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Rozprawy doktorskie na temat „Image segmentation”

Autor: Grafiati

Data publikacji: 4 czerwca 2021

Data aktualizacji: 25 lipca 2025

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1

Zeng, Ziming. "Medical image segmentation on multimodality images." Thesis, Aberystwyth University, 2013. http://hdl.handle.net/2160/17cd13c2-067c-451b-8217-70947f89164e.

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Segmentation is a hot issue in the domain of medical image analysis. It has a wide range of applications on medical research. A great many medical image segmentation algorithms have been proposed, and many good segmentation results were obtained. However, due to the noise, density inhomogenity, partial volume effects, and density overlap between normal and abnormal tissues in medical images, the segmentation accuracy and robustness of some state-of-the-art methods still have room for improvement. This thesis aims to deal with the above segmentation problems and improve the segmentation accurac

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Horne, Caspar. "Unsupervised image segmentation /." Lausanne : EPFL, 1991. http://library.epfl.ch/theses/?nr=905.

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Bhalerao, Abhir. "Multiresolution image segmentation." Thesis, University of Warwick, 1991. http://wrap.warwick.ac.uk/60866/.

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Image segmentation is an important area in the general field of image processing and computer vision. It is a fundamental part of the 'low level' aspects of computer vision and has many practical applications such as in medical imaging, industrial automation and satellite imagery. Traditional methods for image segmentation have approached the problem either from localisation in class space using region information, or from localisation in position, using edge or boundary information. More recently, however, attempts have been made to combine both region and boundary information in order to ove

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4

Craske, Simon. "Natural image segmentation." Thesis, University of Bristol, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.266990.

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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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6

Salem, Mohammed Abdel-Megeed Mohammed. "Multiresolution image segmentation." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät II, 2008. http://dx.doi.org/10.18452/15846.

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Systeme der Computer Vision spielen in der Automatisierung vieler Prozesse eine wichtige Rolle. Die wichtigste Aufgabe solcher Systeme ist die Automatisierung des visuellen Erkennungsprozesses und die Extraktion der relevanten Information aus Bildern oder Bildsequenzen. Eine wichtige Komponente dieser Systeme ist die Bildsegmentierung, denn sie bestimmt zu einem großen Teil die Qualitaet des Gesamtsystems. Fuer die Segmentierung von Bildern und Bildsequenzen werden neue Algorithmen vorgeschlagen. Das Konzept der Multiresolution wird als eigenstaendig dargestellt, es existiert unabhaengig vo

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7

Hillman, Peter. "Segmentation of motion picture images and image sequences." Thesis, University of Edinburgh, 2002. http://hdl.handle.net/1842/15026.

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For Motion Picture Special Effects, it is often necessary to take a source image of an actor, segment the actor from the unwanted background, and then composite over a new background. The resultant image appears as if the actor was filmed in front of the new background. The standard approach requires the unwanted background to be a blue or green screen. While this technique is capable of handling areas where the foreground (the actor) blends into the background, the physical requirements present many practical problems. This thesis investigates the possibility of segmenting images where the un

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8

Chowdhury, Md Mahbubul Islam. "Image segmentation for coding." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape3/PQDD_0017/MQ55494.pdf.

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9

Wang, Jingdong. "Graph based image segmentation /." View abstract or full-text, 2007. http://library.ust.hk/cgi/db/thesis.pl?CSED%202007%20WANG.

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10

Linnett, L. M. "Multi-texture image segmentation." Thesis, Heriot-Watt University, 1991. http://hdl.handle.net/10399/856.

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Visual perception of images is closely related to the recognition of the different texture areas within an image. Identifying the boundaries of these regions is an important step in image analysis and image understanding. This thesis presents supervised and unsupervised methods which allow an efficient segmentation of the texture regions within multi-texture images. The features used by the methods are based on a measure of the fractal dimension of surfaces in several directions, which allows the transformation of the image into a set of feature images, however no direct measurement of the fra

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11

Vyas, Aseem. "Medical Image Segmentation by Transferring Ground Truth Segmentation." Thesis, Université d'Ottawa / University of Ottawa, 2015. http://hdl.handle.net/10393/32431.

