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Dissertations / Theses on the topic 'Segmentation texture'

Author: Grafiati

Published: 8 June 2024

Last updated: 31 July 2025

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1

Camilleri, Kenneth P. "Multiresolution texture segmentation." Thesis, University of Surrey, 1999. http://epubs.surrey.ac.uk/843549/.

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The problem of unsupervised texture segmentation was studied and a texture segmentation algorithm was developed making use of the minimum number of prior assumptions. In particular, no prior information about the type of textures, the number of textures and the appropriate scale of analysis for each texture was required. The texture image was analysed by the multiresolution Gabor expansion. The Gabor expansion generates a large number of features for each image and the most suitable feature space for segmentation needs to be determined automatically. The two-point correlation function was used

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2

Reyes-Aldasoro, Constantino Carlos. "Multiresolution volumetric texture segmentation." Thesis, University of Warwick, 2004. http://wrap.warwick.ac.uk/67756/.

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This thesis investigates the segmentation of data in 2D and 3D by texture analysis using Fourier domain filtering. The field of texture analysis is a well-trodden one in 2D, but many applications, such as Medical Imaging, Stratigraphy or Crystallography, would benefit from 3D analysis instead of the traditional, slice-by-slice approach. With the intention of contributing to texture analysis and segmentation in 3D, a multiresolution volumetric texture segmentation (M-VTS) algorithm is presented. The method extracts textural measurements from the Fourier domain of the data via sub-band filtering

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3

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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4

Porter, Robert Mark Stefan. "Texture classification and segmentation." Thesis, University of Bristol, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.389032.

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5

Haddad, Stephen. "Texture measures for segmentation." Master's thesis, University of Cape Town, 2007. http://hdl.handle.net/11427/7461.

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Includes bibliographical references (p. 67-72).<br>Texture is an important visual cue in both human and computer vision. Segmenting images into regions of constant texture is used in many applications. This work surveys a wide range of texture descriptors and segmentation methods to determine the state of the art in texture segmentation. Two types of texture descriptors are investigated: filter bank based methods and local descriptors. Filter banks deconstruct an image into several bands, each of which emphasises areas of the image with different properties. Textons are an adaptive histogram m

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6

Pongratananukul, Nattorn. "Texture Segmentation Using Fractal Features." Honors in the Major Thesis, University of Central Florida, 2000. http://digital.library.ucf.edu/cdm/ref/collection/ETH/id/677.

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This item is only available in print in the UCF Libraries. If this is your Honors Thesis, you can help us make it available online for use by researchers around the world by following the instructions on the distribution consent form at http://library.ucf<br>Bachelors<br>Engineering<br>Electrical Engineering

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7

Wen, Wen. "Computational texture analysis and segmentation." Thesis, University of Strathclyde, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.358812.

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8

Pandit, Sanjay. "Texture segmentation by global optimization." Thesis, University of Surrey, 1999. http://epubs.surrey.ac.uk/843855/.

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This thesis is concerned with the investigation of a specific approach to the problem of texture segmentation, namely that based on the global optimization of a cost function. Many tasks in image analysis are expressed as global optimization problems in which the general issue is to find the global minimum of a cost function which describes the interaction between the different variables modelling the image features and the interaction of these variables with the data in a given problem. The minimization of such a global cost function is a difficult problem since the number of hidden variables

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9

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

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10

Xie, Zhi-Yan. "Multi-scale analysis and texture segmentation." Thesis, University of Oxford, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.260776.

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11

Tania, Sheikh. "Efficient texture descriptors for image segmentation." Thesis, Federation University Australia, 2022. http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/184087.

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Colour and texture are the most common features used in image processing and computer vision applications. Unlike colour, a local texture descriptor needs to express the unique variation pattern in the intensity differences of pixels in the neighbourhood of the pixel-of-interest (POI) so that it can sufficiently discriminate different textures. Since the descriptor needs spatial manipulation of all pixels in the neighbourhood of the POI, approximation of texture impacts not only the computational cost but also the performance of the applications. In this thesis, we aim to develop novel texture

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12

King, Stephen. "A machine vision system for texture segmentation." Thesis, Brunel University, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.310081.

