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Статті в журналах з теми "Texture description"

Автор: Grafiati

Опубліковано: 11 березня 2023

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

CECCARELLI, MICHELE, FRANCESCO MUSACCHIA, and ALFREDO PETROSINO. "CONTENT-BASED IMAGE RETRIEVAL BY A FUZZY SCALE-SPACE APPROACH." International Journal of Pattern Recognition and Artificial Intelligence 20, no. 06 (September 2006): 849–67. http://dx.doi.org/10.1142/s0218001406005009.

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Image descriptions aimed at the realization of content-based image retrieval (CBIR) should include the vagueness of both data representations and user queries. Here we show how multiscale textural gradient can be used as an efficient visual cue for image description. This feature has been already efficiently used in problems of image segmentation and texture separation. Our main idea is based on the assumption that, for image description, shape and textures should be considered together within a unified model. We report an efficient image description algorithm where the multiscale analysis is modeled by a differential morphological filter. Experiments with large image databases and comparisons with classical methods are reported.

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12

ANH, V. V., F. GRAS, and H. T. TSUI. "MULTIFRACTAL DESCRIPTION OF NATURAL SCENES." Fractals 04, no. 01 (March 1996): 35–43. http://dx.doi.org/10.1142/s0218348x96000066.

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In this paper, a general class of fractional random fields, ℬα, 0≤α<2, is defined. The members of ℬα can be used to model natural scenes and textures. It is shown that the fractal dimension of random fields in ℬα is a linear nonincreasing function of a for 0≤α<α0 and a linear nondecreasing function of α for α0<α<2. The number α0 corresponds to the Hausdorff-Besicovitch dimension of the random field. These linear relationships are significant for texture comparison and classification.

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13

Shivanna, D. M., S. D. Kavitha, and M. B. Kiran. "Texture Feature Analysis of Milled Components Using Vision System." Advanced Materials Research 845 (December 2013): 745–49. http://dx.doi.org/10.4028/www.scientific.net/amr.845.745.

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Surface texture assessment is very useful in predicting the functional behaviour of engineering components. Surface texture is composed of three elements-roughness, waviness and form Error. The proposed method analyzes surface texture in two ways-Subjective analysis and Objective analysis. Subjective analysis makes use of histogram and texture spectrum whereas objective analysis uses Grey Level Co-occurrence Matrix (GLCM) based standard texture descriptors. Different milled surfaces having different textures are prepared by varying the machining parameters. The proposed method is non-contact in nature and high measuring speeds are possible. The method provides a complete texture description for a given surface.

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14

Li, Feng, Lina Yuan, Kun Zhang, and Wenqing Li. "A defect detection method for unpatterned fabric based on multidirectional binary patterns and the gray-level co-occurrence matrix." Textile Research Journal 90, no. 7-8 (October 1, 2019): 776–96. http://dx.doi.org/10.1177/0040517519879904.

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A new texture-feature description operator, called the multidirectional binary patterns (MDBP) operator, is proposed in this paper. The operator can extract the detailed distribution of textures in local regions by comparing the differences in the gray levels between neighboring pixels. Moreover, the texture expression ability is enhanced by focusing on the texture features in the linear neighborhood of the image in multiple directions. The MDBP operator was modified by introducing a “uniform” pattern to reduce the grayscale values in the image. Combining the “uniform” MDBP operator and the gray-level co-occurrence matrix, an unpatterned fabric-defect detection scheme is proposed, including texture-feature extraction and detection stages. In the first stage, the multidirectional texture-feature matrix of a nondefective fabric image is extracted, and then the detection threshold is determined based on the similarity between the feature matrices. In the second stage, the defect is detected with the detection threshold. The proposed method is adapted to various grayscale textile images with different characteristics and is robust to a wide variety of image-processing operations. In addition, it is invariant to grayscale changes, performs well when representing textures and detecting defects and has lower computational complexity than other methods.

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15

Mingolo, N., and C. Vial-Edwards. "Description of Texture and Yield Locus Evolution Under Different Deformation Paths of Copper Sheet." Textures and Microstructures 23, no. 4 (January 1, 1995): 237–48. http://dx.doi.org/10.1155/tsm.23.237.

