Дисертації з теми "Segmentation texture"
Щоб переглянути інші типи публікацій з цієї теми, перейдіть за посиланням: Segmentation texture.
Оформте джерело за APA, MLA, Chicago, Harvard та іншими стилями
Ознайомтеся з топ-50 дисертацій для дослідження на тему "Segmentation texture".
Біля кожної праці в переліку літератури доступна кнопка «Додати до бібліографії». Скористайтеся нею – і ми автоматично оформимо бібліографічне посилання на обрану працю в потрібному вам стилі цитування: APA, MLA, «Гарвард», «Чикаго», «Ванкувер» тощо.
Також ви можете завантажити повний текст наукової публікації у форматі «.pdf» та прочитати онлайн анотацію до роботи, якщо відповідні параметри наявні в метаданих.
Переглядайте дисертації для різних дисциплін та оформлюйте правильно вашу бібліографію.
1
Camilleri, Kenneth P. "Multiresolution texture segmentation." Thesis, University of Surrey, 1999. http://epubs.surrey.ac.uk/843549/.
Анотація:
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 to test the separability of the distributions in each feature space. A measure was developed to evaluate evidence of multiple clusters from the two-point correlation function, making it possible to determine the most suitable feature space for clustering. Thus, at a given resolution level, the most appropriate feature space was selected and used to segment the image. Due to inherent ambiguities and limitations of the two-point correlation function, this feature space exploration and segmentation was performed several times at the same resolution level until no further evidence of multiple clusters was found, at which point, the process was repeated at the next finer resolution level. In this way, the image was progressively segmented, proceeding from coarse to fine Gabor resolution levels without any knowledge of the actual number of textures present. In order to refine the region-labelled image obtained at the end of the segmentation process, two postprocessing pixel-level algorithms were developed and implemented. The first was the mixed pixel classification algorithm which is based on the analysis of the effect of the averaging window at the boundary between two regions and re-assigns the pixel labels to improve the boundary localisation. Multiresolution probabilistic relaxation is the second postprocessing algorithm which we developed. This algorithm incorporates contextual evidence to relabel pixels close to the boundary in order to smooth it and improve its localisation. The results obtained were quantified by known error measures, as well as by new error measures which we developed. The quantified results were compared to similar results by other authors and show that our unsupervised algorithm performs as well as other methods which assume prior information.
2
Reyes-Aldasoro, Constantino Carlos. "Multiresolution volumetric texture segmentation." Thesis, University of Warwick, 2004. http://wrap.warwick.ac.uk/67756/.
Анотація:
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 using a Second Orientation Pyramid. A novel Bhattacharyya space, based on the Bhattacharyya distance is proposed for selecting of the most discriminant measurements and produces a compact feature space. Each dimension of the feature space is used to form a Quad Tree. At the highest level of the tree, new positional features are added to improve the contiguity of the classification. The classified space is then projected to lower levels of the tree where a boundary refinement procedure is performed with a 3D equivalent of butterfly filters. The performance of M-VTS is tested in 2D by classifying a set of standard texture images. The figures contain different textures that are visually stationary. M-VTS yields lower misclassification rates than reported elsewhere ([104, 111, 124]). The algorithm was tested in 3D with artificial isotropic data and three Magnetic Resonance Imaging sets of human knees with satisfactory results. The regions segmented from the knees correspond to anatomical structures that could be used as a starting point for other measurements. By way of example, we demonstrate successful cartilage extraction using our approach.
3
Linnett, L. M. "Multi-texture image segmentation." Thesis, Heriot-Watt University, 1991. http://hdl.handle.net/10399/856.
Анотація:
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 fractal dimension is made. Using this set of features, supervised and unsupervised, statistical processing schemes are presented which produce low classification error rates. Natural texture images are examined with particular application to the analysis of sonar images of the seabed. A number of processes based on fractal models for texture synthesis are also presented. These are used to produce realistic images of natural textures, again with particular reference to sonar images of the seabed, and which show the importance of phase and directionality in our perception of texture. A further extension is shown to give possible uses for image coding and object identification.
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.
5
Haddad, Stephen. "Texture measures for segmentation." Master's thesis, University of Cape Town, 2007. http://hdl.handle.net/11427/7461.
Анотація:
Includes bibliographical references (p. 67-72).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 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.
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.