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13

Spann, Michael. "Texture description and segmentation in image processing." Thesis, Aston University, 1985. http://publications.aston.ac.uk/8057/.

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Textured regions in images can be defined as those regions containing a signal which has some measure of randomness. This thesis is concerned with the description of homogeneous texture in terms of a signal model and to develop a means of spatially separating regions of differing texture. A signal model is presented which is based on the assumption that a large class of textures can adequately be represented by their Fourier amplitude spectra only, with the phase spectra modelled by a random process. It is shown that, under mild restrictions, the above model leads to a stationary random proces

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14

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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15

Jobanputra, Rishi. "Preserving Texture Boundaries for SAR Sea Ice Segmentation." Thesis, University of Waterloo, 2004. http://hdl.handle.net/10012/913.

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Texture analysis has been used extensively in the computer-assisted interpretation of SAR sea ice imagery. Provision of maps which distinguish relevant ice types is significant for monitoring global warming and ship navigation. Due to the abundance of SAR imagery available, there exists a need to develop an automated approach for SAR sea ice interpretation. Grey level co-occurrence probability (<i>GLCP</i>) texture features are very popular for SAR sea ice classification. Although these features are used extensively in the literature, they have a tendency to erode and misclassify

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16

Tardif, Pierre-Martin. "Segmentation d'images de texture par des modèles multirésolutions." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape4/PQDD_0021/NQ54032.pdf.

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17

Li, Chang-Tsun. "Unsupervised texture segmentation using multiresolution Markov random fields." Thesis, University of Warwick, 1998. http://wrap.warwick.ac.uk/39307/.

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In this thesis, a multiresolution Markov Random Field (MMRF) model for segmenting textured images without supervision is proposed. Stochastic relaxation labelling is adopted to assign the class label with highest probability to the block (site) being visited. Class information is propagated from low spatial resolution to high spatial resolution, via appropriate modifications to the interaction energies defining the field, to minimise class-position uncertainty. The thesis contains novel ideas presented in Chapter 4 and 5, respectively. In Chapter 4, the Multiresolution Fourier Transform (MFT)

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18

Rzadca, Mark C. "Multivariate granulometry and its application to texture segmentation /." Online version of thesis, 1994. http://hdl.handle.net/1850/12200.

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19

Lumbreras, Ruiz Felipe. "Segmentation, classification and modelization of textures by means of multiresolution decomposition techniques." Doctoral thesis, Universitat Autònoma de Barcelona, 2001. http://hdl.handle.net/10803/3024.

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El análisis de texturas es un área de estudio interesante con suficiente peso específico dentro de los diferentes campos que componen la visión por ordenador. En este trabajo hemos desarrollado métodos específicos para resolver aspectos importantes de dicha área. El primer acercamiento al tema viene de la mano de un problema de segmentación de un tipo de texturas muy concreto como son las imágenes microscópicas de láminas de mármol. Este primer tipo de imágenes se componen de un conjunto de granos cuyas formas y tamaños sirven a los especialistas para identificar, catalogar y determinar el ori

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20

Abou, Merhy Bassel. "Segmentation par analyse de texture de grilles d'occupation probabilistes." Thesis, University of Ottawa (Canada), 2006. http://hdl.handle.net/10393/27322.

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Le concept des grilles d'occupation et des images probabilistes a été introduit à la fin des années quatre-vingt avec le travail d'Alberto Elfes [1][2][3][4] qui se situait dans le contexte de la robotique mobile pour la construction de cartes de l'environnement. Depuis, la recherche s'est principalement concentrée sur la représentation, la fusion de données et la génération des modèles d'occupation. Malgré que ces modèles d'environnements probabilistes soient extrêmement riches en terme de contenu, peu d'efforts ont été investis dans leur traitement et dans l'extraction des données pertinente

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21

Tran, Minh Tue. "Pixel and patch based texture synthesis using image segmentation." University of Western Australia. School of Computer Science and Software Engineering, 2010. http://theses.library.uwa.edu.au/adt-WU2010.0030.

