Dissertations / Theses: 'Computer vision, image processing'

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Hunt, Neil. "Tools for image processing and computer vision." Thesis, University of Aberdeen, 1990. http://digitool.abdn.ac.uk/R?func=search-advanced-go&find_code1=WSN&request1=AAIU025003.

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The thesis describes progress towards the construction of a seeing machine. Currently, we do not understand enough about the task to build more than the simplest computer vision systems; what is understood, however, is that tremendous processing power will surely be involved. I explore the pipelined architecture for vision computers, and I discuss how it can offer both powerful processing and flexibility. I describe a proposed family of VLSI chips based upon such an architecture, each chip performing a specific image processing task. The specialisation of each chip allows high performance to be achieved, and a common pixel interconnect interface on each chip allows them to be connected in arbitrary configurations in order to solve different kinds of computational problems. While such a family of processing components can be assembled in many different ways, a programmable computer offers certain advantages, in that it is possible to change the operation of such a machine very quickly, simply by substituting a different program. I describe a software design tool which attempts to secure the same kind of programmability advantage for exploring applications of the pipelined processors. This design tool simulates complete systems consisting of several of the proposed processing components, in a configuration described by a graphical schematic diagram. A novel time skew simulation technique developed for this application allows coarse grain simulation for efficiency, while preserving the fine grain timing details. Finally, I describe some experiments which have been performed using the tools discussed earlier, showing how the tools can be put to use to handle real problems.

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Arnab, Sylvester. "Python for speech/image processing and computer vision /." Leeds, 2001. http://www.leeds.ac.uk/library/counter2/compstmsc/20002001/arnab.doc.

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Crossley, Simon. "Robust temporal stereo computer vision." Thesis, University of Sheffield, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.327614.

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Fletcher, Gordon James. "Geometrical problems in computer vision." Thesis, University of Liverpool, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.337166.

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Tonelli, Alfredo. "Image Processing e Computer Vision con Python e OpenCV." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/20390/.

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Studio di uno degli ecosistemi software oggi maggiormente utilizzati per Image Processing e Computer Vision. Lo studio affrontato parte dalle discipline di Image Processing e Computer Vision, passando per le principali soluzioni software adottate, tra le quali spiccano il linguaggio di programmazione ad alto livello Python e la libreria di Computer Vision OpenCV, per terminare con esempi pratici di base utili per capire il funzionamento delle tecnologie illustrate.

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Liu, Kexue. "Nonlinear evolutionary PDEs in image processing and computer vision." College Park, Md. : University of Maryland, 2004. http://hdl.handle.net/1903/2034.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2004.
Thesis research directed by: Mathematics. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.

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Liu, Xu. "Computer vision and image processing techniques for mobile applications." College Park, Md.: University of Maryland, 2008. http://hdl.handle.net/1903/8916.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2008.
Thesis research directed by: Dept. of Computer Science. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.

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Clarke, Kelvyn Craig Christopher. "Computer vision for the study of fluid flows." Thesis, University of Bristol, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.336853.

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Mairal, Julien. "Sparse coding for machine learning, image processing and computer vision." Phd thesis, École normale supérieure de Cachan - ENS Cachan, 2010. http://tel.archives-ouvertes.fr/tel-00595312.

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We study in this thesis a particular machine learning approach to represent signals that that consists of modelling data as linear combinations of a few elements from a learned dictionary. It can be viewed as an extension of the classical wavelet framework, whose goal is to design such dictionaries (often orthonormal basis) that are adapted to natural signals. An important success of dictionary learning methods has been their ability to model natural image patches and the performance of image denoising algorithms that it has yielded. We address several open questions related to this framework: How to efficiently optimize the dictionary? How can the model be enriched by adding a structure to the dictionary? Can current image processing tools based on this method be further improved? How should one learn the dictionary when it is used for a different task than signal reconstruction? How can it be used for solving computer vision problems? We answer these questions with a multidisciplinarity approach, using tools from statistical machine learning, convex and stochastic optimization, image and signal processing, computer vision, but also optimization on graphs.

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Ali, Abdulamer T. "Computer vision aided road traffic analysis." Thesis, University of Bristol, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.333953.

