Tesis sobre el tema "Segmentation texture"
Crea una cita precisa en los estilos APA, MLA, Chicago, Harvard y otros
Consulte los 50 mejores tesis para su investigación sobre el tema "Segmentation texture".
Junto a cada fuente en la lista de referencias hay un botón "Agregar a la bibliografía". Pulsa este botón, y generaremos automáticamente la referencia bibliográfica para la obra elegida en el estilo de cita que necesites: APA, MLA, Harvard, Vancouver, Chicago, etc.
También puede descargar el texto completo de la publicación académica en formato pdf y leer en línea su resumen siempre que esté disponible en los metadatos.
Explore tesis sobre una amplia variedad de disciplinas y organice su bibliografía correctamente.
Camilleri, Kenneth P. "Multiresolution texture segmentation". Thesis, University of Surrey, 1999. http://epubs.surrey.ac.uk/843549/.
Texto completoReyes-Aldasoro, Constantino Carlos. "Multiresolution volumetric texture segmentation". Thesis, University of Warwick, 2004. http://wrap.warwick.ac.uk/67756/.
Texto completoLinnett, L. M. "Multi-texture image segmentation". Thesis, Heriot-Watt University, 1991. http://hdl.handle.net/10399/856.
Texto completoPorter, Robert Mark Stefan. "Texture classification and segmentation". Thesis, University of Bristol, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.389032.
Texto completoHaddad, Stephen. "Texture measures for segmentation". Master's thesis, University of Cape Town, 2007. http://hdl.handle.net/11427/7461.
Texto completoTexture 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 method which describes the distribution of typical feature vectors. Local descriptors calculate features from smaller neighbourhoods than filter banks. Some local descriptors calculate a scale for their local neighbourhood to achieve scale invariance. Both local and global segmentation methods are investigated. Local segmentation methods consider each pixel in isolation. Global segmentation methods penalise jagged borders or fragmented regions in the segmentation. Pixel labelling and border detection methods are investigated. Methods for measuring the accuracy of segmentation are discussed. Two data sets are used to test the texture segmentation algorithms. The Brodatz Album mosaics are composed of grayscale texture images from the Brodatz Album. The Berkeley Natural Images data set has 300 colour images of natural scenes. The tests show that, of the descriptors tested, filter bank based textons are the best texture descriptors for grayscale images. Local image patch textons are best for colour images. Graph cut segmentation is best for pixel labelling problems and edge detection with regular borders. Non-maxima suppression is best for edge detection with irregular borders. Factors affecting the performance of the algorithms are investigated.
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.
Texto completoBachelors
Engineering
Electrical Engineering
Wen, Wen. "Computational texture analysis and segmentation". Thesis, University of Strathclyde, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.358812.
Texto completoPandit, Sanjay. "Texture segmentation by global optimization". Thesis, University of Surrey, 1999. http://epubs.surrey.ac.uk/843855/.
Texto completoTan, Tieniu. "Image texture analysis : classification and segmentation". Thesis, Imperial College London, 1990. http://hdl.handle.net/10044/1/8697.
Texto completoXie, Zhi-Yan. "Multi-scale analysis and texture segmentation". Thesis, University of Oxford, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.260776.
Texto completoTania, Sheikh. "Efficient texture descriptors for image segmentation". Thesis, Federation University Australia, 2022. http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/184087.
Texto completoDoctor of Philosophy
King, Stephen. "A machine vision system for texture segmentation". Thesis, Brunel University, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.310081.
Texto completoSpann, Michael. "Texture description and segmentation in image processing". Thesis, Aston University, 1985. http://publications.aston.ac.uk/8057/.
Texto completoMuñoz, Pujol Xavier 1976. "Image segmentation integrating colour, texture and boundary information". Doctoral thesis, Universitat de Girona, 2003. http://hdl.handle.net/10803/7719.
Texto completoSe 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 identificar el número de regiones presentes en la imagen y colocar en el interior de cada una de ellas una semilla, con el objetivo de modelar estadísticamente las características de las regiones y definir de esta forma la información de región. Esta información, conjuntamente con la información de frontera, es utilizada en la definición de una función de energía que expresa las propiedades requeridas a la segmentación deseada: uniformidad en el interior de las regiones y contraste con las regiones vecinas en los límites. Un conjunto de regiones activas inician entonces su crecimiento, compitiendo por los píxeles de la imagen, con el objetivo de optimizar la función de energía o, en otras palabras, encontrar la segmentación que mejor se adecua a los requerimientos exprsados en dicha función. Finalmente, todo esta proceso ha sido considerado en una estructura piramidal, lo que nos permite refinar progresivamente el resultado de la segmentación y mejorar su coste computacional.