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The evolution of texture with plastic deformation along different loading paths has been studied for a commercial DHP copper sheet metal. Two models have been utilized to predict the evolution of textures: Viscoplastic under Relaxed Constraints conditions (VRC) and a Self Consistent approach (SC) with viscoplastic conditions, where the {111}<110> active slip systems were selected according to a strain rate sensitivity law.The stress-strain curves along different loading paths were calculated taking into account the texture evolution predicted by VRC and SC models. Predictions with the SC formulation were very close to experimental results. Texture evolution depended on the deformation path.

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16

Muller, K. H., D. Eckert, P. A. P. Wendhausen, N. Handstein, S. Wirth, and M. Wolf. "Description of texture for permanent magnets." IEEE Transactions on Magnetics 30, no. 2 (March 1994): 586–88. http://dx.doi.org/10.1109/20.312343.

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17

GUI, Yan, Mingang CHEN, Zhifeng XIE, Lizhuang MA, and Zhihua CHEN. "Texture Synthesis based on Feature Description." Journal of Advanced Mechanical Design, Systems, and Manufacturing 6, no. 3 (2012): 376–88. http://dx.doi.org/10.1299/jamdsm.6.376.

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18

Katkar, Girish, and Pravin Ghosekar. "TexRet." International Journal of Information Systems and Social Change 3, no. 1 (January 2012): 37–46. http://dx.doi.org/10.4018/jissc.2012010104.

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The TEXRET-System, a texture retrieval system based on soft-computing technologies is being developed. The importance of this kind of system is increasing due to the massive access to digital image databases, which also demand the existence of systems that can understand human high-level requests. The TEXRET system has the following features: (i) direct access from the Internet, (ii) high interactivity, (iii) texture retrieval using human-like or fuzzy description of the textures, (iv) content-based texture retrieval using user-feedback, and (v) synthesis or generation of the requested textures when these are not found in the database, which allows a growing of the database. One of the main system features is synthesis of the requested textures when these are not found in the database, which allows a growing of the database. Missing textures are synthesized interactively using Markov Random Fields and interactive genetic algorithms. This paper is centered on the texture synthesis of the textures.

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19

Goyal, Aparna, and Reena Gunjan. "Bleeding Detection in Gastrointestinal Images using Texture Classification and Local Binary Pattern Technique: A Review." E3S Web of Conferences 170 (2020): 03007. http://dx.doi.org/10.1051/e3sconf/202017003007.

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Texture analysis has proven to be a breakthrough in many applications of computer image analysis. It has been used for classification or segmentation of images which requires an effective description of image texture. Due to high discriminative power and simplicity of computation, the local binary pattern descriptors have been used for distinguishing different textures and in extracting texture and color in medical images. This paper discusses performance of various texture classification techniques using Contourlet Transform, Discrete Fourier Transform, Local Binary Patterns and Lacunarity analysis. The study reveals that the incorporation of efficient image segmentation, enhancement and texture classification using local binary pattern descriptor detects bleeding region in human intestines precisely.

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20

Qian, Chun Hua, He Qun Qiang, and Sheng Rong Gong. "A Retrieval Strategy for Texture Image." Applied Mechanics and Materials 635-637 (September 2014): 1018–25. http://dx.doi.org/10.4028/www.scientific.net/amm.635-637.1018.

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Texture Information is widely used as one of the main low-layer features in the content-based image retrieval. In general, when the retrieval is carried out in texture image space, the same description method is adopted to regular and irregular texture images. As a large amount of regular and irregular texture images existed in the image database, it is very difficult to describe every texture with the same description method. In this paper, a retrieval strategy for texture image is proposed. The proposed strategy is divided into steps: First, classify texture images by Wold decomposition into regular and irregular texture images, then describe and retrieve them by regular and irregular texture description separately. Experimental results have showed that proposed strategy can improve classification and retrieval precision.

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21

Chi, Jianning, Xiaosheng Yu, Yifei Zhang, and Huan Wang. "A Novel Local Human Visual Perceptual Texture Description with Key Feature Selection for Texture Classification." Mathematical Problems in Engineering 2019 (February 4, 2019): 1–20. http://dx.doi.org/10.1155/2019/3756048.

