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Добірка наукової літератури з теми "Texture description"

Автор: Grafiati

Опубліковано: 11 березня 2023

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Статті в журналах з теми "Texture description"

Reynolds, Craig. "Interactive Evolution of Camouflage." Artificial Life 17, no. 2 (April 2011): 123–36. http://dx.doi.org/10.1162/artl_a_00023.

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This article presents an abstract computation model of the evolution of camouflage in nature. The 2D model uses evolved textures for prey, a background texture representing the environment, and a visual predator. A human observer, acting as the predator, is shown a cohort of 10 evolved textures overlaid on the background texture. The observer clicks on the five most conspicuous prey to remove (“eat”) them. These lower-fitness textures are removed from the population and replaced with newly bred textures. Biological morphogenesis is represented in this model by procedural texture synthesis. Nested expressions of generators and operators form a texture description language. Natural evolution is represented by genetic programming (GP), a variant of the genetic algorithm. GP searches the space of texture description programs for those that appear least conspicuous to the predator.

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Eschner, Th, and J. J. Fundenberger. "Application of Anisotropic Texture Components." Textures and Microstructures 28, no. 3-4 (January 1, 1997): 181–95. http://dx.doi.org/10.1155/tsm.28.181.

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The description of textures in terms of texture components is an established conception in quantitative texture analysis. Recent developments lead to the representation of orientation distribution functions as a weighted sum of model functions, each corresponding to one anisotropic texture component. As was shown previously, an adequate texture description is possible with only a very small number of anisotropic texture components. As a result, textures and texture changes can be described by a small number of vivid parameters and their variations, namely by volume parts, half widths and ideal orientations.The texture of a tensile tested commercial aluminum alloy was investigated by decomposition into anisotropic components. The texture evolution during tensile testing is represented by the corresponding changes of the component parameters and compared with results from an iterative series expansion analysis.

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Khan, Fahad Shahbaz, Rao Muhammad Anwer, Joost van de Weijer, Michael Felsberg, and Jorma Laaksonen. "Compact color–texture description for texture classification." Pattern Recognition Letters 51 (January 2015): 16–22. http://dx.doi.org/10.1016/j.patrec.2014.07.020.

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Delannay, L., P. Van Houtte, and A. Van Bael. "New Parameter Model for Texture Description in Steel Sheets." Textures and Microstructures 31, no. 3 (January 1, 1999): 151–75. http://dx.doi.org/10.1155/tsm.31.151.

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A new model is proposed for the characterisation of steel sheet textures. This model relies on the identification of 25 relevant parameters in the Orientation Distribution Function (ODF). Textures consisting of alpha- and gamma-fibres and/or cube and Goss components can be generated. The model is mathematically formulated and an automatic parameter identification technique is presented.It was found that the model can quantitatively reproduce almost any industrial steel sheet texture. Based on this parameter model, a method is presented to systematically study the sensitivity of material properties on texture.

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Berezina, S. I., Yu O. Gordienko, and O. I. Solonets. "ANALYSIS OF WAYS OF SOLVING THE SEGMENTATION PROBLEM FOR HIGHLY TEXTURED OBJECTS." Проблеми створення, випробування, застосування та експлуатації складних інформаційних систем, no. 17 (December 30, 2019): 27–40. http://dx.doi.org/10.46972/2076-1546.2019.17.03.

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Increment of speed and reliability of aerospace images processing is directly related to solution of the task of automation of images interpretation process, which is achieved by minimizing search areas, detecting masked objects and defining the dynamics of changes in surveillance areas. The primary stage that in general determines the quality of results received by automated processing and interpretation is thematic segmentation of the image. In the process of thematic segmentation it is necessary to take into account presence of a large number of textured objects. The paper analyzes the ways of solving the segmentation problem for highly textured objects with large range of variation of possible color values. The research included separation of woodlands and single plants from meadows, steppes, etc., which are characterized by similar color characteristics, but differ in texture. It also included separation of residential areas from forests, which are characterized by the same grain size of texture and different color characteristics. The method of texture description, which is based on calculation of the number of differences in brightness per unit area of the image, the method of description and measurement of texture, characterized by the length of the series, the methods of texture description based on calculation of their fractal dimension have been investigated. In order to describe the texture by different methods, first of all, an aperture of the analysis window was defined. That aperture ensures separation of different classes of objects. The analyzed methods of texture description showed that areas of false identification are always present in the result images. It was determined that the best results were obtained with two of the discussed methods. The first one was the method of texture description and measurement based on calculation of the number of brightness differences per unit area of the image. The second one was the method of texture description based on calculation of fractal dimension by searching the area of the pyramid which covers image fragments. To obtain a more accurate segmented map of an image containing highly textured fragments, a combination of the two methods is suggested.

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PACHOWICZ, PETER W. "INTEGRATING LOW-LEVEL FEATURES COMPUTATION WITH INDUCTIVE LEARNING TECHNIQUES FOR TEXTURE RECOGNITION." International Journal of Pattern Recognition and Artificial Intelligence 04, no. 02 (June 1990): 147–65. http://dx.doi.org/10.1142/s0218001490000113.