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The segmentation of medical images is a difficult task due to the inhomogeneous intensity variations that occurs during digital image acquisition, the complicated shape of the object, and the medical expert’s lack of semantic knowledge. Automated segmentation algorithms work well for some medical images, but no algorithm has been general enough to work for all medical images. In practice, most of the time the segmentation results are corrected by the experts before the actual use. In this work, we are motivated to determine how to make use of manually segmented data in automatic segmentation.

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12

Murphy, Sean Daniel. "Medical image segmentation in volumetric CT and MR images." Thesis, University of Glasgow, 2012. http://theses.gla.ac.uk/3816/.

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This portfolio thesis addresses several topics in the field of 3D medical image analysis. Automated methods are used to identify structures and points of interest within the body to aid the radiologist. The automated algorithms presented here incorporate many classical machine learning and imaging techniques, such as image registration, image filtering, supervised classification, unsupervised clustering, morphology and probabilistic modelling. All algorithms are validated against manually collected ground truth. Chapter two presents a novel algorithm for automatically detecting named anatomica

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13

Sharma, Karan. "The Link Between Image Segmentation and Image Recognition." PDXScholar, 2012. https://pdxscholar.library.pdx.edu/open_access_etds/199.

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A long standing debate in computer vision community concerns the link between segmentation and recognition. The question I am trying to answer here is, Does image segmentation as a preprocessing step help image recognition? In spite of a plethora of the literature to the contrary, some authors have suggested that recognition driven by high quality segmentation is the most promising approach in image recognition because the recognition system will see only the relevant features on the object and not see redundant features outside the object (Malisiewicz and Efros 2007; Rabinovich, Vedaldi, and

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14

Lundström, Claes. "Segmentation of Medical Image Volumes." Thesis, Linköping University, Linköping University, Computer Vision, 1997. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-54357.

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<p>Segmentation is a process that separates objects in an image. In medical images, particularly image volumes, the field of application is wide. For example 3D visualisations of the anatomy could benefit enormously from segmentation. The aim of this thesis is to construct a segmentation tool.</p><p>The project consist three main parts. First, a survey of the actual need of segmentation in medical image volumes was carried out. Then a unique three-step model for a segmentation tool was implemented, tested and evaluated.</p><p>The first step of the segmentation tool is a seed-growing method tha

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15

Keshtkar, Abolfazl. "Swarm intelligence-based image segmentation." Thesis, University of Ottawa (Canada), 2007. http://hdl.handle.net/10393/27525.

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One of the major difficulties met in image segmentation lies in the varying degrees of homogeneousness of the different regions in a given image. Hence, it is more efficient to adopt adaptive threshold type methodologies to identify the regions in the images. Throughout the last decade, many image processing tools and techniques have emerged based on the former technology which we called conventional and new technologies such as intelligent-based image processing techniques and algorithm. In some cases, a combination of both technologies is adapted to form a hybrid image processing technique.

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16

Johnson, M. A. "Semantic segmentation and image search." Thesis, University of Cambridge, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.605649.

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Understanding the meaning behind visual data is increasingly important as the quantity of digital images in circulation explodes, and as computing in general and the Internet in specific shifts quickly towards an increasingly visual presentation of data. However, the remarkable amount of variance inside categories (e.g. different kinds of chairs) combined with the occurrence of similarity between categories (e.g. similar breeds of cats and dogs) makes this problem incredibly difficult to solve. In particular, the <i>semantic segmentation</i> of images into contiguous regions of similar interpr

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17

Morgan, Pamela Sheila. "Medical image coding and segmentation :." Thesis, University of Bristol, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.442206.

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18

Tweed, David S. "Motion segmentation across image sequences." Thesis, University of Bristol, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.364960.

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19

O'Connor, Kevin Luke. "Image segmentation through optimal tessellation." Thesis, Imperial College London, 1988. http://hdl.handle.net/10044/1/47210.

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20

O'Donnell, Lauren (Lauren Jean) 1976. "Semi-automatic medical image segmentation." Thesis, Massachusetts Institute of Technology, 2001. http://hdl.handle.net/1721.1/87175.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, February 2002.<br>Includes bibliographical references (leaves 92-96).<br>by Lauren O'Donnell.<br>S.M.

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21

Spencer, Jack A. "Variational methods for image segmentation." Thesis, University of Liverpool, 2016. http://livrepository.liverpool.ac.uk/3003758/.