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Millman, Michael Peter. "Computer vision for yarn quality inspection." Thesis, Loughborough University, 2000. https://dspace.lboro.ac.uk/2134/34196.

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Structural parameters that determine yarn quality include evenness, hairiness and twist. This thesis applies machine vision techniques to yarn inspection, to determine these parameters in a non-contact manner. Due to the increased costs of such a solution over conventional sensors, the thesis takes a wide look at, and where necessary develops, the potential uses of machine vision for several key aspects of yarn inspection at both low and high speed configurations. Initially, the optimum optical / imaging conditions for yarn imaging are determined by investigating the various factors which degrade a yarn image. The depth of field requirement for imaging yarns is analysed, and various solutions are discussed critically including apodisation, wave front encoding and mechanical guidance. A solution using glass plate guides is proposed, and tested in prototype. The plates enable the correct hair lengths to be seen in the image for long hairs, and also prevent damaging effects on the hairiness definition due to yarn vibration and yarn rotation. The optical system parameters and resolution limits of the yarn image when using guide plates are derived and optimised. The thesis then looks at methods of enhancing the yarn image, using various illumination methods, and incoherent and coherent dark-field imaging.

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Andreadis, Ioannis. "Colour processing for image segmentation and recognition." Thesis, University of Manchester, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.284238.

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Nadadur, Desikachari. "Noise covariance estimation in low-level computer vision /." Thesis, Connect to this title online; UW restricted, 2001. http://hdl.handle.net/1773/5988.

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Fung, Hong Chee. "Image processing & robot positioning." Ohio University / OhioLINK, 1990. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1183490009.

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Ozkaya, Yasar A. "Evaluation of yarn characteristics using computer vision and image processing." Thesis, Loughborough University, 2004. https://dspace.lboro.ac.uk/2134/36038.

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Irregularity, hairiness and twist are among the most important characteristics that define yarn quality. This thesis describes computer vision and image processing techniques developed to evaluate these characteristics. The optical and electronic aspects such as the illumination, lens parameters and aberrations play crucial role on the quality of yam images and on the overall performance of image processing. The depth of field limitation being the most important restraint in yam imaging as well as image distortion in line scan cameras arising from digitisation and yam movement are modelled mathematically and verified through experiments both for front-lit and back-lit illuminations. Various light sources and arrangements are tested and relative advantages and disadvantages are discussed based on the image quality. Known problems in defining the hair-core boundaries and determining the total hairiness from yam images are addressed and image enhancement and processing algorithms developed to overcome these problems are explained. A method to simulate various yam scanning resolution conditions is described. Using this method, the minimum scanning resolution limits to measure the hairiness and irregularity are investigated.

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Li, Yanhua. "Efficient recursive factorization methods for determining structure from motion." Title page, abstract and contents only, 2000. http://web4.library.adelaide.edu.au/theses/09PH/09phl6929.pdf.

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Tordoff, Ben. "Active control of zoom for computer vision." Thesis, University of Oxford, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.270752.

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Draelos, Timothy John 1961. "INTERACTIVE IMAGE SEGMENTATION." Thesis, The University of Arizona, 1987. http://hdl.handle.net/10150/276392.

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Goodenough, Norman John. "A heterogeneous VLSI architecture for real-time image processing." Thesis, University of Sheffield, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.364243.

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Urey, Hakan. "Image acquisition and processing with AC-coupled cameras." Diss., Georgia Institute of Technology, 1997. http://hdl.handle.net/1853/13351.

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Tsai, Ming-Jong. "A new technique for 3-D computer vision." Thesis, University of Liverpool, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.240784.

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Christmas, W. J. "Structural matching in computer vision using probabilistic reasoning." Thesis, University of Surrey, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.308472.

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Gawley, Darren John. "Towards an estimation framework for some problems in computer vision." Title page, abstract and table of contents only, 2004. http://web4.library.adelaide.edu.au/theses/09PH/09phg284.pdf.

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Thesis (Ph.D.)--University of Adelaide, School of Computer Science and Cooperative Research Centre for Sensor Signal and Information Processing, 2004.
"September 2004" Includes bibliographical references (leaves 119-126).