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This paper presents a method for applying inductive learning techniques to texture description and recognition. Local features of texture are computed by two well-known methods, Laws’ masks and co-occurrence matrices. Then, a three-level generalization of local features is applied to create texture description rules. The first level generalization, the scaling interface, has been implemented to transform the numeric data of local texture features into their higher symbolic representation as numerical ranges. This scaling interface tests data consistency as well. The creation of description rules incorporating the inductive incremental learning algorithm is the second generalization step. The SG-TRUNC method of rule reduction is applied as the next hierarchical generalization level. This machine learning approach to texture description and recognition is compared with the classic pattern recognition methodology. The results from the recognition phase are presented from six classes of textures, characterized by smoothly changing illumination and/or texture resolution. The average recognition rate was 91% for the inductive learning approach, and all classes of textures were recognized. In comparison, the traditional k-NN pattern recognition method did not recognize one class of texture, and the average recognition rate was 83%. The proposed methodology smooths the recognition rates through the hierarchy of generalization levels, i.e. the next generalization step increases these rates for classes that were less easily recognized, and decreases these rates for classes that were more easily recognized.

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Fanany Onnilita Gaffar, Achmad, Darius Shyafary, Rony H, and Arief Baramanto Wicaksono Putra. "The new proposed method for texture modification of closed up face image based on image processing using local weighting pattern (LWP) with enhancement technique." International Journal of Engineering & Technology 7, no. 2.2 (March 5, 2018): 94. http://dx.doi.org/10.14419/ijet.v7i2.2.12742.

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The texture is a two- and three-dimensional design element that is distinguished by the visual and physical properties perceived. Textured areas in the image can be marked with uniform or varying spatial intensity distribution. There are many techniques and methods from simple to sophisticated which available including machine learning-based methods to modify the texture map. The texture feature description becomes a new challenge in the field of computer vision and pattern recognition since the emergence of the local pattern binary method (LBP). This study proposes a new method called Local Weighting Pattern (LWP) for modifying textures based on the pixel's neighborhood of an RGB image. The results of this study obtained that LWP method produces a texture with a unique and artistic visualization. The Log function has been used to improve the image quality of the LWP method.

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Guo, Yimo, Guoying Zhao, and Matti Pietikäinen. "Discriminative features for texture description." Pattern Recognition 45, no. 10 (October 2012): 3834–43. http://dx.doi.org/10.1016/j.patcog.2012.04.003.

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R., Reena Rose, Suruliandi A., and Meena K. "LOCAL TEXTURE DESCRIPTION FRAMEWORK FOR TEXTURE BASED FACE RECOGNITION." ICTACT Journal on Image and Video Processing 04, no. 03 (February 1, 2014): 773–84. http://dx.doi.org/10.21917/ijivp.2014.0112.

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Dnieprenko, V. N., and S. V. Divinskii. "A New Approach to Describing Three-Dimensional Orientation Distribution Functions in Textured Materials–Part I: Formation of Pole Density Distribution on Model Pole Figures." Textures and Microstructures 22, no. 2 (January 1, 1993): 73–85. http://dx.doi.org/10.1155/tsm.22.73.

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New method for simulation of orientation distribution functions of textured materials has been proposed. The approach is based on the concept to describe any texture class by a superposition of anisotropic partial fibre components. The texture maximum spread is described in a “local” coordinate system connected with the texture component axis. A set of Eulerian angles γ1,γ2,γ3 are introduced with this aim. To specify crystallite orientations with respect to the sample coordinate system two additional sets of Eulerian angles are introduced besides γ1,γ2,γ3. One of them, (Ψ0,θ0,ϕ0), defines the direction of the texture axis of a component with respect to the directions of the cub. The other set, (Ψ1,θ1,ϕ1), is determined by the orientation of the texture component and its texture axis in the sample coordinate system. Analytical expressions approximating real spreads of crystallites in three-dimensional orientation space have been found and their corresponding model pole figures have been derived. The proposed approach to the texture spread description permits to simulate a broad spectrum of real textures from single crystals to isotropic polycrystals with a high enough degree of correspondence.

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Більше джерел

Дисертації з теми "Texture description"

Siqueira, Fernando Roberti de 1989. "Multi-scale approaches to texture description = Abordagens multiescala para descrição de textura." [s.n.], 2013. http://repositorio.unicamp.br/jspui/handle/REPOSIP/275604.