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The work in this thesis is concerned with variational methods for two-phase segmentation problems. We are interested in both the obtaining of numerical solutions to the partial differential equations arising from the minimisation of a given functional, and forming variational models that tackle some practical problem in segmentation (e.g. incorporating prior knowledge, dealing with intensity inhomogeneity). With that in mind we will discuss each aspect of the work as follows. A seminal two-phase variational segmentation problem in the literature is that of Active Contours Without Edges, introd

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22

Brown, Ryan Charles. "IRIS: Intelligent Roadway Image Segmentation." Thesis, Virginia Tech, 2014. http://hdl.handle.net/10919/49105.

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The problem of roadway navigation and obstacle avoidance for unmanned ground vehicles has typically needed very expensive sensing to operate properly. To reduce the cost of sensing, it is proposed that an algorithm be developed that uses a single visual camera to image the roadway, determine where the lane of travel is in the image, and segment that lane. The algorithm would need to be as accurate as current lane finding algorithms as well as faster than a standard k- means segmentation across the entire image. This algorithm, named IRIS, was developed and tested on several sets of roadway ima

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23

Wu, Qian. "Segmentation-based Retinal Image Analysis." Thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-18524.

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Context. Diabetic retinopathy is the most common cause of new cases of legal blindness in people of working age. Early diagnosis is the key to slowing the progression of the disease, thus preventing blindness. Retinal fundus image is an important basis for judging these retinal diseases. With the development of technology, computer-aided diagnosis is widely used. Objectives. The thesis is to investigate whether there exist specific regions that could assist in better prediction of the retinopathy disease, it means to find the best region in fundus image that works the best in retinopathy class

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24

Pan, Huizhu. "Variational Image Segmentation with Constraints." Thesis, Curtin University, 2020. http://hdl.handle.net/20.500.11937/80866.

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The research of Huizhu Pan addresses the problem of image segmentation with constraints though designing and solving various variational models. A novel constraint term is designed for the use of landmarks in image segmentation. Two region-based segmentation models were proposed where the segmentation contour passes through landmark points. A more stable and memory efficient solution to the self-repelling snakes model, a variational model with the topology preservation constraint, was also designed.

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25

Felhi, Mehdi. "Document image segmentation : content categorization." Thesis, Université de Lorraine, 2014. http://www.theses.fr/2014LORR0109/document.

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Dans cette thèse, nous abordons le problème de la segmentation des images de documents en proposant de nouvelles approches pour la détection et la classification de leurs contenus. Dans un premier lieu, nous étudions le problème de l'estimation d'inclinaison des documents numérisées. Le but de ce travail étant de développer une approche automatique en mesure d'estimer l'angle d'inclinaison du texte dans les images de document. Notre méthode est basée sur la méthode Maximum Gradient Difference (MGD), la R-signature et la transformée de Ridgelets. Nous proposons ensuite une approche hybride pour

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26

Felhi, Mehdi. "Document image segmentation : content categorization." Electronic Thesis or Diss., Université de Lorraine, 2014. http://www.theses.fr/2014LORR0109.

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Dans cette thèse, nous abordons le problème de la segmentation des images de documents en proposant de nouvelles approches pour la détection et la classification de leurs contenus. Dans un premier lieu, nous étudions le problème de l'estimation d'inclinaison des documents numérisées. Le but de ce travail étant de développer une approche automatique en mesure d'estimer l'angle d'inclinaison du texte dans les images de document. Notre méthode est basée sur la méthode Maximum Gradient Difference (MGD), la R-signature et la transformée de Ridgelets. Nous proposons ensuite une approche hybride pour

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27

Toh, Vivian. "Statistical image analysis : length estimation and colour image segmentation." Thesis, University of Strathclyde, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.415373.

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28

Xu, Dongxiang. "Image segmentation and its application on MR image analysis /." Thesis, Connect to this title online; UW restricted, 2001. http://hdl.handle.net/1773/6063.

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29

Muñoz, Pujol Xavier 1976. "Image segmentation integrating colour, texture and boundary information." Doctoral thesis, Universitat de Girona, 2003. http://hdl.handle.net/10803/7719.

