifieke prosesingenieurstoepassings. Drie gevallestudies, waarin tekstuur ‘n belangrike rol behoort te speel, is ondersoek: (i) die voorspelling van platinumgraadklasse vanaf beelde van platinumflottasieskuime, (ii) die voorspelling van fynfraksieklasse vanaf beelde van steenkoolpartikels op ‘n vervoerband, en (iii) die voorspelling van gemiddelde partikelgrootteklasse vanaf beelde van hidrosikloon ondervloeie. Elk van die vyf tekstuurkenmerkstelle is as insette vir twee verskillende klassifiseerders (K-naaste bure en diskriminantanalise) gebruik om die klasse van die uitsetveranderlikes te voorspeel, vir elk van die drie gevallestudies hierbo genoem. Die kwaliteit van die kenmerke wat deur elke metode ge-ekstraheer is, is op ‘n gestruktureerde manier bepaal, gebaseer op hul klassifikasieprestasie na die optimering van die hiperparameters wat verbonde is aan elke metode. In die platinumskuimflottasiegevallestudie het stuurbare piramiedes en LBPs betekenisvol beter as die GSMM–, golfie– en tekstonmetodes presteer. In die steenkoolfynfraksiegevallestudie het die GSMM-metode betekenisvol slegter as al vier ander metodes presteer. Laastens, in die hidrosikloon ondervloeigevallestudie het stuurbare piramiedes en LBPs betekenisvol beter as die GSMM– en golfiemetodes presteer, terwyl die resultaat vir tekstons nie beslissend was nie. Deur al hierdie resultate gesamentlik te beskou, is die oorkoepelende gevolgtrekking gemaak dat twee van die drie gevorderde tekstuurkenmerkekstraksiemetodes, naamlik stuurbare piramiedes en LBPs, hoër kwaliteit kenmerkstelle kan ekstraheer in vergelyking met die GSMM– en golfiemetodes, vir hierdie drie gevallestudies. Die toepassing van stuurbare piramiedes en LBPs op verdere beeldanalise-datastelle word dus aanbeveel as ‘n lewensvatbare alternatief tot die tradisionele GSMM– en golfietekstuuranalisemetodes.

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Dissertations / Theses on the topic 'Cluster analysis Pattern recognition systems'

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