Анотація:
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.ucfBachelors
Engineering
Electrical Engineering
7
Wen, Wen. "Computational texture analysis and segmentation." Thesis, University of Strathclyde, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.358812.
8
Pandit, Sanjay. "Texture segmentation by global optimization." Thesis, University of Surrey, 1999. http://epubs.surrey.ac.uk/843855/.
Анотація:
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 (labels) is very large and the global cost function may have many local minima. This problem can be overcome to a large extent by using a stochastic relaxation algorithm (for example, Simulated annealing). Initially, various classical techniques on texture segmentation are reviewed. Ideally, any texture segmentation algorithm should segment an image, so that there is one to one correspondence between the segmentated edgels and the ground truth edgels. The effectiveness of an algorithm can be quantified in terms of under and over detection errors for each segmented output image. These measures are used throughout this thesis to quantify the quality of the results. A particular method which uses global optimization for texture segmentation is initially identified as potentially interesting and is implemented and studied. The implementation proved that this method suffered from many shortcomings and it is not really as good as it was reported in the literature. As the general approach to the problem is a well established methodology for image processing problems, the rest of the thesis is devoted into different attempts to make this method work. The novel ideas introduced in order to improve the method are: An improved version of the cost function. The use of alternative statistics that characterize each texture. The use of a combination of statistics to charaterize textures. The use of an implicit dictionary of penalizable label configurations, as opposed to an explicit dictionary, leading to penalties applied to anything not acceptable rather than to a selection of unacceptable configurations. The introduction of a modified transfer function that maps statistical differences to label differences. The use of a database of training patterns instead of assuming that one knows a priori which textures are present in the image to be segmented. The use of alternative time schedules with which the model is imposed to the data gradually, in a linear, non-linear and in an adaptive way. The introduction of an enhanced set of labels that allows the use of local orientation of the boundary. The introduction of a novel way to create new states of the system during the process of simulated annealing in order to achieve faster acceleration, by updating the values of 9 label sites instead of a single label site at a time. The results obtained by all these modifications vastly improve the performance of the algorithm from its original version. However, the whole approach does not really produce the quality of the results expected for real applications and it does not exhibit the robustness of a system that could be used in practice. The reason appears to be the bluntness of the statistical tests used to identify the boundary. So, my conclusion is that although global optimization methods are good for edge detection where the data are the local values of the first derivative, the approach is not very appropriate for texture segmentation where one has to rely on statistical differences.
9
Tan, Tieniu. "Image texture analysis : classification and segmentation." Thesis, Imperial College London, 1990. http://hdl.handle.net/10044/1/8697.
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.
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.
Анотація:
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 descriptors, especially for hierarchical image segmentation techniques that have recently gained popularity for their wide range of applications in medical imaging, video surveillance, autonomous navigation, and computer vision in general. To pursue the aim, we focus in reducing the length of the texture feature and directly modelling the distribution of intensity-variation in the parametric space of a probability density function (pdf). In the first contributory chapter, we enhance the state-of-the-art Weber local descriptor (WLD) by considering the mean value of neighbouring pixel intensities along radial directions instead of sampling pixels at three scales. Consequently, the proposed descriptor, named Radial Mean WLD (RM-WLD), is three-fold shorter than WLD and it performs slightly better than WLD in hierarchical image segmentation. The statistical distributions of pixel intensities in different image regions are diverse by nature. In the second contributory chapter, we propose a novel texture feature, called ‘joint scale,’ by directly modelling the probability distribution of intensity differences. The Weibull distribution, one of the extreme value distributions, is selected for this purpose as it can represent a wide range of probability distributions with a couple of parameters. In addition, gradient orientation feature is calculated from all pixels in the neighbourhood with an extended Sobel operator, instead of using only the vertical and horizontal neighbours as considered in WLD. The length of the texture descriptor combining joint scale and gradiet orientation features remains the same as RM-WLD, but it exhibits significantly improved discrimination capability for better image segmentation. Initial regions in hierarchical segmentation play an important role in approximating texture features. Traditional arbitrary-shaped initial regions maintain the uniform colour property and thus may not retain the texture pattern of the segment they belong to. In the final contributory chapter, we introduce regular-shaped initial regions by enhancing the cuboidal partitioning technique, which has recently gained popularity in image/video coding research. Since the regions (cuboids) of cuboidal partitioning are of rectangular shape, they do not follow the colour-based boundary adherence of traditional initial regions. Consequently, the cuboids retain sufficient texture pattern cues to provide better texture approximation and discriminating capability. We have used benchmark segmentation datasets and metrics to evaluate the proposed texture descriptors. Experimental results on benchmark metrics and computational time are promising when the proposed texture features are used in the state-of-the-art iterative contraction and merging (ICM) image segmentation technique.Doctor of Philosophy
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.