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La tesis se centra en la Visión por Computador y, más concretamente, en la segmentación de imágenes, la cual es una de las etapas básicas en el análisis de imágenes y consiste en la división de la imagen en un conjunto de regiones visualmente distintas y uniformes considerando su intensidad, color o textura.<br/>Se propone una estrategia basada en el uso complementario de la información de región y de frontera durante el proceso de segmentación, integración que permite paliar algunos de los problemas básicos de la segmentación tradicional. La información de frontera permite inicialmente identi

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Elmowafy, Osama Mohammed Elsayed. "Image processing systems for TV image tracking." Thesis, University of Kent, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.310164.

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Li, Xiaobing. "Automatic image segmentation based on level set approach: application to brain tumor segmentation in MR images." Reims, 2009. http://theses.univ-reims.fr/exl-doc/GED00001120.pdf.

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L'objectif de la thèse est de développer une segmentation automatique des tumeurs cérébrales à partir de volumes IRM basée sur la technique des « level sets ». Le fonctionnement «automatique» de ce système utilise le fait que le cerveau normal est symétrique et donc la localisation des régions dissymétriques permet d'estimer le contour initial de la tumeur. La première étape concerne le prétraitement qui consiste à corriger l'inhomogénéité de l'intensité du volume IRM et à recaler spatialement les volumes d'IRM d'un même patient à différents instants. Le plan hémisphérique du cerveau est reche

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32

Farnebäck, Gunnar. "Motion-based segmentation of image sequences." Thesis, Linköping University, Linköping University, Computer Vision, 1996. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-54351.

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<p>This Master's Thesis addresses the problem of segmenting an image sequence with respect to the motion in the sequence. As a basis for the motion estimation, 3D orientation tensors are used. The goal of the segmentation is to partition the images into regions, characterized by having a coherent motion. The motion model is affine with respect to the image coordinates. A method to estimate the parameters of the motion model from the orientation tensors in a region is presented. This method can also be generalized to a large class of motion models.</p><p>Two segmentation algorithms are presente

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33

Pichon, Eric. "Novel Methods for Multidimensional Image Segmentation." Diss., Georgia Institute of Technology, 2005. http://hdl.handle.net/1853/7504.

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Artificial vision is the problem of creating systems capable of processing visual information. A fundamental sub-problem of artificial vision is image segmentation, the problem of detecting a structure from a digital image. Examples of segmentation problems include the detection of a road from an aerial photograph or the determination of the boundaries of the brain's ventricles from medical imagery. The extraction of structures allows for subsequent higher-level cognitive tasks. One of them is shape comparison. For example, if the brain ventricles of a patient are segmented, can their shapes b

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34

Ersoy, Ozan. "Image Segmentation With Improved Region Modeling." Master's thesis, METU, 2004. http://etd.lib.metu.edu.tr/upload/12605627/index.pdf.

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Image segmentation is an important research area in digital image processing with several applications in vision-guided autonomous robotics, product quality inspection, medical diagnosis, the analysis of remotely sensed images, etc. The aim of image segmentation can be defined as partitioning an image into homogeneous regions in terms of the features of pixels extracted from the image. Image segmentation methods can be classified into four main categories: 1) clustering methods, 2) region-based methods, 3) hybrid methods, and 4) bayesian methods. In this thesis, major image segmentation metho

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35

Duramaz, Alper. "Image Segmentation Based On Variational Techniques." Master's thesis, METU, 2006. http://etd.lib.metu.edu.tr/upload/12607721/index.pdf.

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Recently, solutions to the problem of image segmentation and denoising are developed based on the Mumford-Shah model. The model provides an energy functional, called the Mumford-Shah functional, which should be minimized. Since the minimization of the functional has some difficulties, approximate approaches are proposed. Two such methods are the gradient flows method and the Chan-Vese active contour method. The performance evolution in terms of speed shows that the gradient flows method converges to the boundaries of the smooth parts faster<br>but for the hierarchical four-phase segmentation,

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Altinoklu, Metin Burak. "Image Segmentation Based On Variational Techniques." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/12610415/index.pdf.

