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Journal articles on the topic 'Texture description'

Author: Grafiati

Published: 11 March 2023

Last updated: 31 July 2025

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1

Reynolds, Craig. "Interactive Evolution of Camouflage." Artificial Life 17, no. 2 (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. Nes

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2

Eschner, Th, and J. J. Fundenberger. "Application of Anisotropic Texture Components." Textures and Microstructures 28, no. 3-4 (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

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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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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

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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 (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 proper

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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 (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 rul

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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 (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 W

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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 (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

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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 (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

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

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11

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 (2014): 773–84. http://dx.doi.org/10.21917/ijivp.2014.0112.

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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 i

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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 (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 gr

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ANH, V. V., F. GRAS, and H. T. TSUI. "MULTIFRACTAL DESCRIPTION OF NATURAL SCENES." Fractals 04, no. 01 (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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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 (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

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16

MISS., NAMRATA N. RADE. "IMAGE TEXTURE CLASSIFICATION: SURF WITH SVM." IJIERT - International Journal of Innovations in Engineering Research and Technology 4, no. 7 (2017): 43–47. https://doi.org/10.5281/zenodo.1459102.

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<strong>Nowadays,various approaches of texture classification have been developed which works on acquired image features and separate them into different classes by using a specific classifier . This paper gives a state - of - the - art texture classification technique called Speeded up Robust Features (SURF) with SVM (Support Vector Machine) classifier. In this concept,image data representation is accomplished by capturing feature s in the form of key - points. SURF uses determinant of Hessian matrix to achieve point of interests on which description and classification is carried out. This me

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17

Katkar, Girish, and Pravin Ghosekar. "TexRet." International Journal of Information Systems and Social Change 3, no. 1 (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 texture

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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 an

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19

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

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20

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 (1994): 586–88. http://dx.doi.org/10.1109/20.312343.

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21

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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22

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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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 descri

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23

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 su

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Akoushideh, Alireza. "CFS: An effective statistical texture descriptor." Indonesian Journal of Electrical Engineering and Computer Science 19, no. 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 r

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Alireza, Akoushideh, and Modabernia Mohamadreza. "CFS: an effective statistical texture descriptor." Indonesian Journal of Electrical Engineering and Computer Science (IJEECS) 19, no. 1 (2020): 553–62. https://doi.org/10.11591/ijeecs.v19.i1.pp553-562.

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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 representation

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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 (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 pr

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

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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 numb

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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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29

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 (2015): 871–75. http://dx.doi.org/10.1109/lsp.2014.2374608.

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30

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

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31

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

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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 (1989): 150–66. http://dx.doi.org/10.1016/0734-189x(89)90130-8.

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

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

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

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

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

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38

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 th

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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 (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 si

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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 t

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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 (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

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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 (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

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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 (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 compli

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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 (2009): 523–28. http://dx.doi.org/10.1007/s11432-009-0070-4.

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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 (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 (1995): 471–78. http://dx.doi.org/10.1016/0167-8655(95)00123-x.

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Díaz, Adalberto Gabriel. "Analysis of periodic structures with Fourier description and neuronal network." Revista Facultad de Ingeniería Universidad de Antioquia, no. 40 (July 31, 2014): 106–17. http://dx.doi.org/10.17533/udea.redin.20152.

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This work is developed in a project of textile lattices inspection. The structure of a superficial texture is manifested with a behavior on the base of a model known as pattern which, is associated with a group of characteristics that define it as such. The identification process and classification of shortcomings in the texture consists on establishing a region of conformity in the coordinated space defined by the pattern’s characteristics. The reduction process of this m-dimensional space, helps to its identification in an n-dimensional space, such that m > n, where the classification sys

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Swirad, Slawomir. "Changes in Areal Surface Textures Due to Ball Burnishing." Materials 16, no. 17 (2023): 5904. http://dx.doi.org/10.3390/ma16175904.

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The description of the areal texture of the surface is more comprehensive than that of roughness profiles. Ball burnishing led to an improvement in surface quality. In this work, the changes in areal surface textures due to ball burnishing were studied. Three surfaces of steel, two after milling and one after grinding, were subjected to ball burnishing. In the burnishing process, the burnishing pressure and width were variable parameters. Surface topographies before and after burnishing were measured using a white light interferometer. Ball burnishing was found to lead to a decrease in surface

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Baiq, Nurul Aisyah, Syamsuardi, and Nurainas. "Morphological Differentiation between Nibung (Oncosperma tigillarium (Jack) Ridl), A Mascot Flora of Riau Province, and Its Closed Related Species, Bayas (Oncosperma horridum (Griff) Scheffer)." World Journal of Advanced Research and Reviews 18, no. 2 (2023): 621–26. https://doi.org/10.5281/zenodo.8405852.

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Nibung and Bayas plants are members of the Arecaceae family, which is characterized by monocots, solitary or clumped stems, pinnate compound leaves, and flower frames. The province of Riau contains Nibung (<em>O. tigillarium</em>). Nibung (<em>Oncosperma tigillarium</em>) and Bayas (<em>Oncosperma horridum</em>) are members of the same family, but their morphology and habitat are distinct. This study seeks to determine the morphological distinctions between Nibung and Bayas plants found in the province of Riau, as well as their distribution. This investigation utilized field samples collected

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Wu, Xiao Sheng, and Jun Ding Sun. "A Brief Study on a Novel Texture Spectrum Descriptor for Material Images." Applied Mechanics and Materials 63-64 (June 2011): 507–10. http://dx.doi.org/10.4028/www.scientific.net/amm.63-64.507.

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The local binary pattern (LBP) operator has been proved to be theoretically simple yet very effective for texture description. However, it lacks the full description of texture in a region and produces a rather long histogram. A novel texture spectrum descriptor was proposed to alleviate these limitations in the paper. It uses the relation of 3 pixels in an 8-neighborhood, the center and the center-symmetric pixels, to define the local texture patterns. The new operator fully uses the texture information contained in the 8-neighbourhood and produces a rather short histogram. On the other hand,

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