13
Spann, Michael. "Texture description and segmentation in image processing." Thesis, Aston University, 1985. http://publications.aston.ac.uk/8057/.
Анотація:
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 process. Results indicate that this assumption is valid for those textures lacking significant local structure. A texture segmentation scheme is described which separates textured regions based on the assumption that each texture has a different distribution of signal energy within its amplitude spectrum. A set of bandpass quadrature filters are applied to the original signal and the envelope of the output of each filter taken. The filters are designed to have maximum mutual energy concentration in both the spatial and spatial frequency domains thus providing high spatial and class resolutions. The outputs of these filters are processed using a multi-resolution classifier which applies a clustering algorithm on the data at a low spatial resolution and then performs a boundary estimation operation in which processing is carried out over a range of spatial resolutions. Results demonstrate a high performance, in terms of the classification error, for a range of synthetic and natural textures.
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.
Анотація:
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.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 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.
15
Jobanputra, Rishi. "Preserving Texture Boundaries for SAR Sea Ice Segmentation." Thesis, University of Waterloo, 2004. http://hdl.handle.net/10012/913.
Анотація:
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 (GLCP) 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 texture boundaries. Proposed is an advancement to the GLCP method which will preserve texture boundaries during image segmentation. This method exploits the relationship a pixel has with its closest neighbors and weights the texture measurement accordingly. These texture features are referred to as WGLCP (weighted GLCP) texture features. In this research, the WGLCP and GLCP feature sets are compared in terms of boundary preservation, unsupervised segmentation ability, robustness to increasing boundary density and computation time. The WGLCP method outperforms the GLCP method in all aspects except for computation time, where it suffers. From the comparative analysis, an inconsistency with the GLCP correlation statistic was observed, which motivated an investigative study into using this statistic for image segmentation. As the overall goal of the thesis is to improve SAR sea ice segmentation accuracy, the concepts developed from the study are applied to the image segmentation problem. The results indicate that for images with high contrast boundaries, the GLCP correlation statistical feature decreases segmentation accuracy. When comparing WGLCP and GLCP features for segmentation, the WGLCP features provide higher segmentation accuracy.
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.
17
Li, Chang-Tsun. "Unsupervised texture segmentation using multiresolution Markov random fields." Thesis, University of Warwick, 1998. http://wrap.warwick.ac.uk/39307/.
Анотація:
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) is used to provide a set of spatially localised texture descriptors, which are based on a two-component model of texture, in which one component is a deformation, representing the structural or deterministic elements and the other is a stochastic one. Experiments show that the algorithm is efficient in alleviating class-position uncertainty via data propagation across resolutions. However, the blocking artifacts of the segmentation results show that it is preferable to combine both class and position information so as to achieve smoother and more accurate boundary estimation. In Chapter 5, based on the same MFT-MMRF framework, a boundary process is proposed to refine the segmentation result of the region process proposed in Chapter 4. At each resolution, all the image blocks on either sides of the preliminary boundary detected in the region process are treated as potential boundary-containing blocks (PBCB's). The orientation and the centroid of the boundary-segment contained in each PBCB are calculated. The sequence of PBCB's are then modelled as a MRF and the interaction energy between each pair of neighbouring blocks is defined as a function of the 'distance' D between the centroids of the two boundary segments. During the stochastic relaxation process boundary/non-boundary labels are assigned to the PBCB's. Once the algorithm converges, the centroids of the identified true boundary blocks are connected to form the refined boundary which is propagated down to the next resolution for further refinement.
18
Rzadca, Mark C. "Multivariate granulometry and its application to texture segmentation /." Online version of thesis, 1994. http://hdl.handle.net/1850/12200.
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.
Анотація:
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 origen de dichas muestras. Identificar y analizar los granos que componen tales imágenes de manera individual necesita de una etapa de segmentación. En esencia, esto implica la localización de las fronteras representadas en este caso por valles que separan zonas planas asociadas a los granos. De los diferentes métodos estudiados para la detección de dichos valles y para el caso concreto de imágenes petrográficas son los basados en técnicas de morfología matemática los que han dado mejores resultados. Además, la segmentación requiere un filtrado previo para el que se han estudiado nuevamente un conjunto de posibilidades entre las que cabe destacar los algoritmos multirresolución basados en wavelets.El 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.