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In this thesis, the image segmentation methods based on the Mumford&amp<br>#8211<br>Shah variational approach have been studied. By obtaining an optimum point of the Mumford-Shah functional which is a piecewise smooth approximate image and a set of edge curves, an image can be decomposed into regions. This piecewise smooth approximate image is smooth inside of regions, but it is allowed to be discontinuous region wise. Unfortunately, because of the irregularity of the Mumford Shah functional, it cannot be directly used for image segmentation. On the other hand, there are several approaches to

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37

Ismaili, Imdad Ali. "Natural image segmentation using colour information." Thesis, Imperial College London, 1996. http://hdl.handle.net/10044/1/8010.

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Tan, Tieniu. "Image texture analysis : classification and segmentation." Thesis, Imperial College London, 1990. http://hdl.handle.net/10044/1/8697.

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Seemann, Torsten 1973. "Digital image processing using local segmentation." Monash University, School of Computer Science and Software Engineering, 2002. http://arrow.monash.edu.au/hdl/1959.1/8055.

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40

Ikonomakis, Nicolaos. "A hybrid colour image segmentation scheme." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape7/PQDD_0006/MQ45988.pdf.

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41

Akhlaghian, Tab Fardin. "Multiresolution scalable image and video segmentation." Access electronically, 2005. http://www.library.uow.edu.au/adt-NWU/public/adt-NWU20060227.100704/index.html.

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42

Ezzati, Majid. "Fast image segmentation using stereo vision." Thesis, McGill University, 1995. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=23258.

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Binocular stereopsis is a biologically motivated approach that uses two slightly different views of a scene to extract information about its three-dimensional properties. The two underlying principles of our approach to stereo vision are local computation of binocular disparities and the use of the resulting disparity map for image segmentation.<br>The cepstrum is used to provide an estimation of binocular disparity between corresponding regions of the stereo image pair. We study the cepstrum and its properties, and suggest improvements to the initial disparity estimation stage. Next a modifie

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43

Gulshan, Varun. "From interactive to semantic image segmentation." Thesis, University of Oxford, 2011. http://ora.ox.ac.uk/objects/uuid:706b648a-e5e7-4334-a456-0f0b5701dbc4.

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This thesis investigates two well defined problems in image segmentation, viz. interactive and semantic image segmentation. Interactive segmentation involves power assisting a user in cutting out objects from an image, whereas semantic segmentation involves partitioning pixels in an image into object categories. We investigate various models and energy formulations for both these problems in this thesis. In order to improve the performance of interactive systems, low level texture features are introduced as a replacement for the more commonly used RGB features. To quantify the improvement obta

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Hasan, Basela Sharif. "Image segmentation using deformable spatial priors." Thesis, University of Leeds, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.590422.

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Image segmentation is one of the main problems that need to be solved as a component procedure in many computer vision tasks such as recognition, image editing, and indexing. Poor quality segmentation results can markedly deteriorate the performance demonstrated by the whole task. Therefore, a great deal of research heeds to the set of segmentation techniques focused on finding high accuracy segmentations. Existing methods tend to exploit low and high level information about the object in a given image. Incorporating shape priors within the MRF formulation were shown to be extremely helpful in

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45

梁志堅 and Chi-kin Leung. "Segmentation based on segmented-image entropy." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1996. http://hub.hku.hk/bib/B31234987.

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46

Heerlein, Danny. "Image segmentation for improvised explosive devices." Thesis, Monterey, California. Naval Postgraduate School, 2012. http://hdl.handle.net/10945/27843.

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This thesis creates algorithms to preprocess colored images in order to segment Improvised Explosive Devices (IEDs). IEDs are usually concealed and camouflaged and therefore more difficult to segment than other objects. We address the increased difficulty with three key contributions: 1) Our algorithm automatically divides a user-defined background area into smaller areas. We generate separate color models for each of these areas to ensure that a color model includes only colors that appear in the same area of the background. 2) We compress each of these complex color models into a statistical

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47

Zahedi, Fariborz. "A systems approach to image segmentation." Thesis, University of Brighton, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.260978.

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48

Meulemans, Peter R. "Hierarchical image sequence analysis and segmentation." Thesis, University of Warwick, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.391881.

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49

Martin, Ian John. "Multi-spectral image segmentation and compression." Thesis, University of Warwick, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.343123.

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50

Wright, Adrian. "Image segmentation using local surface fitting." Thesis, University College London (University of London), 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.394935.

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