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Orientadores: Hélio Pedrini, William Robson Schwartz
Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Computação
Made available in DSpace on 2018-08-24T04:06:04Z (GMT). No. of bitstreams: 1 Siqueira_FernandoRobertide_M.pdf: 20841189 bytes, checksum: 62053b7b36d54bbdccc8b5aa3650fe6a (MD5) Previous issue date: 2013
Resumo: Visão computacional e processamento de imagens desempenham um papel importante em diversas áreas, incluindo detecção de objetos e classificação de imagens, tarefas muito importantes para aplicações em imagens médicas, sensoriamento remoto, análise forense, detecção de pele, entre outras. Estas tarefas dependem fortemente de informação visual extraída de imagens que possa ser utilizada para descrevê-las eficientemente. Textura é uma das principais propriedades usadas para descrever informação tal como distribuição espacial, brilho e arranjos estruturais de superfícies. Para reconhecimento e classificação de imagens, um grande grupo de descritores de textura foi investigado neste trabalho, sendo que apenas parte deles é realmente multiescala. Matrizes de coocorrência em níveis de cinza (GLCM) são amplamente utilizadas na literatura e bem conhecidas como um descritor de textura efetivo. No entanto, este descritor apenas discrimina informação em uma única escala, isto é, a imagem original. Escalas podem oferecer informações importantes em análise de imagens, pois textura pode ser percebida por meio de diferentes padrões em diferentes escalas. Dessa forma, duas estratégias diferentes para estender a matriz de coocorrência para múltiplas escalas são apresentadas: (i) uma representação de escala-espaço Gaussiana, construída pela suavização da imagem por um filtro passa-baixa e (ii) uma pirâmide de imagens, que é definida pelo amostragem de imagens em espaço e escala. Este descritor de textura é comparado com outros descritores em diferentes bases de dados. O descritor de textura proposto e então aplicado em um contexto de detecção de pele, como forma de melhorar a acurácia do processo de detecção. Resultados experimentais demonstram que a extensão multiescala da matriz de coocorrência exibe melhora considerável nas bases de dados testadas, exibindo resultados superiores em relação a diversos outros descritores, incluindo a versão original da matriz de coocorrência em escala única
Abstract: Computer vision and image processing techniques play an important role in several fields, including object detection and image classification, which are very important tasks with applications in medical imagery, remote sensing, forensic analysis, skin detection, among others. These tasks strongly depend on visual information extracted from images that can be used to describe them efficiently. Texture is one of the main used characteristics that describes information such as spatial distribution, brightness and surface structural arrangements. For image recognition and classification, a large set of texture descriptors was investigated in this work, such that only a small fraction is actually multi-scale. Gray level co-occurrence matrices (GLCM) have been widely used in the literature and are known to be an effective texture descriptor. However, such descriptor only discriminates information on a unique scale, that is, the original image. Scales can offer important information in image analysis, since texture can be perceived as different patterns at distinct scales. For that matter, two different strategies for extending the GLCM to multiple scales are presented: (i) a Gaussian scale-space representation, constructed by smoothing the image with a low-pass filter and (ii) an image pyramid, which is defined by sampling the image both in space and scale. This texture descriptor is evaluated against others in different data sets. Then, the proposed texture descriptor is applied in skin detection context, as a mean of improving the accuracy of the detection process. Experimental results demonstrated that the GLCM multi-scale extension has remarkable improvements on tested data sets, outperforming many other feature descriptors, including the original GLCM
Mestrado
Ciência da Computação
Mestre em Ciência da Computação

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Brady, Karen. "A probabilistic framework for adaptive texture description." Nice, 2003. http://www.theses.fr/2003NICE4048.

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Cette thèse s’intéresse au problème de la description de textures. Au point de départ de notre travail se trouve la constatation que, si nous voulons modéliser une texture avec précision, nous avons besoin d’une distribution de probabilités »s sur un espace d’images de taille infinie. Cela nous permet ensuite de générer des distributions sur des images de taille finie par marginalisation. Pour une distribution gaussienne, les contraintes de calcul imposées par la diagonalisation nous amènent naturellement à des modèles adaptatifs utilisant des paquets d’ondelettes. En effet, ces derniers saisissent au mieux les périodicités principales de l’image ainsi que les corrélations à longue distance, tout en préservant l’indépendance des coefficients des paquets d’ondelettes. Nous utilisons les modèles ainsi obtenues dans deux méthodes de segmentation destinées à analyser des mosaïques de texture de Brodatz et des images de télédétection à haute réduction
This thesis deals with the issue of texture description. We start from the fact that in order to model texture accurately one needs a probability distributions on the space of infinite images. From this we generate a distribution on finite regions by marginalization. For a Gaussian distribution, the computational requirement of diagonalisation and the modelling requirement of adaptivity together lead naturally to adaptive wavelet packet models which capture the principal periodicities present in the textures and allow long-range correlations while preserving the independence of the wavelet packet coefficients. The resulting models are used within two different segmentation schemes for the purposes of analysing mosaics of natural textures from the Brodatz album and high resolution remote sensing images

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

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

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Ylioinas, J. (Juha). "Towards optimal local binary patterns in texture and face description." Doctoral thesis, Oulun yliopisto, 2016. http://urn.fi/urn:isbn:9789526214498.