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Rondahl, Thomas. "Face Detection in Digital Imagery Using Computer Vision and Image Processing." Thesis, Umeå universitet, Institutionen för datavetenskap, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-51406.

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By adding a failure fault limit to an existing implementation of a face detection system application and a tolerance limit for detection time, a desired throughput for detected objects could be established. The aim of this thesis was to add an increased detection rate for pro le/partial faces while increasing the stability and run-time of the system. The results were obtained through an empirical analysis of test data which was compared between the implementation done for this thesis and the older implementation. The results showed an increase in detected faces (in low sized images) by 10% while also increasing the number of false-positives by 0.725 detections per average image. In large size image cases, an automatic scaling functionality was added, to decrease detection time and decrease false-negatives. The results indicated a decrease in average detection time from (old implementation) 15 seconds to 2 seconds, while still increasing positive detection with 23%, from an average of 42% to 65%. False-positives were also decreased from 5.8 to 0.2 detections per average image used in test.

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VASCONCELOS, CRISTINA NADER. "IMAGE PROCESSING AND COMPUTER VISION ALGORITHMS FOR GRAPHICS CARDS PARALLEL ARCHITECTURES." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2009. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=13444@1.

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CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO
Diversas tarefas de Visão Computacional (VC) são formadas por operações aritméticas, replicadas sobre grandes volumes de dados. Esta caracterização descreve também as qualidades desejadas ao considerar uma aplicação qualquer como boa candidata a usufruir do crescente poder de processamento do hardware gráfico. Esta tese formula um conjunto de algoritmos de VC sobre representações do conteúdo visual em baixo nível, para serem processados em GPU. Dando suporte às propostas, são apresentadas uma visão geral das GPUs e de padrões de programação paralelos, os quais oferecem blocos construtores para tarefas de visão. Neste sentido, propomos uma definição formal para o padrão de Redução Múltipla e analisamos seu desempenho segundo diferentes fatores de redução e variações para seus arranjos. Apresentamos duas propostas para extração de informações no espaço da imagem em GPU: a MOCT descreve a localização de objetos identificáveis por suas cores em vídeos naturais, enquanto o operador de Redução Regional Múltipla Paralela (MRR) distribui a computação de operadores definidos sobre diferentes regiões de interesse. Como aplicação do MRR, descrevemos a construção em GPU de um Diagrama Centroidal de Voronoi baseado no algoritmo de Lloyd. Tratamos do conteúdo visual em aglomerados de pixels, mais especificamente, em Quadtrees de regiões. Introduzimos a QuadN4tree, um modelo para representação de quadtrees que permite a navegação através do sistema de vizinhança das folhas e alcança custos ótimos no levantamento do conjunto de vizinhas de uma folha. Em seguida, propomos uma aceleração para aplicações baseadas em minimização de energia via corte de grafo, introduzindo uma etapa de pré-processamento que agrupa pixels similares em folhas de uma quadtree, com o objetivo de reduzir o tamanho do grafo sobre o qual o corte mínimo é encontrado. O método proposto é aplicado ao problema de segmentação de imagens naturais com iluminação ativa. Algumas contribuições desta tese, descrevendo formulações paralelas a dados, foram publicadas nos artigos incluídos nos apêndices.
Arithmetically intensive operations, replicated over huge data sets (usually image pixels or scanned data), are an important part of many Computer Vision (CV) tasks, making them good candidates for taking advantage of the processing power of contemporary graphics processing units (GPUs). This thesis formulates a set of CV algorithms that use low level representations of visual content and are tailored for running on GPUs. A general view of GPUs and parallel programming patterns that offers interesting building blocks for CV tasks provides the necessary background for the algorithms. We also propose a formal definition for the Multiple Reduction pattern and evaluate its efficiency according to different reduction factors and layouts. We present two techniques for extracting data from the image space using the GPU: MOCT, a technique for tracking a set of objects identified by their colors from natural videos, and MRR, a technique for distributing the evaluation of a set of operators defined over different regions of interest within an image. As a MRR application we describe a Centroidal Voronoi Diagram construction based on Lloyds algorithm but entirely computed using GPU resources. We also deal with visual content representations as pixel agglomerations, more specifically, as Regional Quadtrees. We introduce the QuadN4tree: a new model for representing quadtree leaves that allows navigation through their neighborhood systems and achieves an optimal cost for the retrieval of neighbor sets. We also propose modifying the setup for CV applications based on energy minimization via graph cuts, introducing a preprocessing step that groups similar pixels into regional quadtree leaves. This modification aims to reduce the size of the graph for which a minimum cut is to be found. We apply our proposed method to the problem of natural image segmentation by active illumination. Published papers detailing some contributions of this dissertation are included as appendixes. They present data-parallel formulations for the CV tasks we describe.