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.
Анотація:
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 pertinentes qu'ils renferment.
Ce travail de recherche contribue à ce domaine, du fait qu'il propose un algorithme de segmentation spécialisé dans le traitement des modèles d'environnements probabilistes bidimensionnels et tridimensionnels. La méthode de segmentation proposée est non-supervisée et basée sur l'analyse et sur la différenciation entre les textures qui caractérisent les régions dotées d'une occupation donnée. La texture est représentée à l'aide de la double distribution du "motif local binaire" (Local Binary Pattern, LBP) et du "contraste" (Contrast, C). Le ratio logarithmique de probabilité, le G-statistique, est utilisé afin de mesurer le degré de similarité entre les différentes régions de l'environnement. Cette mesure pseudo-métrique compare les distributions LBP/C relatives aux différents segments. L'algorithme propose qui est utilisé pour segmenter l'espace probabiliste en régions uniformes dotées d'un état d'occupation déterministe, différencie entre les divers objets présents dans l'environnement en analysant la proximité entre les segments occupés obtenus. Notre schéma de segmentation ouvre la voie à un grand nombre d'applications en robotique autonome. Parmi celles-ci nous retrouvons: (1) La planification de la trajectoire et de mouvements d'un robot mobile; (2) L'évitement des obstacles et l'interaction d'un robot avec son environnement; (3) La sélection autonome de points de vue dans la construction de cartes de l'espace; (4) La reconnaissance d'objets par leur forme à partir de mesures de surface incomplètes et incertaines.
Deux versions de l'algorithme sont proposées, la première est specialisée dans la segmentation des images probabilistes bidimensionnelles tandis que la seconde correspond à une extension capable de prendre en compte une dimension additionnelle pour ainsi traiter le cas des environnements probabilistes tridimensionnels.
Le schéma de segmentation proposé est valide expérimentalement sur un grand nombre d'images probabilistes de dimensions variées et créées à partir de capteurs dotés d'une erreur sur la mesure de la distance ainsi que d'un pas angulaire limité. Le choix des paramètres intrinsèques du schéma de segmentation est déterminé empiriquement et évalué à l'aide d'une mesure d'erreur à deux niveaux basée sur les résultats obtenus avec différents réglages.
Finalement, l'applicabilité de l'algorithme proposé est évaluée au-delà du traitement des modèles d'environnements probabilistes en le testant, sans aucune adaptation, pour la segmentation d'images aériennes et médicales.
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.
Анотація:
[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 design, image processing and visualisation to produce realistic recreations of what we see in the world. For example, the texture on an object communicates its shape and surface properties in a 3D scene. Humans can discriminate between two textures and decide on their similarity in an instant, yet, achieving this algorithmically is not a simple process. Textures range in complexity and developing an approach that consistently synthe- sises this immense range is a dfficult problem to solve and motivates this research. Typically, texture synthesis methods aim to replicate texture by transferring the recognisable repeated patterns from the sample texture to synthesised output. Feature transferal can be achieved by matching pixels or patches from the sample to the output. As a result, two main approaches, pixel-based and patch-based, have es- tablished themselves in the active eld of texture synthesis. This thesis contributes to the present knowledge by introducing two novel texture synthesis methods. Both methods use image segmentation to improve synthesis results. ... The sample is segmented and the boundaries of the middle patch are confined to follow segment boundaries. This prevents texture features from being cut o prematurely, a common artifact of patch-based results, and eliminates the need for patch boundary comparisons that most other patch- based synthesis methods employ. Since no user input is required, this method is simple and straight-forward to run. The tiling of pre-computed tile pairs allows outputs that are relatively large to the sample size to be generated quickly. Output results show great success for textures with stochastic and semi-stochastic clustered features but future work is needed to suit more highly structured textures. Lastly these two texture synthesis methods are applied to the areas of image restoration and image replacement. These two areas of image processing involve replacing parts of an image with synthesised texture and are often referred to as constrained texture synthesis. Images can contain a large amount of complex information, therefore replacing parts of an image while maintaining image fidelity is a difficult problem to solve. The texture synthesis approaches and constrained synthesis implementations proposed in this thesis achieve successful results comparable with present methods.