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Abstract Local binary patterns (LBP) are among the most popular image description methods and have been successfully applied in a diverse set of computer vision problems, covering texture classification, material categorization, face recognition, and image segmentation, to name only a few. The popularity of the LBP methodology can be verified by inspecting the number of existing studies about its different variations and extensions. The number of those studies is vast. Currently, the methodology has been acknowledged as one of the milestones in face recognition research. The starting point of this research is to gain more understanding of which principles the original LBP descriptor is based on. After gaining some degree of insight, yet another try is made to improve some steps of the LBP pipeline, consisted of image pre-processing, pattern sampling, pattern encoding, binning, and further histogram post-processing. The main contribution of this thesis is a bunch of novel LBP extensions that partly try to unify some of the existing derivatives and extensions. The basis for the design of the new additional LBP methodology is to maximise data-driven premises, at the same time minimizing the need for tuning by hand. Prior to local binary pattern extraction, the thesis presents an image upsampling step dubbed as image pre-interpolation. As a natural consequence of upsampling, a greater number of patterns can be extracted and binned to a histogram improving the representational performance of the final descriptor. To improve the following two steps of the LBP pipeline, namely pattern sampling and encoding, three different learning-based methods are introduced. Finally, a unifying model is presented for the last step of the LBP pipeline, namely for local binary pattern histogram post-processing. As a special case of this, a novel histogram smoothing scheme is proposed, which shares the motivation and the effects with the image pre-interpolation for the most of its part. Deriving descriptors for such face recognition problems as face verification or age estimation has been and continues to be among the most popular domains where LBP has ever been applied. This study is not an exception in that regard as the main investigations and conclusions here are made on the basis of how the proposed LBP variations perform especially in the problems of face recognition. The experimental part of the study demonstrates that the proposed methods, experimentally validated using publicly available texture and face datasets, yield results comparable to the best performing LBP variants found in the literature, reported with the corresponding benchmarks
Tiivistelmä Paikalliset binäärikuviot kuuluvat suosituimpiin menetelmiin kuville suoritettavassa piirteenirrotuksessa. Menetelmää on sovellettu moniin konenäön ongelmiin, kuten tekstuurien luokittelu, materiaalien luokittelu, kasvojen tunnistus ja kuvien segmentointi. Menetelmän suosiota kuvastaa hyvin siitä kehitettyjen erilaisten johdannaisten suuri lukumäärä ja se, että nykyään kyseinen menetelmien perhe on tunnustettu yhdeksi virstanpylvääksi kasvojentunnistuksen tutkimusalueella. Tämän tutkimuksen lähtökohtana on ymmärtää periaatteita, joihin tehokkaimpien paikallisten binäärikuvioiden suorituskyky perustuu. Tämän jälkeen tavoitteena on kehittää parannuksia menetelmän eri askelille, joita ovat kuvan esikäsittely, binäärikuvioiden näytteistys ja enkoodaus, sekä histogrammin koostaminen ja jälkikäsittely. Esiteltävien uusien menetelmien lähtökohtana on hyödyntää mahdollisimman paljon kohdesovelluksesta saatavaa tietoa automaattisesti. Ensimmäisenä menetelmänä esitellään kuvan ylösnäytteistykseen perustuva paikallisten binäärikuvioiden johdannainen. Ylösnäytteistyksen luonnollisena seurauksena saadaan näytteistettyä enemmän binäärikuvioita, jotka histogrammiin koottuna tekevät piirrevektorista alkuperäistä erottelevamman. Seuraavaksi esitellään kolme oppimiseen perustuvaa menetelmää paikallisten binäärikuvioiden laskemiseksi ja niiden enkoodaukseen. Lopuksi esitellään paikallisten binäärikuvioiden histogrammin jälkikäsittelyn yleistävä malli. Tähän malliin liittyen esitellään histogrammin silottamiseen tarkoitettu operaatio, jonka eräs tärkeimmistä motivaatioista on sama kuin kuvan ylösnäytteistämiseen perustuvalla johdannaisella. Erilaisten piirteenirrotusmenetelmien kehittäminen kasvojentunnistuksen osa-alueille on erittäin suosittu paikallisten binäärikuvioiden sovellusalue. Myös tässä työssä tutkittiin miten kehitetyt johdannaiset suoriutuvat näissä osa-ongelmissa. Tutkimuksen kokeellinen osuus ja siihen liittyvät numeeriset tulokset osoittavat, että esitellyt menetelmät ovat vertailukelpoisia kirjallisuudesta löytyvien parhaimpien paikallisten binäärikuvioiden johdannaisten kanssa

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Sitepu, Husinsyah. "March-type models for the description of texture in granular materials." Thesis, Curtin University, 1998. http://hdl.handle.net/20.500.11937/2314.

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Анотація:

Texture in crystalline materials, i.e. preferred orientation (PO), is of interest in terms of texture-property relationships and also in X-ray diffraction science because PO can cause serious systematic errors in quantitative phase analysis using diffraction data. The single- parameter, pole-density distribution function (PDDF), proposed by March (1932) to represent PO in diffraction analysis, is used widely it Rietveld pattern-fitting following a suggestion by Dollase (1986). While the March model is an excellent descriptor of PO for gibbsite [AI(OH)3] x-ray powder diffraction (XRPD) data (O'Connor, Li and Sitepu, 1991), the model has proved to be deficient for Rietveld modelling with molybdite [Mo03], calcite [CaCO3] and kaolinite [A12O3.2SiO2.2H2O] XRPD data (Sitepu, 1991; O'Connor, Li and Sitepu, 1992; and Sitepu, O'Connor and Li, 1996). Therefore, the March model should not be regarded as a general-purpose PDDF descriptor.This study has examined the validity of the March model using XRPD and neutron powder diffraction (NPD) instruments operated, respectively, by the Curtin Materials Research Group in Perth and by the Australian Nuclear Science and Technology Organisation at the HIFAR reactor facility at Lucas Heights near Sydney. Extensive suites of XRPD and NPD data were measured for uniaxially-pressed powders of molybdite and calcite, for which the compression was systematically varied. It is clear from the various Rietveld refinements that the March model becomes increasingly unsatisfactory as the uniaxial pressure (and, therefore, the level of PO) increases.The March model has been tested with a physical relationship developed by the author which links the March r-parameter to the uniaxial pressure via the powder bulk modulus, B. The agreement between the results obtained from directly measured values of B and from Rietveld analysis with the March model are promising in terms of deducing the powder bulk modulus from the March r-parameter.An additional test of the March model was made with NPD data for specimens mounted, first, parallel to the instrument rotation axis and, then, normal to the axis. The results have provided some further indication that the March model is deficient for the materials considered in the study.During the course of the study, it was found that there are distinct differences between the direction of the near-surface texture in calcite, as measured by XRPD, and bulk texture characterised by NPD. The NPD-derived textures appear to be correct descriptions for the bulk material in uniaxially-pressed powders, whereas the XRPD textures are heavily influenced by the pressing procedure.An additional outcome of the NPD work has been the discovery, made jointly with Dr Brett Hunter of ANSTO, that the popular LHPM Rietveld code did not allow for inclusion of PO contributions from symmetry-equivalent reflections. Revision of the code by Dr Hunter showed that there is substantial bias in Rietveld-March r-parameters if these reflections are not factored correctly into the calculations.Finally, examination of pole-figure data has underlined the extent to which the March model oversimplifies the true distributions. It is concluded that spherical harmonics modelling should be used rather than the March model as a general PO modelling tool.