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Gorantla, Lakshmi Anjana Devi. "Development of a Computer Vision and Image Processing Toolbox for Matlab." Thesis, Southern Illinois University at Edwardsville, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10844337.

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Image Processing and Computer Vision (CVIP) applications can be developed and analyzed using the CVIPtools software developed at Southern Illinois University Edwardsville in the CVIP Laboratory under the guidance of Dr. Scott E Umbaugh. The CVIPtools software has been created with the code in the C/C++/C# programming languages. Due to the popularity in engineering applications for Matlab use it was decided to port the CVIPtools libraries functions to Matlab M-files and create a CVIP Toolbox for Matlab.

This work consists of developing, testing, packaging, developing documentation for, and releasing the first version of the Matlab Computer Vision and Image Processing Toolbox. In this there are several steps involved which are described clearly in this research work. The primary aim of thesis work is to create a toolbox which is independent of any other toolboxes in Matlab. CVIPtools has over 200 functions which are written in C, but due to growing demand for Matlab we decided to make the functions available in Matlab. After the toolbox is created, the user can install it and can use the functions in the toolbox as Matlab inbuilt functions. This will make it easy for the user to understand and experiment with different CVIP algorithms.

Initially the toolbox was created writing wrapper functions for the programs written in C through the creation of MEX functions. But later due to problems during testing, it was determined [5] that it would be more suitable to write separate Matlab code, M-files for all the functions and create new toolbox.

The CVIP Toolbox for Matlab is an open source project and is independent of any other toolboxes. Thus, the user can install the toolbox and can use all the functions as Matlab inbuilt functions without the need to purchase any of the other Matlab toolboxes, which is required for other toolboxes of this type. There are 206 functions in this first version of toolbox which are the primary functions for CVIP applications. These functions are arranged according to categories so that it will be easy for the user to understand and search various functions.

The CVIP Toolbox is organized into several folders including CVIP Lab, which allows the user to create any algorithm with the help of functions available in the toolbox. The user can explore by using different functions in the toolbox and varying parameters experimentally to achieve desired results. The skeleton program for lab is in cviplab.m which has a sample function implemented so that the user can see how the sample is executed and can call other functions using the same method.

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Tu, Peter Henry. "Extracting and analysing seismic events using computer vision techniques." Thesis, University of Oxford, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.282329.

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Khendek, Hamid. "Computer optical-vision system for glass measurement and inspection." Thesis, University of Nottingham, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.283378.

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Berriss, William Paul. "Acquisition of skin wound images and measurement of wound healing rate and status using colour image processing." Thesis, University of Reading, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.325218.

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許建平 and Kin-ping Hui. "Computer texture boundary detection based on texton model and neural positive feedback." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1994. http://hub.hku.hk/bib/B42574298.

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Hui, Kin-ping. "Computer texture boundary detection based on texton model and neural positive feedback." [Hong Kong] : The University of Hong Kong, 1994. http://sunzi.lib.hku.hk/hkuto/record/B42574298.

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Mishra, Deependra. "Computer Vision and Image Processing| Development and Optimization of MATLAB CVIP Toolbox." Thesis, Southern Illinois University at Edwardsville, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10807716.