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.
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.
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.
Анотація:
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1997.Includes bibliographical references (p. 117-123).
by Edward H. Baik.
M.Eng.
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.
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.
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.
Анотація:
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 interesting possibility is that since surface properties
are most immediately accessible to touch, texture perception may be more intimately
associated with texture than with vision (it is known that tactile input can affect
vision). Coincidentally, the basic organization of the touch (somatosensory) system
bears some analogy to that of the visual system. In particular, recent neurophysiological
findings showed that receptive fields for touch resemble that of vision, albeit
with some subtle differences.
The main novelty and contribution of this thesis is in the use of tactile receptive
field responses for texture segmentation. Furthermore, we showed that touch-based
representation is superior to its vision-based counterpart when used in texture boundary
detection. Tactile representations were also found to be more discriminable (LDA
and ANOVA). We expect our results to help better understand the nature of texture
perception and build more powerful texture processing algorithms. The results suggest that touch has an advantage over vision in texture processing.
Findings in this study are expected to shed new light on the role of tactile perception
of texture and its interaction with vision, and help develop more powerful, biologically
inspired texture segmentation algorithms.
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.
Анотація:
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 used for the clustering in this work.
29
jacquelin, christophe. "Segmentation de textures par algorithmes génétiques." Paris 5, 1996. http://www.theses.fr/1996PA05W072.
Анотація:
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 the image and allowed to compete. Each PAFO springs up on regions of the image as far as the underlying texture is an acceptable regimen. Some generations later, segmentation is achieved. The basic concepts of the proposed method are detailed. Our first results dealing with artificial textured images are shown and discussed
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.
Анотація:
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 panchromatic aerial photographs digitized at four spatial resolutions: 1.5, 5, 10 and 20 m. The main conclusion of these tests is that given an adequate spatial resolution, texture energy measures can provide a reliable segmentation of forest cover into two classes of textures corresponding to stands with open and closed canopies. Only the finest spatial resolution (1.5 m) proved to be adequate.Forestry, Faculty of
Graduate
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.
Анотація:
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 neighbourhoods centered at every pixel in an image, local and global texture attributes of the image can be described. Rotating the image has a similar effect as spatially shifting the pixels in the circular neighbourhood around without altering their intensities. A number of sequences can be formed by the intensities of pixels at various fixed distances from the center of the neighbourhood. Fourier transforming the sequences would generate coefficients that contain the texture information of the neighbourhood. From the magnitudes of these coefficients, several rotation invariant features are obtained. The capabilities of the new features are investigated in a number of classification and segmentation experiments. The experimental results compare favourably with those of prominent descriptors like the circular autoregressive model, the wavelet transform, the Gaussian Markov radom field and the co-occurrence matrix. In the majority of the instances, the new method shows superior performance.
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.
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.
Анотація:
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 detected in EM images while being unrecognizable by molecular methods. One problem with diagnostic EM is its high dependency on experts performing the analysis. Another problematic circumstance is that the EM facilities capable of handling the most dangerous pathogens are few, and decreasing in number. This thesis addresses these shortcomings with diagnostic EM by proposing image analysis methods mimicking the actions of an expert operating the microscope. The methods cover strategies for automatic image acquisition, segmentation of possible virus particles, as well as methods for extracting characteristic properties from the particles enabling virus identification. One discriminative property of viruses is their surface morphology or texture in the EM images. Describing texture in digital images is an important part of this thesis. Viruses show up in an arbitrary orientation in the TEM images, making rotation invariant texture description important. Rotation invariance and noise robustness are evaluated for several texture descriptors in the thesis. Three new texture datasets are introduced to facilitate these evaluations. Invariant features and generalization performance in texture recognition are also addressed in a more general context. The work presented in this thesis has been part of the project Panvirshield, aiming for an automatic diagnostic system for viral pathogens using EM. The work is also part of the miniTEM project where a new desktop low-voltage electron microscope is developed with the aspiration to become an easy to use system reaching high levels of automation for clinical tissue sections, viruses and other nano-sized particles.
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.
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.
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.
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.
Анотація:
Thesis (Ph.D. in Electrical Engineering)--S.M.U.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.
38
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.
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.