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Sitepu, Husinsyah. "March-type models for the description of texture in granular materials." Curtin University of Technology, School of Physical Sciences, 1998. http://espace.library.curtin.edu.au:80/R/?func=dbin-jump-full&object_id=10543.

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Анотація:

Texture in crystalline materials, i.e. preferred orientation (PO), is of interest in terms of texture-property relationships and also in X-ray diffraction science because PO can cause serious systematic errors in quantitative phase analysis using diffraction data. The single- parameter, pole-density distribution function (PDDF), proposed by March (1932) to represent PO in diffraction analysis, is used widely it Rietveld pattern-fitting following a suggestion by Dollase (1986). While the March model is an excellent descriptor of PO for gibbsite [AI(OH)3] x-ray powder diffraction (XRPD) data (O'Connor, Li and Sitepu, 1991), the model has proved to be deficient for Rietveld modelling with molybdite [Mo03], calcite [CaCO3] and kaolinite [A12O3.2SiO2.2H2O] XRPD data (Sitepu, 1991; O'Connor, Li and Sitepu, 1992; and Sitepu, O'Connor and Li, 1996). Therefore, the March model should not be regarded as a general-purpose PDDF descriptor.This study has examined the validity of the March model using XRPD and neutron powder diffraction (NPD) instruments operated, respectively, by the Curtin Materials Research Group in Perth and by the Australian Nuclear Science and Technology Organisation at the HIFAR reactor facility at Lucas Heights near Sydney. Extensive suites of XRPD and NPD data were measured for uniaxially-pressed powders of molybdite and calcite, for which the compression was systematically varied. It is clear from the various Rietveld refinements that the March model becomes increasingly unsatisfactory as the uniaxial pressure (and, therefore, the level of PO) increases.The March model has been tested with a physical relationship developed by the author which links the March r-parameter to the uniaxial pressure via the powder bulk modulus, B. The agreement between the results obtained from directly measured values of B and from Rietveld analysis with the March model are ++
promising in terms of deducing the powder bulk modulus from the March r-parameter.An additional test of the March model was made with NPD data for specimens mounted, first, parallel to the instrument rotation axis and, then, normal to the axis. The results have provided some further indication that the March model is deficient for the materials considered in the study.During the course of the study, it was found that there are distinct differences between the direction of the near-surface texture in calcite, as measured by XRPD, and bulk texture characterised by NPD. The NPD-derived textures appear to be correct descriptions for the bulk material in uniaxially-pressed powders, whereas the XRPD textures are heavily influenced by the pressing procedure.An additional outcome of the NPD work has been the discovery, made jointly with Dr Brett Hunter of ANSTO, that the popular LHPM Rietveld code did not allow for inclusion of PO contributions from symmetry-equivalent reflections. Revision of the code by Dr Hunter showed that there is substantial bias in Rietveld-March r-parameters if these reflections are not factored correctly into the calculations.Finally, examination of pole-figure data has underlined the extent to which the March model oversimplifies the true distributions. It is concluded that spherical harmonics modelling should be used rather than the March model as a general PO modelling tool.

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Esling, Claude Baro R. "Description de la texture des solides polycristallins et de leur déformation plastique." Metz : Université de Metz, 2008. ftp://ftp.scd.univ-metz.fr/pub/Theses/1972/Esling.Claude.SMZ7202.pdf.

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Wu, Jimin. "Description quantitative et modélisation de la texture d'un granite : granite de Guéret (France)." Bordeaux 1, 1995. http://www.theses.fr/1995BOR10600.

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Le travail présenté consiste en une analyse de la densité, de la taille, de la forme et de la distribution spatiale des principaux minéraux (quartz, feldspaths, biotite et muscovite) du granite de Guéret (France). Après un rappel des méthodes et techniques de l'analyse d'images, on développe trois méthodes d'acquisition d'images : méthode macro-photographique sur lames minces, méthode de saisie directe en microscopie et méthode semi-automatique par dessin manuel de la texture. Les paramètres mesures font l'objet d'une analyse statistique et d'une analyse critique par comparaison des résultats obtenus au moyen de chacune des méthodes. Les lois weibull, de laplace-gauss, et de poisson sont respectivement utilisées dans la modélisation de la distribution des tailles, de la forme et de la distribution spatiale des mineraux. Une analyse détaillée de la taille et de la distribution spatiale de biotite met en évidence une orientation privilégiée de la biotite parallèlement à une famille de fracture géometriquement bien identifiée. L'analyse des modèles de la distribution spatiale des minéraux se fait par adéquation de leur distribution expérimentale a une loi de poisson.

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Favier, Eric. "Contribution de l'analyse multi-résolution à la description des contours et des textures." Saint-Etienne, 1994. http://www.theses.fr/1994STET4020.