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[Truncated abstract] Texture exists all around us and serves as an important visual cue for the human visual system. Captured within an image, we identify texture by its recognisable visual pattern. It carries extensive information and plays an important role in our interpretation of a visual scene. The subject of this thesis is texture synthesis, which is de ned as the creation of a new texture that shares the fundamental visual characteristics of an existing texture such that the new image and the original are perceptually similar. Textures are used in computer graphics, computer-aided desig

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22

Booth, David M. "Image segmentation on the basis of texture and depth." Thesis, Oxford Brookes University, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.280621.

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23

Schofield, Andrew John. "Neural network models for texture segmentation and target detection." Thesis, Keele University, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.358048.

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24

Baik, Edward H. (Edward Hyeen). "Surface-based segmentation of volume data using texture features." Thesis, Massachusetts Institute of Technology, 1997. http://hdl.handle.net/1721.1/43516.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1997.<br>Includes bibliographical references (p. 117-123).<br>by Edward H. Baik.<br>M.Eng.

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25

Cesmeli, Erdo?an. "Texture- and motion-based image segmentation using oscillatory correlation /." The Ohio State University, 1999. http://rave.ohiolink.edu/etdc/view?acc_num=osu1488187763847638.

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26

Fletcher, Neil David. "Multi-scale texture segmentation of synthetic aperture radar images." Thesis, University of Bath, 2005. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.415766.

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27

Bai, Yoon Ho. "Relative advantage of touch over vision in the exploration of texture." Texas A&M University, 2008. http://hdl.handle.net/1969.1/85999.

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Texture segmentation is an effortless process in scene analysis, yet its mechanisms have not been sufficiently understood. Several theories and algorithms exist for texture discrimination based on vision. These models diverge from one another in algorithmic approaches to address texture imagery using spatial elements and their statistics. Even though there are differences among these approaches, they all begin from the assumption that texture segmentation is a visual task. However, considering that texture is basically a surface property, this assumption can at times be misleading. An interest

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28

Pasáček, Václav. "Segmentace obrazu podle textury." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2012. http://www.nusl.cz/ntk/nusl-236463.

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Image segmentation is an important step in image processing. A traditional way how to segment an image is a texture-based segmentation that uses texture features to describe image texture. In this work, Local Binary Patterns (LBP) are used for image texture representation. Texture feature is a histogram of occurences of LBP codes in a small image window. The work also aims to comparison of results of various modifications of Local Binary Patterns and their usability in the image segmentation which is done by unsupervised clustering of texture features. The Fuzzy C-Means algorithm is finally us

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29

jacquelin, christophe. "Segmentation de textures par algorithmes génétiques." Paris 5, 1996. http://www.theses.fr/1996PA05W072.

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PAttern Fed Objects (PAFOs) are software objects devoted to image segmentation according to texture homogeneity. They live in the two-dimensional world of images with the goals of surviving and proliferating as micro-organisms. 3 A PAFO is made of a chromosome in which texture parameter values that reflect the PAFO's relish for learned textures are coded. During its youth a PAFO is fed with different textures belonging to a coherent set, and is taught to recognize bthe characteristic parameters of this set. To segment an image having an unknown zone distribution, various PAFO are spread over t

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30

Dumoulin, François. "Using texture energy measures for the segmentation of forest scenes." Thesis, University of British Columbia, 1985. http://hdl.handle.net/2429/25736.