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CVIPtools is a software package for the exploration of computer vision and image processing developed in the Computer Vision and Image Processing Laboratory at Southern Illinois University Edwardsville. Initially, CVIPtools was available to users with a graphical user interface and the CVIPtools C libraries. Here, the CVIPtools C functions are ported to MATLAB, using the MEX interface and compared to implementing the functions directly with MATLAB M-files.

The main purpose of CVIPtools is to offer the students, faculty, researchers, and any user a free and user-friendly tool to explore image processing techniques. With the addition of MATLAB CVIP toolbox, now the users have more freedom to choose an environment to work on image processing problems. Many functions have been implemented and are updated on a regular basis; the library has reached a level of sophistication that makes it suitable for both educational and research purposes.

The main objective of this research is to make MATLAB CVIP toolbox more extensive by adding more CVIPtools library function. And the other goal is to perform the computation time comparison between successfully ported function and MATLAB script to optimize the MATLAB CVIP toolbox for better performance and stability.

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Jagoe, John Roger. "Computer registration and processing of retinal images." Thesis, Imperial College London, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.261761.

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Griffin, A. C. "Computer vision techniques for the automatic interpretation of thermochromic paint." Thesis, University of Surrey, 2001. http://epubs.surrey.ac.uk/844526/.

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The aim of this study is to provide an automatic method for the interpretation of images of objects that are coated with thermal paint. Thermal paint changes colour permanently according to the temperature to which it is heated and can be employed as a temperature gauge where more cumbersome measurement apparatus may not be suitable. Such a gauge requires a means to convert the manifestation of the measurement to the corresponding numerical values. In our case this involves the grouping of ranges of colour together into temperature bands and the extraction of the temperature contours between these bands, a task currently performed by a human operator. This study will demonstrate some success in the automatic interpretation of thermal paints through computer vision approaches. In summary the main contributions of this work are: The demonstration that edge detection is not a useful step. Human operators tend to interpret thermochromic paint not simply by colour matching, but by locating prominent colour change points. We demonstrate why in our opinion this in not necessarily the best step through an exploration of colour edge detection. The development of a feature space model of the paint colour formation based on B-splines and the employment of this within a maximum likelihood estimation scheme [GKWG96],[CSGW97] The development of a paint interpretation method based on a Markov Random Field and Simulated Annealing [GSW+98] Our methods axe applicable to cases of ideal data. We highlight some troublesome paint artefacts that occur in real cases and that hinder interpretation. We discuss possible solutions. Finally we draw conclusions and point to directions for possible future work. Key words: thermochromic paint, maximum likelihood estimate, simulated annealing.

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Gan, Zhifeng. "An object-based approach to image-based rendering." Click to view the E-thesis via HKUTO, 2006. http://sunzi.lib.hku.hk/hkuto/record/B38731836.

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Thornton, Kenneth B. "Accurate image-based 3D object registration and reconstruction /." Thesis, Connect to this title online; UW restricted, 1996. http://hdl.handle.net/1773/5910.

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Lyons, Laura Christine. "An investigation of systematic errors in machine vision hardware." Thesis, Georgia Institute of Technology, 1989. http://hdl.handle.net/1853/16759.

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Luo, Hui. "Knowledge-based image understanding and classification systems for medical image databases." Buffalo, N.Y. : Dept. of Computer Science, State University of New York at Buffalo, 2001. http://www.cse.buffalo.edu/tech%2Dreports/2001%2D07.prn.Z.

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Annavarjula, Vaishnavi. "Computer-Vision Based Retinal Image Analysis for Diagnosis and Treatment." Thesis, Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-14979.