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Cette thèse s'attache à l'étude multirésolution des contours discrets et des images à niveaux de gris de texture. Le but est de fournir une description de ces objets à différentes échelles d'étude et d'essayer de déterminer la ou les échelles d'étude les plus appropriées pour l'analyse de ceux-ci. La première partie de ce travail se rapporte à l'étude des images binaires et plus particulièrement à l'étude des contours discrets. La notion d'échelle d'étude d'un contour est définie ainsi que des algorithmes permettant de la choisir. Pour chaque contour, on détermine la (ou les) échelle(s) d'étude permettant de le décrire de manière optimale. Des algorithmes de calculs de courbures en chaque point du contour, de détermination de points dominants en fonction de l'échelle d'étude choisie sont décrits. Une définition de la convexité est donnée en fonction des études choisies ainsi que la notion de t enveloppe convexe. Il est également présenté une distance sur l'ensemble des contours discrets. Cette dernière est une distance de convexité à une échelle d'étude donnée qui permet de comparer deux contours indépendamment de leur taille sur un critère de convexité. De plus, un lien est tissé entre ces différents algorithmes et les opérations de granulométrie ou d'ouvertures connues dans la morphologie mathématique. La seconde partie de cette thèse aborde l'étude des images en niveaux de gris multitexturées, là aussi le but est de décrire ces images en fonction de l'échelle d'étude et de trouver les bons paramètres pour l'analyse de ce type d'images. Les méthodes sont d'ordre statistique et les processus mis en oeuvre sont liés aux approches multirésolutions. Un modèle gaussien d'images de texture est présenté. Chaque image est étudiée à différentes échelles d'étude, et le choix de la meilleure échelle d'étude est abordé ce qui permet de proposer des méthodes automatiques de détection des zones de texture semblable. Des résultats sont présentés pour des exemples d'images multitexturées et une analyse des résultats montre que nos méthodes permettent de segmenter de manière très satisfaisante certaines images qui posent des problèmes à de nombreux algorithmes existants

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Kellokumpu, V. P. (Vili-Petteri). "Vision-based human motion description and recognition." Doctoral thesis, Oulun yliopisto, 2011. http://urn.fi/urn:isbn:9789514296758.

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Abstract This thesis investigates vision based description and recognition of human movements. Automated vision based human motion analysis is a fundamental technology for creating video based human computer interaction systems. Because of its wide range of potential applications, the topic has become an active area of research in the computer vision community. This thesis proposes the use of low level description of dynamics for human movement description and recognition. Two groups of approaches are developed: first, texture based methods that extract dynamic features for human movement description, and second, a framework that considers ballistic dynamics for human movement segmentation and recognition. Two texture based descriptions for human movement analysis are introduced. The first method uses the temporal templates as a preprocessing stage and extracts a motion description using local binary pattern texture features. This approach is then extended to a spatiotemporal space and a dynamic texture based method that uses local binary patterns from three orthogonal planes is proposed. The method needs no accurate segmentation of silhouettes, rather, it is designed to work on image data. The dynamic texture based description is also applied to gait recognition. The proposed descriptions have been experimentally validated on publicly available databases. Psychological studies on human movement indicate that common movements such as reaching and striking are ballistic by nature. Based on the psychological observations this thesis considers the segmentation and recognition of ballistic movements using low level motion features. Experimental results on motion capture and video data show the effectiveness of the method
Tiivistelmä Tässä väitöskirjassa tutkitaan ihmisen liikkeen kuvaamista ja tunnistamista konenäkömenetelmillä. Ihmisen liikkeen automaattinen analyysi on keskeinen teknologia luotaessa videopohjaisia järjestelmiä ihmisen ja koneen vuorovaikutukseen. Laajojen sovellusmahdollisuuksiensa myötä aiheesta on tullut aktiivinen tutkimusalue konenäön tutkimuksen piirissä. Väitöskirjassa tutkitaan matalan tason piirteiden käyttöä ihmisen liikkeen dynaamiikan kuvaamiseen ja tunnistamiseen. Työssä esitetään kaksi tekstuuripohjaista mentelmää ihmisen liikkeen kuvaamiseen ja viitekehys ballististen liikkeiden segmentointiin ja tunnistamiseen. Työssä esitetään kaksi tekstuuripohjaista menetelmää ihmisen liikkeen analysointiin. Ensimmäinen menetelmä käyttää esikäsittelynä ajallisia kuvamalleja ja kuvaa mallit paikallisilla binäärikuvioilla. Menetelmä laajennetaan myös tila-aika-avaruuteen. Dynaamiseen tekstuuriin perustuva menetelmä irroittaa paikalliset binäärikuviot tila-aika-avaruuden kolmelta ortogonaaliselta tasolta. Menetelmä ei vaadi ihmisen siluetin tarkkaa segmentointia kuvista, koska se on suunniteltu toimimaan suoraan kuvatiedon perusteella. Dynaamiseen tekstuuriin pohjautuvaa menetelmää sovelletaan myös henkilön tunnistamiseen kävelytyylin perusteella. Esitetyt menetelmät on kokeellisesti vahvistettu yleisesti käytetyillä ja julkisesti saatavilla olevilla tietokannoilla. Psykologiset tutkimukset ihmisen liikkumisesta osoittavat, että yleiset liikkeet, kuten kurkoittaminen ja iskeminen, ovat luonteeltaan ballistisia. Tässä työssä tarkastellaan ihmisen liikkeen ajallista segmentointia ja tunnistamista matalan tason liikepiirteistä hyödyntäen psykologisia havaintoja. Kokeelliset tulokset liikkeenkaappaus ja video aineistolla osoittavat menetelmän toimivan hyvin

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Більше джерел

Книги з теми "Texture description"

Spann, Michael. Texture description and segmentation in image processing. Birmingham: University of Aston. Department of Electrical and Electronic Engineering, 1985.