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The stratification of forest cover is a basis for most forest management activities. With the development of computer-based image analysis systems, attempts have been made to automate the photo-interpretation process using local spectral signatures. The results have generally failed to meet the classification accuracy of trained humans. To complement the spectral analysis, local texture analysis is proposed. This thesis investigates the potential of a technique based on texture energy measures (Laws 1980) for the segmentation of forest scenes. The technique is tested with a set of two panchro

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31

Arof, H. "Texture classification and segmentation using one dimensional discrete Fourier transforms." Thesis, Swansea University, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.635797.

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This thesis introduces a texture descriptor that is invariant to rotation. The new texture descriptor utilizes the property of the magnitudes of Fourier transform coefficients that do not change with spatial shift of input elements. Since rotating an image by an arbitrary angle does not change pixel intensities in an image but shifts them in circular motion, the notion of producing texture features invariant to rotation using 1-D Fourier transform coefficients can be realized if the relationship between circular motion and spatial shift can be established. By analyzing individual circular neig

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32

Tan, Zhigang, and 譚志剛. "A region merging methodology for color and texture image segmentation." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2009. http://hub.hku.hk/bib/B43224143.

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33

Kylberg, Gustaf. "Automatic Virus Identification using TEM : Image Segmentation and Texture Analysis." Doctoral thesis, Uppsala universitet, Avdelningen för visuell information och interaktion, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-217328.

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Viruses and their morphology have been detected and studied with electron microscopy (EM) since the end of the 1930s. The technique has been vital for the discovery of new viruses and in establishing the virus taxonomy. Today, electron microscopy is an important technique in clinical diagnostics. It both serves as a routine diagnostic technique as well as an essential tool for detecting infectious agents in new and unusual disease outbreaks. The technique does not depend on virus specific targets and can therefore detect any virus present in the sample. New or reemerging viruses can be detecte

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34

Ross, Michael G. (Michael Gregory) 1975. "Exploiting texture-motion duality in optical flow and image segmentation." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/86632.

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35

Pipes, Mark 1979. "Image segmentation by texture discrimination using variations of simulated annealing." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/87291.

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36

Lee, Dong-Cheon. "An adaptive texture segmentation approach for applications in digital photogrammetry /." The Ohio State University, 1997. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487948440824379.

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37

Smith, Mark. "Unsupervised object-based video segmentation using color, texture and motion." Ann Arbor, Mich. : ProQuest, 2008. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3303749.

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Thesis (Ph.D. in Electrical Engineering)--S.M.U.<br>Title from PDF title page (viewed Mar. 16, 2009). Source: Dissertation Abstracts International, Volume: 69-03, Section: B, page: 1850. Adviser: Alireza Khotanzad. Includes bibliographical references.

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Tan, Zhigang. "A region merging methodology for color and texture image segmentation." Click to view the E-thesis via HKUTO, 2009. http://sunzi.lib.hku.hk/hkuto/record/B43224143.

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39

Hidayat, Jefferson Ray Tan. "Texture-boundary detection in real-time." Thesis, University of Canterbury. Computer Science and Software Engineering, 2010. http://hdl.handle.net/10092/4951.

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Boundary detection is an essential first-step for many computer vision applications. In practice, boundary detection is difficult because most images contain texture. Normally, texture-boundary detectors are complex, and so cannot run in real-time. On the other hand, the few texture boundary detectors that do run in real-time leave much to be desired in terms of quality. This thesis proposes two real-time texture-boundary detectors – the Variance Ridge Detector and the Texton Ridge Detector – both of which can detect high-quality texture-boundaries in real-time. The Variance Ridge Detector is

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40

Muzzolini, Russell E. "A volumetric approach to segmentation and texture characterisation of ultrasound images." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/nq24041.pdf.

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41

Celano, Daniel Guiseppe Cyril. "Studies of texture and other methods for segmentation of digital images." Thesis, Royal Holloway, University of London, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.363061.

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42

Kim, Kyu-Heon. "Segmentation of natural texture images using a robust stochastic image model." Thesis, University of Newcastle Upon Tyne, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.307927.