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This paper proposes a novel local texture description method which defines six human visual perceptual characteristics and selects the minimal subset of relevant as well as nonredundant features based on principal component analysis (PCA). We assign six texture characteristics, which were originally defined by Tamura et al., with novel definition and local metrics so that these measurements reflect the human perception of each characteristic more precisely. Then, we propose a PCA-based feature selection method exploiting the structure of the principal components of the feature set to find a subset of the original feature vector, where the features reflect the most representative characteristics for the textures in the given image dataset. Experiments on different publicly available large datasets demonstrate that the proposed method provides superior performance of classification over most of the state-of-the-art feature description methods with respect to accuracy and efficiency.

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22

Pawlus, Pawel, Rafal Reizer, Michał Wieczorowski, and Grzegorz Królczyk. "Parametric description of one-process surface texture." Measurement 204 (November 2022): 112066. http://dx.doi.org/10.1016/j.measurement.2022.112066.

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23

He, Chu, Tong Zhuo, Xin Su, Feng Tu, and Dong Chen. "Local Topographic Shape Patterns for Texture Description." IEEE Signal Processing Letters 22, no. 7 (July 2015): 871–75. http://dx.doi.org/10.1109/lsp.2014.2374608.

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24

Alvarez, Susana, Anna Salvatella, Maria Vanrell, and Xavier Otazu. "Low-dimensional and comprehensive color texture description." Computer Vision and Image Understanding 116, no. 1 (January 2012): 54–67. http://dx.doi.org/10.1016/j.cviu.2011.08.004.

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25

Keller, James M., and Susan Chen. "Texture description and segmentation through fractal geometry." Computer Vision, Graphics, and Image Processing 44, no. 3 (December 1988): 368. http://dx.doi.org/10.1016/0734-189x(88)90133-8.

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26

Keller, James M., Susan Chen, and Richard M. Crownover. "Texture description and segmentation through fractal geometry." Computer Vision, Graphics, and Image Processing 45, no. 2 (February 1989): 150–66. http://dx.doi.org/10.1016/0734-189x(89)90130-8.

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27

Fernández, Antonio, Marcos X. Álvarez, and Francesco Bianconi. "Texture Description Through Histograms of Equivalent Patterns." Journal of Mathematical Imaging and Vision 45, no. 1 (September 12, 2012): 76–102. http://dx.doi.org/10.1007/s10851-012-0349-8.

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28

Xu, Yong, Hui Ji, and Cornelia Fermüller. "Viewpoint Invariant Texture Description Using Fractal Analysis." International Journal of Computer Vision 83, no. 1 (February 24, 2009): 85–100. http://dx.doi.org/10.1007/s11263-009-0220-6.

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29

Mulvaney, D. J., D. E. Newland, and K. F. Gill. "A complete description of surface texture profiles." Wear 132, no. 1 (July 1989): 173–82. http://dx.doi.org/10.1016/0043-1648(89)90210-x.

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30

Rafi, Mudassir, and Susanta Mukhopadhyay. "Texture description using multi-scale morphological GLCM." Multimedia Tools and Applications 77, no. 23 (May 29, 2018): 30505–32. http://dx.doi.org/10.1007/s11042-018-5989-2.

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31

Kasparis, T., N. S. Tzannes, M. Bassiouni, and Q. Chen. "Texture description using fractal and energy features." Computers & Electrical Engineering 21, no. 1 (January 1995): 21–32. http://dx.doi.org/10.1016/0045-7906(94)00012-6.

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32

Akoushideh, Alireza. "CFS: An effective statistical texture descriptor." Indonesian Journal of Electrical Engineering and Computer Science 19, no. 1 (July 1, 2020): 553. http://dx.doi.org/10.11591/ijeecs.v19.i1.pp553-562.

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<p class="IETAbstractText">Coarseness is an effective description for texture analysis and many approaches capture this property of texture in different methods. In this research, we propose a new texture descriptor (CFS) that works based on coarseness and the fineness similarity score. For tuning of its configuration, we collect coarse and fine textures based on human visual perception at first. After that, we relabel the "coarse" and "fine" categories of the gathered textures during the configuration of the operator in a proposed framework. We concatenated the features of two pyramid representations with the CFS information of the original texture (PCFS). In addition, we combine the PCFS information with our last proposed feature selection approach to improve the efficiency of the CFS. We evaluated the proposed feature extraction method with classification of one of the well-known data set, Outex. Experimental results depict satisfactory performance of the CFS with very low-dimensional feature length.</p>

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33

Mocofan, Muguras, and Vasiu Radu. "A 3D Approach to Co-Occurrence Matrix Features Used in Dynamic Textures Indexing." Advanced Engineering Forum 8-9 (June 2013): 516–26. http://dx.doi.org/10.4028/www.scientific.net/aef.8-9.516.