La estrategia ha sido extendida al problema de segmentación de texturas, lo que implica algunas consideraciones básicas como el modelaje de las regiones a partir de un conjunto de características de textura y la extracción de la información de frontera cuando la textura es presente en la imagen.
Finalmente, se ha llevado a cabo la extensión a la segmentación de imágenes teniendo en cuenta las propiedades de color y textura. En este sentido, el uso conjunto de técnicas no-paramétricas de estimación de la función de densidad para la descripción del color, y de características textuales basadas en la matriz de co-ocurrencia, ha sido propuesto para modelar adecuadamente y de forma completa las regiones de la imagen.
La propuesta ha sido evaluada de forma objetiva y comparada con distintas técnicas de integración utilizando imágenes sintéticas. Además, se han incluido experimentos con imágenes reales con resultados muy positivos.
Image segmentation is an important research area in computer vision and many segmentation methods have been proposed. However, elemental segmentation techniques based on boundary or region approaches often fail to produce accurate segmentation results. Hence, in the last few years, there has been a tendency towards the integration of both techniques in order to improve the results by taking into account the complementary nature of such information. This thesis proposes a solution to the image segmentation integrating region and boundary information. Moreover, the method is extended to texture and colour texture segmentation.
An exhaustive analysis of image segmentation techniques which integrate region and boundary information is carried out. Main strategies to perform the integration are identified and a classification of these approaches is proposed. Thus, the most relevant proposals are assorted and grouped in their corresponding approach. Moreover, characteristics of these strategies as well as the general lack of attention that is given to the texture is noted. The discussion of these aspects has been the origin of all the work evolved in this thesis, giving rise to two basic conclusions: first, the possibility of fusing several approaches to the integration of both information sources, and second, the necessity of a specific treatment for textured images.
Next, an unsupervised segmentation strategy which integrates region and boundary information and incorporates three different approaches identified in the previous review is proposed. Specifically, the proposed image segmentation method combines the guidance of seed placement, the control of decision criterion and the boundary refinement approaches. The method is composed by two basic stages: initialisation and segmentation. Thus, in the first stage, the main contours of the image are used to identify the different regions present in the image and to adequately place a seed for each one in order to statistically model the region. Then, the segmentation stage is performed based on the active region model which allows us to take region and boundary information into account in order to segment the whole image. Specifically, regions start to shrink and expand guided by the optimisation of an energy function that ensures homogeneity properties inside regions and the presence of real edges at boundaries. Furthermore, with the aim of imitating the Human Vision System when a person is slowly approaching to a distant object, a pyramidal structure is considered. Hence, the method has been designed on a pyramidal representation which allows us to refine the region boundaries from a coarse to a fine resolution, and ensuring noise robustness as well as computation efficiency.
The proposed segmentation strategy is then adapted to solve the problem of texture and colour texture segmentation. First, the proposed strategy is extended to texture segmentation which involves some considerations as the region modelling and the extraction of texture boundary information. Next, a method to integrate colour and textural properties is proposed, which is based on the use of texture descriptors and the estimation of colour behaviour by using non-parametric techniques of density estimation. Hence, the proposed strategy of segmentation is considered for the segmentation taking both colour and textural properties into account.
Finally, the proposal of image segmentation strategy is objectively evaluated and then compared with some other relevant algorithms corresponding to the different strategies of region and boundary integration. Moreover, an evaluation of the segmentation results obtained on colour texture segmentation is performed. Furthermore, results on a wide set of real images are shown and discussed.
Jobanputra, Rishi. "Preserving Texture Boundaries for SAR Sea Ice Segmentation". Thesis, University of Waterloo, 2004. http://hdl.handle.net/10012/913.
Texto completoTardif, 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.
Texto completoLi, Chang-Tsun. "Unsupervised texture segmentation using multiresolution Markov random fields". Thesis, University of Warwick, 1998. http://wrap.warwick.ac.uk/39307/.
Texto completoRzadca, Mark C. "Multivariate granulometry and its application to texture segmentation /". Online version of thesis, 1994. http://hdl.handle.net/1850/12200.
Texto completoLumbreras, 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.
Texto completoEl segundo problema que hemos atacado en este trabajo es la clasificación de imágenes de textura. En él también hemos utilizado técnicas multirresolución como base para su resolución. A diferencia de otros enfoques de carácter global que encontramos extensamente en la literatura sobre texturas, nos hemos centrado en problemas donde las diferencias visuales entre las clases de dichas texturas son muy pequeñas. Y puesto que no hemos establecido restricciones fuertes en este análisis, las estrategias desarrolladas son aplicables a un extenso espectro de texturas, como pueden ser las baldosas cerámicas, las imágenes microscópicas de pigmentos de efecto, etc.