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Rao, A. Ravishankar. A Taxonomy for Texture Description and Identification. New York, NY: Springer US, 1990. http://dx.doi.org/10.1007/978-1-4613-9777-9.

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Rao, A. Ravishankar. A Taxonomy for Texture Description and Identification. New York, NY: Springer US, 1990.

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Rao, A. Ravishankar. A taxonomy for texture description and identification. New York: Springer-Verlag, 1990.

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Vanessa, Cowling, ed. West Coast: Landscape, people, food, texture. Cape Town: Struik, 2006.

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B, Baker David, ed. Thick description and fine texture: Studies in the history of psychology. Akron, Ohio: University of Akron Press, 2003.

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Al-Zalabieh, Abdullah Awad Eid. Wadi Rum: The charm of beauty and the texture of fancy. [Amman?]: Dar Al-Basheer, 2007.

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Lynn, Peterson J., ed. The Texas flowerscaper: A seasonal guide to bloom, height, color, and texture. Salt Lake City: Gibbs Smith, 1996.

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Lefkoşa tarihi kent dokusunda Sarayönü Meydanı oluşumu ve gelişimi: The formation and development of Sarayonu Square within the historical city texture of Nicosia. Lefkoşa (Cyprus): Işık Kitabevi Yayınları, 2008.

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Rhodes, Jeannie Frey. A sense of green: A city's changing texture : an interpretive black & white repeat photography exhibit of the Baton Rouge urban forest : Louisiana Arts and Science Center, October 28. 1997-January 4, 1998. [Baton Rouge]: The Center, 1997.

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Частини книг з теми "Texture description"

Della Ventura, Anna, Isabella Gagliardi, and Raimondo Schettini. "Indexing Color-Texture Image Patterns." In Image Description and Retrieval, 105–20. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-4825-6_5.

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de Ridder, Dick, Robert P. W. Duin, and Josef Kittler. "Texture Description by Independent Components." In Lecture Notes in Computer Science, 587–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-70659-3_61.

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Salvatella, Anna, Maria Vanrell, and Ramon Baldrich. "Subtexture Components for Texture Description." In Pattern Recognition and Image Analysis, 884–92. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-44871-6_102.

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Rao, A. Ravishankar. "Computing oriented texture fields." In A Taxonomy for Texture Description and Identification, 17–58. New York, NY: Springer US, 1990. http://dx.doi.org/10.1007/978-1-4613-9777-9_2.

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Rao, A. Ravishankar. "Disordered textures." In A Taxonomy for Texture Description and Identification, 145–57. New York, NY: Springer US, 1990. http://dx.doi.org/10.1007/978-1-4613-9777-9_5.

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Rao, A. Ravishankar. "Compositional textures." In A Taxonomy for Texture Description and Identification, 158–76. New York, NY: Springer US, 1990. http://dx.doi.org/10.1007/978-1-4613-9777-9_6.

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Huang, Xiaohua, Guoying Zhao, Xiaopeng Hong, Matti Pietikäinen, and Wenming Zheng. "Texture Description with Completed Local Quantized Patterns." In Image Analysis, 1–10. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38886-6_1.

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Rao, A. Ravishankar. "Analyzing strongly ordered textures." In A Taxonomy for Texture Description and Identification, 126–44. New York, NY: Springer US, 1990. http://dx.doi.org/10.1007/978-1-4613-9777-9_4.

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Rao, A. Ravishankar. "Introduction." In A Taxonomy for Texture Description and Identification, 1–16. New York, NY: Springer US, 1990. http://dx.doi.org/10.1007/978-1-4613-9777-9_1.

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Rao, A. Ravishankar. "The analysis of oriented textures through phase portraits." In A Taxonomy for Texture Description and Identification, 59–125. New York, NY: Springer US, 1990. http://dx.doi.org/10.1007/978-1-4613-9777-9_3.

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Тези доповідей конференцій з теми "Texture description"

Kasparis, Takis, Nicolaos S. Tzannes, Mostafa A. Bassiouni, and Qing Chen. "Fractal-based multifeature texture description." In Munich '91 (Lasers '91), edited by Hatem N. Nasr. SPIE, 1991. http://dx.doi.org/10.1117/12.46061.

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"Color description and texture analysis." In 2015 International Conference on Image Processing Theory, Tools and Applications (IPTA). IEEE, 2015. http://dx.doi.org/10.1109/ipta.2015.7367135.

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Ben Haj Ayech, Marouane, and Hamid Amiri. "Texture description using statistical feature extraction." In 2016 2nd International Conference on Advanced Technologies for Signal and Image Processing (ATSIP). IEEE, 2016. http://dx.doi.org/10.1109/atsip.2016.7523072.

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Quan Pang, Cuirong Yang, Yingle Fan, and Ping Xu. "Texture Image Segmentation Based on Description Complexity." In 2007 IEEE International Conference on Control and Automation. IEEE, 2007. http://dx.doi.org/10.1109/icca.2007.4376882.

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Manian, Vidya B., and Ramon E. Vasquez. "Genetic algorithm for texture description and classification." In AeroSense 2002, edited by Zia-ur Rahman, Robert A. Schowengerdt, and Stephen E. Reichenbach. SPIE, 2002. http://dx.doi.org/10.1117/12.477592.

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Ardizzone, Edoardo, Alessandro Bruno, and Giuseppe Mazzola. "Copy-move forgery detection via texture description." In the 2nd ACM workshop. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1877972.1877990.