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Context- Vision is one of the five elementary physiologial senses. Vision is enabled via the eye, a very delicate sense organ which is highly susceptible to damage which results in loss of vision. The damage comes in the form of injuries or diseases such as diabetic retinopathy and glaucoma. While it is not possible to predict accidents, predicting the onset of disease in the earliest stages is highly attainable. Owing to the leaps in imaging technology,it is also possible to provide near instant diagnosis by utilizing computer vision and image processing capabilities. Objectives- In this thesis, an algorithm is proposed and implemented to classify images of the retina into healthy or two classes of unhealthy images, i.e, diabetic retinopathy, and glaucoma thus aiding diagnosis. Additionally the algorithm is studied to investigate which image transformation is more feasible in implementation within the scope of this algorithm and which region of retina helps in accurate diagnosis. Methods- An experiment has been designed to facilitate the development of the algorithm. The algorithm is developed in such a way that it can accept all the values of a dataset concurrently and perform both the domain transforms independent of each other. Results- It is found that blood vessels help best in predicting disease associations, with the classifier giving an accuracy of 0.93 and a Cohen’s kappa score of 0.90. Frequency transformed images also presented a accuracy in prediction with 0.93 on blood vessel images and 0.87 on optic disk images. Conclusions- It is concluded that blood vessels from the fundus images after frequency transformation gives the highest accuracy for the algorithm developed when the algorithm is using a bag of visual words and an image category classifier model. Keywords-Image Processing, Machine Learning, Medical Imaging

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Lai, Bing-Chang. "Combining generic programming with vector processing for machine vision." Access electronically, 2005. http://www.library.uow.edu.au/adt-NWU/public/adt-NWU20060221.095043/index.html.

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Sze, Wui-fung. "Robust feature-point based image matching." Click to view the E-thesis via HKUTO, 2006. http://sunzi.lib.hku.hk/hkuto/record/B37153262.

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Sze, Wui-fung, and 施會豐. "Robust feature-point based image matching." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2006. http://hub.hku.hk/bib/B37153262.

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Schreiber, Michael J. "Outdoor tracking using computer vision, xenon strobe illumination and retro-reflective landmarks." Diss., Georgia Institute of Technology, 1996. http://hdl.handle.net/1853/18940.

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Hluchoweckyj, Lydia Theodosia. "Recognising road marking structures for autonomous vehicle navigation using computer vision." Thesis, University of Bristol, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.357691.

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Moustafa, Moustafa Abdel-Azim. "Feature recognition using a fast radon transform-based computer vision system." Thesis, Cranfield University, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.305610.

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Crespo, José. "Morphological connected filters and intra-region smoothing for image segmentation." Diss., Georgia Institute of Technology, 1993. http://hdl.handle.net/1853/15771.

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Ng, Ee Sin. "Image feature matching using pairwise spatial constraints." Thesis, University of Cambridge, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.610418.

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Billings, Rachel Mae. "On Efficient Computer Vision Applications for Neural Networks." Thesis, Virginia Tech, 2021. http://hdl.handle.net/10919/102957.

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Since approximately the dawn of the new millennium, neural networks and other machine learning algorithms have become increasingly capable of adeptly performing difficult, dull, and dangerous work conventionally carried out by humans in times of old. As these algorithms become steadily more commonplace in everyday consumer and industry applications, the consideration of how they may be implemented on constrained hardware systems such as smartphones and Internet-of-Things (IoT) peripheral devices in a time- and power- efficient manner while also understanding the scenarios in which they fail is of increasing importance. This work investigates implementations of convolutional neural networks specifically in the context of image inference tasks. Three areas are analyzed: (1) a time- and power-efficient face recognition framework, (2) the development of a COVID-19-related mask classification system suitable for deployment on low-cost, low-power devices, and (3) an investigation into the implementation of spiking neural networks on mobile hardware and their conversion from traditional neural network architectures.
Master of Science
The subject of machine learning and its associated jargon have become ubiquitous in the past decade as industries seek to develop automated tools and applications and researchers continue to develop new methods for artificial intelligence and improve upon existing ones. Neural networks are a type of machine learning algorithm that can make predictions in complex situations based on input data with human-like (or better) accuracy. Real-time, low-power, and low-cost systems using these algorithms are increasingly used in consumer and industry applications, often improving the efficiency of completing mundane and hazardous tasks traditionally performed by humans. The focus of this work is (1) to explore when and why neural networks may make incorrect decisions in the domain of image-based prediction tasks, (2) the demonstration of a low-power, low-cost machine learning use case using a mask recognition system intended to be suitable for deployment in support of COVID-19-related mask regulations, and (3) the investigation of how neural networks may be implemented on resource-limited technology in an efficient manner using an emerging form of computing.