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Анотація:

The area of applications of dynamic texture is increasingly wide: video surveillance, transaction systems, medical application and video synthesis. The paper presents an indexing model in large databases of dynamics texture using the co-occurrence matrix features. The data from the video sequence that represents the dynamic texture are loaded in a 3D matrix. The application of the co-occurrence matrix is performed for each frame of the data parallelepiped covered in 3 directions. This enables/facilitates the integration of the temporal features of the dynamic texture in the mathematical description of the behaviour. Additionally, we use more translations to compute the indexing vector from the 2D+T space of dynamic textures.

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34

Bunge, H. J. "Partial Texture Analysis." Textures and Microstructures 12, no. 1-3 (January 1, 1990): 47–63. http://dx.doi.org/10.1155/tsm.12.47.

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Анотація:

The texture is the orientation distribution function of the crystallites of a polycrystalline sample. Being a continuous function of three variables, it requires a large number of values for its complete description. The texture can be expressed in terms of a series expansion. It then requires a large number of coefficients for its complete description. When all functional values or all coefficients are determined from experimental measurements we speak of complete texture analysis. The most important methods for complete texture analysis are individual orientation measurements of a large number of crystals and pole figure measurement followed by pole figure inversion.If only a limited number of values of the texture function or a few of its coefficients are being determined we speak of “partial texture analysis”. The most important methods of partial texture analysis are the fixed angle texture analyzer and the measurement of the anisotropy of physical properties such as Young's modulus magnetic properties, thermal expansion and others.

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35

Materka, Andrzej, and Michał Strzelecki. "On The Effect Of Image Brightness And Contrast Nonuniformity On Statistical Texture Parameters." Foundations of Computing and Decision Sciences 40, no. 3 (September 1, 2015): 163–85. http://dx.doi.org/10.1515/fcds-2015-0011.

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Abstract Computerized texture analysis characterizes spatial patterns of image intensity, which originate in the structure of tissues. However, a number of texture descriptors also depend on local average image intensity and/or contrast. This variations, known as image nonuniformity (inhomogeneity) artefacts often occur, e.g. in MRI. Their presence may lead to errors in tissue description. This unwanted effect is explained in this paper using statistical texture descriptors applied for MRI slices of a normal and fibrotic liver. To reduce the errors, correction of image spatial nonuniformity prior to texture analysis is performed. The issue of sensitivity of popular texture parameters to image nonuniformities is discussed. It is illustrated by classification examples of natural Brodatz textures, digitally modified to account for inhomogeneities – modeled as smooth variations of image intensity and contrast. A set of texture features is identified which represent certain immunity to image inhomogeneities.

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Sitepu, H., and Heinz Günter Brokmeier. "Quantitative Texture Analysis and Phase Fraction of Nickel-Titanium Shape Memory Alloys by Means of Neutron Diffraction." Materials Science Forum 443-444 (January 2004): 267–70. http://dx.doi.org/10.4028/www.scientific.net/msf.443-444.267.

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The orientation distribution function (ODF) of the textured polycrystalline nickel titanium (NiTi) shape memory alloys (SMAs) was determined from the measured austenitic (B2)pole-figures by neutron diffraction. The texture results showed that neutron diffraction is an excellent tool to investigate the minor variation in the texture of NiTi alloys, which is very sensitive to the variation of the content of nickel in the materials. Moreover, the alloys crystallographic phase fraction and texture were calculated from Rietveld refinement with generalized spherical harmonic (GSH) description for the measured complete neutron powder diffraction (ND) spectrum, rather than a few isolated peaks, during in-situ temperature-induced martensitic transformation. The phase fraction results are consistent with the differential scanning calorimeter (DSC) curves.

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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 II: Model ODF for Rolling Textures." Textures and Microstructures 22, no. 3 (January 1, 1994): 169–75. http://dx.doi.org/10.1155/tsm.22.169.