El enfoque que hemos seguido para la clasificación de texturas implica la consecución de una serie de pasos. Hemos centrado nuestra atención en aquellos pasos asociados con las primeras etapas del proceso requeridas para identificar las características importantes que definen la textura, mientras que la clasificación final de las muestras ha sido realizada mediante métodos de clasificación generales. Para abordar estos primeros pasos dentro del análisis hemos desarrollado una estrategia mediante la cual las características de una imagen se ajustan a un modelo que previamente hemos definido, uno de entre varios modelos que están ordenados por complejidad. Estos modelos están asociados a algoritmos específicos y sus parámetros así como a los cálculos que de ellos se derivan. Eligiendo el modelo adecuado, por tanto, evitamos realizar cálculos que no nos aportan información útil para la clasificación.
En un tercer enfoque hemos querido llegar a una descripción de textura que nos permita de forma sencilla su clasificación y su síntesis. Para conseguir este objetivo hemos adoptado por un modelo probabilístico. Dicha descripción de la textura nos permitirá la clasificación a través de la comparación directa de modelos, y también podremos, a partir del modelo probabilístico, sintetizar nuevas imágenes.
Para finalizar, comentar que en las dos líneas de trabajo que hemos expuesto, la segmentación y la clasificación de texturas, hemos llegado a soluciones prácticas que han sido evaluadas sobre problemas reales con éxito y además las metodologías propuestas permiten una fácil extensión o adaptación a nuevos casos. Como líneas futuras asociadas a estos temas trataremos por un lado de adaptar la segmentación a imágenes que poco o nada tienen que ver con las texturas, en las que se perseguirá la detección de sujetos y objetos dentro de escenas, como apuntamos más adelante en esta misma memoria. Por otro lado, y relacionado con la clasificación, abordaremos un problema todavía sin solución como es el de la ingeniería inversa en pigmentos de efecto, en otras palabras la determinación de los constituyentes en pinturas metalizadas, y en el que utilizaremos los estudios aquí presentados como base para llegar a una posible solución.
An interesting problem in computer vision is the analysis of texture images. In this work, we have developed specific methods to solve important aspects of this problem. The first approach involves segmentation of a specific type of textures, i.e. those of microscopy images of thin marble sections. These images comprise a pattern of grains whose sizes and shapes help specialists to identify the origin and quality of marble samples. To identify and analyze individual grains in these images represents a problem of image segmentation. In essence, this involves identifying boundary lines represented by valleys which separate flat areas corresponding to grains. Of several methods tested, we found those based on mathematical morphology particularly successful for segmentation of petrographical images. This involves a pre-filtering step for which again several approaches have been explored, including multiresolution algorithms based on wavelets.
In the second approach we have also used multiresolution analyses to address the problem of classifying texture images. In contrast to more global approaches found in the literature, we have explored situations where visual differences between textures are rather subtle. Since we have tried to impose relatively few restrictions on these analyses, we have developed strategies that are applicable to a wide range of related texture images, such as images of ceramic tiles, microscopic images of effect pigments, etc.
The approach we have used for the classification of texture images involves several technical steps. We have focused our attention in the initial low-level analyses required to identify the general features of the image, whereas the final classification of samples has been performed using generic classification methods. To address the early steps of image analysis, we have developed a strategy whereby the general features of the image fit one of several pre-defined models with increasing levels of complexity. These models are associated to specific algorithms, parameters and calculations for the analysis of the image, thus avoiding calculations that do not provide useful information.
Finally, in a third approach we want to arrive to a description of textures in such a way that it should be able to classify and synthesize textures. To reach this goal we adopt a probabilistic model of the texture. This description of the texture allows us to compare textures through comparison of probabilistic models, and also use those probabilities to generate new similar images.
In conclusion, we have developed strategies of segmentation and classification of textures that provide solutions to practical problems and are potentially applicable with minor modifications to a wide range of situations. Future research will explore (i) the possibility of adapting segmentation to the analysis of images that do not necessarily involve textures, e.g. localization of subjects in scenes, and (ii) classification of effect pigment images to help identify their components.
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.
Texto completoTran, 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.
Texto completoBooth, 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.
Texto completoSchofield, 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.
Texto completoBaik, 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.
Texto completoIncludes bibliographical references (p. 117-123).
by Edward H. Baik.
M.Eng.
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.
Texto completoFletcher, 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.
Texto completoBai, 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.