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43

Dahdouh, Sonia. "Filtrage, segmentation et suivi d'images échographiques : applications cliniques." Phd thesis, Université Paris Sud - Paris XI, 2011. http://tel.archives-ouvertes.fr/tel-00647326.

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La réalisation des néphrolithotomies percutanées est essentiellement conditionnée par la qualité dela ponction calicièle préalable. En effet, en cas d'échec de celle-ci, l'intervention ne peut avoir lieu.Réalisée le plus souvent sous échographie, sa qualité est fortement conditionnée par celle du retouréchographique, considéré comme essentiel par la deuxième consultation internationale sur la lithiase pour limiter les saignements consécutifs à l'intervention.L'imagerie échographique est largement plébiscitée en raison de son faible coût, de l'innocuité del'examen, liée à son caractère non inva

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Monteiro-Palagi, Patricia. "Décomposition fréquentielle des textures : caractérisation et segmentation." Grenoble INPG, 1995. http://www.theses.fr/1995INPG0182.

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Le theme de cette etude est l'analyse de la performance des attributs de textures extraits a partir d'une decomposition frequentielle similaire a celle realisee au niveau des premiers etages du cortex visuel. Par cette decomposition, des images sont segmentees en zones homogenes au sens de la texture. Pour analyser les distributions des caracteristiques produites, nous avons mis en place une methodologie d'analyse de donnees utilisant des techniques statistiques, neuronales et de traitement d'images. Les caracteristiques frequentielles sont issues d'une decomposition en multiresolution ou a ch

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Bhatnagar, Kovid. "Liver Segmentation by Geometric and Texture features using Support Vector Machine (LiGTS)." The Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1452224451.

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Long, Zhiling. "Statistical image modeling in the contourlet domain with application to texture segmentation." Diss., Mississippi State : Mississippi State University, 2007. http://library.msstate.edu/etd/show.asp?etd=etd-11082007-161335.

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47

Hudson, Richard Earl. "Semi-Supervised Visual Texture Based Pattern Classification." Case Western Reserve University School of Graduate Studies / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=case1339081444.

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Lu, Chun-Shien, and 呂俊賢. "Applications of Wavelet Transforms on Textured Images: Texture Segmentation and Shape From Texture." Thesis, 1998. http://ndltd.ncl.edu.tw/handle/35138683045742042929.

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博士<br>國立成功大學<br>電機工程學系<br>86<br>This thesis focuses on the applications of wavelet transform to textured imagesin the topics of 2D texture segmentation, shape from texture, and 3D texture segmentation.Textures play an important role in the fields of image processing, pattern recognition, and computer vision.However, there is no universal definition for textures leading to the difficulty in texture related work.As a result, we introduce the wavelet transform as the tool for multiresolution/m

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Tasi, Chang-Der, and 蔡昌德. "Segmentation of Color Texture Images." Thesis, 1994. http://ndltd.ncl.edu.tw/handle/25598642234953593910.

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碩士<br>國立交通大學<br>資訊科學學系<br>82<br>In this thesis, we propose a new method to segment color texture image. This method consists of two main parts, feature selection and image segmentation. For feature selection, The features suggested in this thesis are found to have good descriptions about the characteristics of the given texture images. It is observed that the time needed for evaluating the selected features is shorter than that of the traditional features. As for image segmentation, we use

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Liu, Jyh Feng, and 劉志鋒. "Texture Segmentation using Fractal Analysis." Thesis, 1998. http://ndltd.ncl.edu.tw/handle/76850789946703840775.

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碩士<br>中正理工學院<br>電機工程研究所<br>86<br>Segmenting an image into uniformly textured regions is a prerequisite for many computer vision and image understanding tasks. Texture segmentation can be achieved either by extracting uniform texture regions or detected textural discontinuities ( texture edge detection ) between image regions. In this thesis an unsupervised texture analysis model is proposed. In this model, the implicit self- similarity property of texture is analyzed by employing the fract

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