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Zhang, Nuo, and Toshinori Watanabe. "Texture image description based on data compression." In 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2011). IEEE, 2011. http://dx.doi.org/10.1109/fskd.2011.6020004.

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Hatipoglu, S., S. K. Mitra, and N. Kingsbury. "Image texture description using complex wavelet transform." In Proceedings of 7th IEEE International Conference on Image Processing. IEEE, 2000. http://dx.doi.org/10.1109/icip.2000.899472.

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Pawlus, Pawel, Rafal Reizer, Michal Wieczorowski, and Grzegorz Krolczyk. "Parametric description of one-process surface texture." In 2021 6th International Conference on Nanotechnology for Instrumentation and Measurement (NanofIM). IEEE, 2021. http://dx.doi.org/10.1109/nanofim54124.2021.9737339.

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Xu, Pengfei, Hongxun Yao, Rongrong Ji, Xiaoshuai Sun, and Xianming Liu. "A rotation and scale invariant texture description approach." In Visual Communications and Image Processing 2010, edited by Pascal Frossard, Houqiang Li, Feng Wu, Bernd Girod, Shipeng Li, and Guo Wei. SPIE, 2010. http://dx.doi.org/10.1117/12.863520.

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Звіти організацій з теми "Texture description"

Wells, Aaron, Tracy Christopherson, Gerald Frost, Matthew Macander, Susan Ives, Robert McNown, and Erin Johnson. Ecological land survey and soils inventory for Katmai National Park and Preserve, 2016–2017. National Park Service, September 2021. http://dx.doi.org/10.36967/nrr-2287466.

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This study was conducted to inventory, classify, and map soils and vegetation within the ecosystems of Katmai National Park and Preserve (KATM) using an ecological land survey (ELS) approach. The ecosystem classes identified in the ELS effort were mapped across the park, using an archive of Geo-graphic Information System (GIS) and Remote Sensing (RS) datasets pertaining to land cover, topography, surficial geology, and glacial history. The description and mapping of the landform-vegetation-soil relationships identified in the ELS work provides tools to support the design and implementation of future field- and RS-based studies, facilitates further analysis and contextualization of existing data, and will help inform natural resource management decisions. We collected information on the geomorphic, topographic, hydrologic, pedologic, and vegetation characteristics of ecosystems using a dataset of 724 field plots, of which 407 were sampled by ABR, Inc.—Environmental Research and Services (ABR) staff in 2016–2017, and 317 were from existing, ancillary datasets. ABR field plots were located along transects that were selected using a gradient-direct sampling scheme (Austin and Heligers 1989) to collect data for the range of ecological conditions present within KATM, and to provide the data needed to interpret ecosystem and soils development. The field plot dataset encompassed all of the major environmental gradients and landscape histories present in KATM. Individual state-factors (e.g., soil pH, slope aspect) and other ecosystem components (e.g., geomorphic unit, vegetation species composition and structure) were measured or categorized using standard classification systems developed for Alaska. We described and analyzed the hierarchical relationships among the ecosystem components to classify 92 Plot Ecotypes (local-scale ecosystems) that best partitioned the variation in soils, vegetation, and disturbance properties observed at the field plots. From the 92 Plot Ecotypes, we developed classifications of Map Ecotypes and Disturbance Landscapes that could be mapped across the park. Additionally, using an existing surficial geology map for KATM, we developed a map of Generalized Soil Texture by aggregating similar surficial geology classes into a reduced set of classes representing the predominant soil textures in each. We then intersected the Ecotype map with the General-ized Soil Texture Map in a GIS and aggregated combinations of Map Ecotypes with similar soils to derive and map Soil Landscapes and Soil Great Groups. The classification of Great Groups captures information on the soil as a whole, as opposed to the subgroup classification which focuses on the properties of specific horizons (Soil Survey Staff 1999). Of the 724 plots included in the Ecotype analysis, sufficient soils data for classifying soil subgroups was available for 467 plots. Soils from 8 orders of soil taxonomy were encountered during the field sampling: Alfisols (<1% of the mapped area), Andisols (3%), Entisols (45%), Gelisols (<1%), Histosols (12%), Inceptisols (22%), Mollisols (<1%), and Spodosols (16%). Within these 8 Soil Orders, field plots corresponded to a total of 74 Soil Subgroups, the most common of which were Typic Cryaquents, Typic Cryorthents, Histic Cryaquepts, Vitrandic Cryorthents, and Typic Cryofluvents.

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Ma, Yue, and Felix Distel. Learning Formal Definitions for Snomed CT from Text. Technische Universität Dresden, 2013. http://dx.doi.org/10.25368/2022.193.

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Snomed CT is a widely used medical ontology which is formally expressed in a fragment of the Description Logic EL++. The underlying logics allow for expressive querying, yet make it costly to maintain and extend the ontology. Existing approaches for ontology generation mostly focus on learning superclass or subclass relations and therefore fail to be used to generate Snomed CT definitions. In this paper, we present an approach for the extraction of Snomed CT definitions from natural language texts, based on the distance relation extraction approach. By benefiting from a relatively large amount of textual data for the medical domain and the rich content of Snomed CT, such an approach comes with the benefit that no manually labelled corpus is required. We also show that the type information for Snomed CT concept is an important feature to be examined for such a system. We test and evaluate the approach using two types of texts. Experimental results show that the proposed approach is promising to assist Snomed CT development.

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