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Sections of a three-dimensional Orientation Distribution Function (ODF) for the α-Fe rolling texture typical for most b.c.c. metals have been constructed on the basis of the proposed new method for ODF simulation through the representation of a crystallite orientation by nine rotations, only three of which are varied for a given component. The description of texture by superposition of partial fibre components in used. A comparison of such a model ODF with an ODF reconstructed from experimental pole figures by series expansion is presented. As a result all really encountered textures can be simulated by variation of the crystallite spread parameters, texture axis positions, and predominant preferred orientations in terms of a common approach.

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Sitepu, H., and Heinz Günter Brokmeier. "Use of Neutron Diffraction for Describing Texture of Isostatically-Pressed Molybdite Powders." Solid State Phenomena 105 (July 2005): 83–88. http://dx.doi.org/10.4028/www.scientific.net/ssp.105.83.

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The modelling and/or describing of texture (i.e. preferred crystallographic orientation (PO)) is of critical importance in powder diffraction analysis - for structural study and phase composition. In the present study, the GSAS Rietveld refinement with generalized spherical harmonic (GSH) was used for describing isostatically-pressed molybdite powders neutron powder diffraction data collected in the ILL D1A instrument. The results showed that for texture in a single ND data of molybdite the reasonable crystal structure parameters may be obtained when applying corrections to intensities using the GSH description. Furthermore, the WIMV method was used to extract the texture description directly from a simultaneous refinement with 1368 whole neutron diffraction patterns taken from the sample held in a variety of orientations in the ILL D1B texture goniometer. The results provided a quantitative description of the texture refined simultaneously with the crystal structure. Finally, the (002) molybdite pole-figures were measured using the GKSS TEX2 texture goniometer. The results showed that neutron diffraction is an excellent tool to investigate the texture in molybdite.

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39

Tagawa, Norio, Takefumi Hayashi, and Atsunobu Mori. "Effects of Moving Three-Dimensional Nano-Textured Disk Surfaces on Thin Film Gas Lubrication Characteristics for Flying Head Slider Bearings in Magnetic Disk Storage." Journal of Tribology 123, no. 1 (July 25, 2000): 151–58. http://dx.doi.org/10.1115/1.1326442.

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This paper describes the effects of moving three-dimensional nano-textured or patterned disk surfaces on thin film gas lubrication characteristics for flying head slider bearings in magnetic disk storage. In order to perform the most realistic simulation of slider flying characteristics over the textured disk surfaces, the direct numerical simulation method is used, instead of using various averaging techniques. Therefore, a deterministic description of the texture is adopted in this study. A dynamic analysis of the slider responses can be carried out, by solving the air bearing equation based on the linearized Boltzmann equation with the equations of motion of the slider under the excitation of the moving texture simultaneously. The slider’s dynamic responses to moving spaced bumps disk surfaces, including both the circumferentially and radially ridged disk surfaces, are computed systematically and basic slider dynamics over patterned disk surfaces is investigated. The effects of the texture area ratios (= texture width/texture pitch) in the circumferential and radial directions on the slider spacing dynamic modulations as well as the slider static flying characteristics are also studied. Furthermore, the effects of three kinds of texture patterns on the slider flying characteristics are investigated. Considering those simulation results, the design optimization for the texture pattern that minimizes not only the slider static flying height increase but also spacing dynamic modulations is discussed in order to achieve ultra-high density proximity magnetic recording.

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40

Gao, RongHua, DeHui Kong, and BaoCai Yin. "An improved description method of the bumpy texture." Science in China Series F: Information Sciences 52, no. 3 (March 2009): 523–28. http://dx.doi.org/10.1007/s11432-009-0070-4.

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41

Xu, Yong, Sibin Huang, Hui Ji, and Cornelia Fermüller. "Scale-space texture description on SIFT-like textons." Computer Vision and Image Understanding 116, no. 9 (September 2012): 999–1013. http://dx.doi.org/10.1016/j.cviu.2012.05.003.

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Ganesan, L., and P. Bhattacharyya. "A new statistical approach for micro texture description." Pattern Recognition Letters 16, no. 5 (May 1995): 471–78. http://dx.doi.org/10.1016/0167-8655(95)00123-x.