Анотація:
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 able to run at 47 frames per second on 320 by 240 images, while scoring an F-measure of 0.62 (out of a theoretical maximum of 0.79) on the Berkeley segmentation dataset. The Texton Ridge Detector runs at 10 frames per second but produces slightly better results, with an F-measure score of 0.63. These objective measurements show that the two proposed texture-boundary detectors outperform all other texture-boundary detectors on either quality or speed. As boundary detection is so widely-used, this development could induce improvements to many real-time computer vision applications.
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.
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.
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.
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.
Анотація:
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 invasif, de sa portabilité ainsi que de son excellente résolutiontemporelle ; elle possède toutefois une très faible résolution spatiale et souffre de nombreux artefacts tels que la mauvaise résolution des images, un fort bruit apparent et une forte dépendance àl'opérateur.L'objectif de cette thèse est de concevoir une méthode de filtrage des données échographiques ainsiqu'une méthode de segmentation et de suivi du rein sur des séquences ultrasonores, dans le butd'améliorer les conditions d'exécution d'interventions chirurgicales telles que les néphrolithotomiespercutanées.Le filtrage des données, soumis et publié dans SPIE 2010, est réalisé en exploitant le mode deformation des images : le signal radiofréquence est filtré directement, avant même la formation del'image 2D finale. Pour ce faire, nous utilisons une méthode basée sur les ondelettes, en seuillantdirectement les coefficients d'ondelettes aux différentes échelles à partir d'un algorithme de typesplit and merge appliqué avant reconstruction de l'image 2D.La méthode de suivi développée (une étude préliminaire a été publiée dans SPIE 2009), exploiteun premier contour fourni par le praticien pour déterminer, en utilisant des informations purementlocales, la position du contour sur l'image suivante de la séquence. L'image est transformée pourne plus être qu'un ensemble de vignettes caractérisées par leurs critères de texture et une premièresegmentation basée région est effectuée sur cette image des vignettes. Cette première étape effectuée, le contour de l'image précédente de la séquence est utilisé comme initialisation afin de recalculer le contour de l'image courante sur l'image des vignettes segmentée. L'utilisation d'informations locales nous a permis de développer une méthode facilement parallélisable, ce qui permettra de travailler dans une optique temps réel.La validation de la méthode de filtrage a été réalisée sur des signaux radiofréquence simulés. Laméthode a été comparée à différents algorithmes de l'état de l'art en terme de ratio signal sur bruitet de calcul de USDSAI. Les résultats ont montré la qualité de la méthode proposée comparativement aux autres. La méthode de segmentation, quant-à elle, a été validée sans filtrage préalable, sur des séquences 2D réelles pour un temps d'exécution sans optimisation, inférieur à la minute pour des images 512*512.
44
Monteiro-Palagi, Patricia. "Décomposition fréquentielle des textures : caractérisation et segmentation." Grenoble INPG, 1995. http://www.theses.fr/1995INPG0182.
Анотація:
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 chaque niveau sont implantes de filtres de type passe-bande orientee (filtres de gabor). Les techniques utilisees pour l'analyse de la complexite des distributions (caracteristiques dispersees, recouvrantes,. . . ) sont principalement les projections non lineaires par reseaux de neurones auto-organisants et la classification hierarchique ascendante. Ces techniques offrent une representation des similarites dans l'espace multidimensionnel des caracteristiques. Pour la segmentation, nous avons utilise un prototypage par simples agregations des donnees, dans le cadre d'une strategie non supervisee. Pour la discrimination, nous avons utilise un classement au plus proche voisin, dans le cadre d'une strategie supervisee. Les resultats obtenus sont presentes selon cette approche, analyses preliminaires de la distribution et tests suivant des protocoles non supervisee et supervisee. Les images analysees couvrent differents domaines, a savoir, des images utilisees en analyse psychophysique, ainsi que des images de textures naturelles a diverses granulosites utilisees en traitement d'images
45
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.
46
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.
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.
48
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.
Анотація:
博士國立成功大學
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.
49
Tasi, Chang-Der, and 蔡昌德. "Segmentation of Color Texture Images." Thesis, 1994. http://ndltd.ncl.edu.tw/handle/25598642234953593910.
Анотація:
碩士國立交通大學
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.
50
Liu, Jyh Feng, and 劉志鋒. "Texture Segmentation using Fractal Analysis." Thesis, 1998. http://ndltd.ncl.edu.tw/handle/76850789946703840775.
Анотація:
碩士中正理工學院
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.