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49

Barman, Roderick A. "A nonlinear switched-capacitor network for edge detection in early vision." Thesis, University of British Columbia, 1990. http://hdl.handle.net/2429/29460.

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A nonlinear switched-capacitor (SC) network for solving the early vision variational problem of edge detection has been designed and constructed using standard SC techniques and a novel nonlinear externally controlled SC resistive element. This new SC element allows, to a limited extent, the form of the variational problem to be "programmable". This allows nonconvex variational problems to be solved by the network using continuation-type methods. Appropriately designed SC networks are guaranteed to converge to a locally stable steady-state. As well, SC networks offer increased accuracy over analog networks composed of nonlinear resistances built from multiple MOSFETs. The operation of the network was analyzed and found to be equivalent to the numerical analysis minimization algorithm of gradient descent. The network's capabilities were demonstrated by "programming" the network to perform the graduated nonconvexity algorithm. A high-level functional network simulation was used to verify the correct operation of the GNC algorithm. A one-dimensional six node CMOS VLSI test chip was designed, simulated and submitted for fabrication.
Applied Science, Faculty of
Electrical and Computer Engineering, Department of
Graduate

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50

Xu, Yongchao. "Tree-based shape spaces : definition and applications in image processing and computer vision." Phd thesis, Université Paris-Est, 2013. http://tel.archives-ouvertes.fr/tel-00981623.

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In a large number of applications, the processing relies on objects or area of interests, and the pixel-based image representation is notwell adapted. These applications would benefit from a region-based processing. Early examples of region-based processing can be found in the area of image segmentation, such as the quad tree. Recently, in mathematical morphology, the connected operators have received much attention. They are region-based filtering tools that act by merging flat zones. They have good contour preservation properties in the sense that they do not create any new boundaries, neither do they shift the existing ones. One popular implementation for connected operators relies on tree-based image representations, notably threshold decomposition representations and hierarchical representations. Those tree-based image representations are widely used in many image processing and computer vision applications. Tree-based connected operators consist in constructing a set of nested or disjoint connected components, followed by a filtering of these connected components based on an attribute function characterizing each connected component. Finally, the filtered image is reconstructed from the simplified tree composed of the remaining connected components. In the work presented in this thesis, we propose to expand the ideas of tree-based connected operators. We introduce the notion of tree-based shape spaces, built from tree-based image representations. Many state-of-the-art methods relying on tree-based image representations consist of analyzing this shape space. A first consequence of this change of point of view is our proposition of a local feature detector, called the tree-based Morse regions (TBMR). It can be seen as a variant of the MSER method. The selection of TBMRs is based on topological information, and hence it extracts the regions independently of the contrast, which makes it truly contrast invariant and quasi parameters free. The accuracy and robustness of the TBMR approach are demonstrated by the repeatability test and by applications to image registration and 3D reconstruction, as compared to some state-of-the-art methods. The basic idea of the main proposition in this thesis is to apply connected filters on the shape space. Such a processing is called the framework of shape-based morphology. It is a versatile framework that deals with region-based image representations. It has three main consequences. 1) For filtering purpose, it is a generalization of the existing tree-based connected operators. Indeed, the framework encompasses classical existing connected operators by attributes. Besides, It also allows us to propose two classes of novel connected operators: shape-based lower/upper levelings and shapings. 2) This framework can be used to object detection/segmentation by selecting relevant points in the shape space. 3) We can also use this framework to transform the hierarchies using the extinction values, so that a hierarchical simplification or segmentation is obtained. Some applications are developed using the framework of shape-based morphology to demonstrate its usefulness. The applications of the shape-based filtering to retinal image analysis show that a mere filtering step that we compare to more evolved processings, achieves state-of-the-art results. An efficient shaping used for image simplification is proposed by minimizing Mumford-Shah functional subordinated to the topographic map. For object detection/segmentation, we proposed a context-based energy estimator that is suitable to characterize object meaningfulness. Last, we extend the hierarchy of constrained connectivity using the aspect of hierarchy transformation of constrained connectivity using the aspect ofhierarchy transformation.

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Dissertations / Theses on the topic 'Computer vision, image processing'

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