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43

Graven, Torø, Iain Emsley, Nicola Bird, and Susan Griffiths. "Improved access to museum collections without vision: How museum visitors with very low or no vision perceive and process tactile–auditory pictures." British Journal of Visual Impairment 38, no. 1 (October 3, 2019): 79–103. http://dx.doi.org/10.1177/0264619619874833.

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This study investigated how museum visitors with very low or no vision perceived and processed tactile pictures and/or audio-descriptions of visual paintings. Two visual paintings were selected and a focus group was established ( N = 8). Qualitative interview and observation data were collected. This study found two types of museum visitors: those who explored the tactile picture first and those who rather listened to the audio-description. When exploring each element in the tactile picture, they all started by exploring the element’s global (shape) outline and, when struggling to recognise it, turned to the audio-description. They preferred the audio-description to start describing where their fingers were. Tactile texture attracted their attention, sparked their curiosity, and enabled them to create a mental image of the tactile picture, but also confused them. They preferred the global (element shape) outline to be straightened out, so that curves become angular, and texture only for targeting certain elements.

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Mirjalili, Fereshteh, and Jon Yngve Hardeberg. "Appearance perception of textiles: a tactile and visual texture study." Color and Imaging Conference 2019, no. 1 (October 21, 2019): 43–48. http://dx.doi.org/10.2352/issn.2169-2629.2019.27.9.

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Texture analysis and characterization based on human perception has been continuously sought after by psychology and computer vision researchers. However, the fundamental question of how humans truly perceive texture still remains. In the present study, using a series of textile samples, the most important perceptual attributes people use to interpret and evaluate the texture properties of textiles were accumulated through the verbal description of texture by a group of participants. Smooth, soft, homogeneous, geometric variation, random, repeating, regular, color variation, strong, and complicated were ten of the most frequently used words by participants to describe texture. Since the participants were allowed to freely interact with the textiles, the accumulated texture properties are most likely a combination of visual and tactile information. Each individual texture attribute was rated by another group of participants via rank ordering. Analyzing the correlations between various texture attributes showed strong positive and negative correlations between some of the attributes. Principal component analysis on the rank ordering data indicated that there is a clear separation of perceptual texture attributes in terms of homogeneity and regularity on one hand, and non-homogeneity and randomness on the other hand.

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45

Ghahremani, Morteza, Yitian Zhao, Bernard Tiddeman, and Yonghuai Liu. "Interwoven texture-based description of interest points in images." Pattern Recognition 113 (May 2021): 107821. http://dx.doi.org/10.1016/j.patcog.2021.107821.

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46

Jiang, Yue. "Texture description based on multiresolution moments of image histograms." Optical Engineering 47, no. 3 (March 1, 2008): 037005. http://dx.doi.org/10.1117/1.2894149.

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47

Xiaopeng Hong, Guoying Zhao, Matti Pietikainen, and Xilin Chen. "Combining LBP Difference and Feature Correlation for Texture Description." IEEE Transactions on Image Processing 23, no. 6 (June 2014): 2557–68. http://dx.doi.org/10.1109/tip.2014.2316640.

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48

Mehta, Rakesh, and Karen Egiazarian. "Rotation Invariant Texture Description Using Symmetric Dense Microblock Difference." IEEE Signal Processing Letters 23, no. 6 (June 2016): 833–37. http://dx.doi.org/10.1109/lsp.2016.2561311.

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49

Ganesan, L., and P. Bhattacharyya. "A statistical design of experiments approach for texture description." Pattern Recognition 28, no. 1 (January 1995): 99–105. http://dx.doi.org/10.1016/0031-3203(94)00081-v.

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Zeng, Hui, Rui Zhang, Mingming Huang, and Xiuqing Wang. "Compact Local Directional Texture Pattern for Local Image Description." Advances in Multimedia 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/360186.

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This paper presents an effective local image feature region descriptor, called CLDTP descriptor (Compact Local Directional Texture Pattern), and its application in image matching and object recognition. The CLDTP descriptor encodes the directional and contrast information in a local region, so it contains the gradient orientation information and the gradient magnitude information. As the dimension of the CLDTP histogram is much lower than the dimension of the LDTP histogram, the CLDTP descriptor has higher computational efficiency and it is suitable for image matching. Extensive experiments have validated the effectiveness of the designed CLDTP descriptor.

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