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Ataky, Steve Tsham Mpinda, Diego Saqui, Jonathan de Matos, Alceu de Souza Britto Junior, and Alessandro Lameiras Koerich. "Multiscale Analysis for Improving Texture Classification." Applied Sciences 13, no. 3 (January 18, 2023): 1291. http://dx.doi.org/10.3390/app13031291.
Повний текст джерелаАнотація:
Information from an image occurs over multiple and distinct spatial scales. Image pyramid multiresolution representations are a useful data structure for image analysis and manipulation over a spectrum of spatial scales. This paper employs the Gaussian–Laplacian pyramid to separately treat different spatial frequency bands of a texture. First, we generate three images corresponding to three levels of the Gaussian–Laplacian pyramid for an input image to capture intrinsic details. Then, we aggregate features extracted from gray and color texture images using bioinspired texture descriptors, information-theoretic measures, gray-level co-occurrence matrix feature descriptors, and Haralick statistical feature descriptors into a single feature vector. Such an aggregation aims at producing features that characterize textures to their maximum extent, unlike employing each descriptor separately, which may lose some relevant textural information and reduce the classification performance. The experimental results on texture and histopathologic image datasets have shown the advantages of the proposed method compared to state-of-the-art approaches. Such findings emphasize the importance of multiscale image analysis and corroborate that the descriptors mentioned above are complementary.
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Hanmandlu, Madasu, and Anirban Das. "Content-based Image Retrieval by Information Theoretic Measure." Defence Science Journal 61, no. 5 (September 2, 2011): 415. http://dx.doi.org/10.14429/dsj.61.1177.
Повний текст джерелаАнотація:
<p>Content-based image retrieval focuses on intuitive and efficient methods for retrieving images from databases based on the content of the images. A new entropy function that serves as a measure of information content in an image termed as 'an information theoretic measure' is devised in this paper. Among the various query paradigms, 'query by example' (QBE) is adopted to set a query image for retrieval from a large image database. In this paper, colour and texture features are extracted using the new entropy function and the dominant colour is considered as a visual feature for a particular set of images. Thus colour and texture features constitute the two-dimensional feature vector for indexing the images. The low dimensionality of the feature vector speeds up the atomic query. Indices in a large database system help retrieve the images relevant to the query image without looking at every image in the database. The entropy values of colour and texture and the dominant colour are considered for measuring the similarity. The utility of the proposed image retrieval system based on the information theoretic measures is demonstrated on a benchmark dataset.</p><p><strong>Defence Science Journal, 2011, 61(5), pp.415-430</strong><strong><strong>, DOI:http://dx.doi.org/10.14429/dsj.61.1177</strong></strong></p>
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Thabt Saleh, Kawther, Raniah Ali Mustafa, and Haitham Salman Chyad. "Human ear print recognition based on fusion of difference theoretic texture and gradient direction pattern features." Indonesian Journal of Electrical Engineering and Computer Science 29, no. 2 (February 1, 2023): 1017. http://dx.doi.org/10.11591/ijeecs.v29.i2.pp1017-1029.
Повний текст джерелаАнотація:
<span lang="EN-US">Human ear recognition can be defined as a branch of biometrics that uses images of the ears to identify people. This paper provides a new ear print recognition approach depending on the combination of gradient direction pattern (GDP2) and difference theoretic texture features (DTTF) features. The region of interest (ROI), the gray scale of the ear print was cut off, noise removal by the median filter, histogram equalization, and local normalization (LN) are the first steps in this approach. After the image has been processed, it is used as input for the fusion of GDP2 and DTTF for extracting the features of ear print images. Lastly, the Gaussian distribution (GD) was utilized to compute the distance among fusion feature vectors (FV) for ear print images for recognizing ear print images for people using a set of images that had been trained and tested. The unconstrained ear recognition challenge (UERC) database, which comprises 330 subjects for ear print images, provides the approach that was suggested by employing ear print databases. Furthermore, experimental results on images from a benchmark dataset reveal that statistical-rely super-resolution methods outperform other algorithms in ear recognition accuracy, which was around 93.70% in this case.</span>
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Lamont, Alexandra, and Nicola Dibben. "Motivic Structure and the Perception of Similarity." Music Perception 18, no. 3 (2001): 245–74. http://dx.doi.org/10.1525/mp.2001.18.3.245.