Texto completoPasáček, Václav. "Segmentace obrazu podle textury". Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2012. http://www.nusl.cz/ntk/nusl-236463.
Texto completojacquelin, christophe. "Segmentation de textures par algorithmes génétiques". Paris 5, 1996. http://www.theses.fr/1996PA05W072.
Texto completoDumoulin, François. "Using texture energy measures for the segmentation of forest scenes". Thesis, University of British Columbia, 1985. http://hdl.handle.net/2429/25736.
Texto completoForestry, Faculty of
Graduate
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.
Texto completoTan, Zhigang y 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.
Texto completoKylberg, 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.
Texto completoRoss, 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.
Texto completoPipes, 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.
Texto completoLee, 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.
Texto completoSmith, 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.
Texto completoTitle 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.
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.
Texto completoHidayat, 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.
Texto completoMuzzolini, 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.
Texto completoCelano, 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.
Texto completoKim, 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.
Texto completoDahdouh, 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.
Texto completoMonteiro-Palagi, Patricia. "Décomposition fréquentielle des textures : caractérisation et segmentation". Grenoble INPG, 1995. http://www.theses.fr/1995INPG0182.
Texto completoBhatnagar, 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.
Texto completoLong, 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.
Texto completoHudson, 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.
Texto completoLu, Chun-Shien y 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.
Texto completo國立成功大學
電機工程學系
86
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/multiscale representation of textured images based on the concept of human vision system.In this thesis, the 2D texture segmentation is firstly concerned. The main difficulties of texture segmentation are (i). determination of the number of clusters; (ii). extraction of reliable local features.In the literature, one method of finding the number of clusters is usually iterative to meet some criteria.Usually, it is time consuming since the same segmentation process are repeated many times.Here, we propose an unsupervised texture segmentation algorithm in which this determination is embedded in the clustering process and therefore the segmentation time is saved.In addition, we suggest the perceptually significant features, Wold features, as the description of textures.Some examples have been illustrated to confirm our idea.It is found by experimental results that textured images with large number of classes can be successfully segmented to a certain degree.Next, we consider the shape from texture problem which recovers visual perception of three dimensional structures from a monocular image by texture cue only.We use continuous wavelet transform to characterize the frequency variations as the ridge surface.The ridge surface is further derived to be parabolic.This is the first method to obtain this form in terms of the frequency variations.Also, a close form solution is proposed for the estimation of tilt and slant angles.Comparative study with a previous method by a same set of images demonstrate the superior ability of our approach. Furthermore, the complex (weakly ordered and disordered) textures in the shape from texture problem often obviated by most researchers are also considered in this thesis.A hybrid of ridge surface fitting and a robust regression techniques is developed for this problem.Experimental results demonstrate that our simple method is powerful in solving shape from complex texture.Finally, the 3D texture segmentation is taken into account as the first step of shape from texture problem.This problem is seldom noticed in the past.We present two algorithms to eliminate the usual assumption of shape from texture problem. The first one needs extraction of local features but the non- blocky effects can be achieved without needing to estimate the local features for every pixel.The second one has the characteristics of getting rid of extracting local features.Both methods are demonstrated by many natural images.In sum, this thesis provides efficient approaches for 2D texture segmentation, for shape from texture problem by considering various ordered textures, and for 3D texture segmentation as the first step of shape from texture problem.The performance of our methods have been demonstrated by a variety of Brodatz textures and real-world images.
Tasi, Chang-Der y 蔡昌德. "Segmentation of Color Texture Images". Thesis, 1994. http://ndltd.ncl.edu.tw/handle/25598642234953593910.
Texto completo國立交通大學
資訊科學學系
82
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 a new clustering technique in this thesis to accomplish segmentation. This new clustering technique is a "split-and-merge" technique using some high diminsional two-class analytical formulas. An important advantage of the proposed segmentation technique is that the technique does not need to know the number of segmented regions contained in a image. This property is very useful for automatic image segmentation. The experimental results show that the proposed method can segment well the given color texture images, and the time needed is short. Finally, we also use the proposed features to detect texture edges or classify texture images. The experimental results show that the proposed method also yields good results and the computation time is short, too.
Liu, Jyh Feng y 劉志鋒. "Texture Segmentation using Fractal Analysis". Thesis, 1998. http://ndltd.ncl.edu.tw/handle/76850789946703840775.
Texto completo中正理工學院
電機工程研究所
86
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 fractal dimension. Using the variagram plot versus a set of pixel distance pair, the scale of the texture element can be obtained. Following the calculation of fractal dimension of each pixel block by block, the texture boundary can be detected. Various experiments are conducted to test the proposed model.