Повний текст джерелаАнотація:
This paper presents a theoretical and empirical investigation into the ways in which different listeners perceive similarity relationships in different kinds of music. We first extend the current understanding of similarity relations in music by drawing together theory and evidence from general cognitive psychology, cognitive psychology of music, and music theory. In the empirical study, trained musicians and nonmusicians rated the similarity of pairs of extracts from piano pieces by Beethoven (Sonata op. 10, no. 1, first movement) and Schoenberg (Klavierstüück op. 33a) and provided adjective ratings for each extract. Similarity judgments were found to be context-specific and roughly equivalent for both types of listener, and were primarily based on more "surface" features such as dynamics, articulation, texture, and contour rather than on "deeper" features such as motivic or harmonic relationships. The implications for music-theoretic views of similarity are discussed.
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Xu, Yuanping, Paul J. Scott, and Jiaoling Zheng. "Novel Research on a Fibration Knowledge-Based Solution for Engineered Surface Tolerances within the Modern GPS Context." International Journal of Software Engineering and Knowledge Engineering 26, no. 05 (June 2016): 845–95. http://dx.doi.org/10.1142/s0218194016500297.
Повний текст джерелаАнотація:
Geometrical product specification and verification (GPS) is the only worldwide tolerancing specification language available for unambiguously communicating functional requirements and precisely expressing geometrical tolerances of workpieces on technical drawings. Although the understanding and application of GPS are of vital importance for modern manufacturing industry, the latest GPS standards contain huge amounts of data, information and knowledge that are obscurely linked together, so they are very difficult for designers, engineers and metrologists to understand, remember and comprehensively use them. This is especially true for surface texture that is one of the most complicated geometrical characteristics in GPS domain. To overcome the implementation barrier of GPS surface texture, a novel knowledge-based solution supported formally by a unified knowledge modeling of the complete GPS surface texture standard chain has been researched. This knowledge model can serve as a formal basis for developing more intelligent and reliable tolerance design and measurement systems. This paper starts with an introduction on how to build up a formal knowledge model based on category theory, called categorical fibration model, to unified acquisition and representation of all surface texture knowledge. It then moves on to define a recursive knowledge refinement process for the devised model to enable the recursive multi-level GPS knowledge transformations and refinements. This knowledge model is validated naturally by checking rules identified from category theoretic notions. Finally, this paper presents a general framework for applying the modeled GPS knowledge in practice, from the aspects of the integrated and complete modeling of all surface texture knowledge, detailed knowledge acquisition and modeling examples for four sub-knowledge bases of the proposed software solution, core functional features of this software, and a comprehensive case study for testing its validity and usability. This knowledge-based solution facilitates life cycle collaboration and integration of the complete GPS surface texture chain to reduce product development cost and lead time, and to ensure functional requirements of geometrical workpieces.
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Demir, Güleser Kalaycı. "A Spectral Graph Theoretical Approach to Oriented Energy Features." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 01 (January 2017): 1755001. http://dx.doi.org/10.1142/s0218001417550011.
Повний текст джерелаАнотація:
In this work, we propose a novel method for determining oriented energy features of an image. Oriented energy features, useful for many machine vision applications like contour detection, texture segmentation and motion analysis, are determined from the filters whose outputs are enhanced at the edges of the image at a given orientation. We use the eigenvectors and eigenvalues of graph Laplacian for determining the oriented energy features of an image. Our method is based on spectral graph theoretical approach in which a graph is assigned complex-valued edge weights whose phases encode orientation information. These edge weights give rise to a complex-valued Hermitian Laplacian whose spectrum enables us to extract oriented energy features of the image. We perform a set of numerical experiments to determine the efficiency and characteristics of the proposed method. In addition, we apply our feature extraction method to texture segmentation problem. We do this in comparison with other known methods, and show that our method performs better for various test textures.
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Dong, Wu, Hongxia Bie, Likun Lu, and Yeli Li. "Blind Quality Evaluation for Screen Content Images Based on Regionalized Structural Features." Algorithms 13, no. 10 (October 11, 2020): 257. http://dx.doi.org/10.3390/a13100257.
Повний текст джерелаАнотація:
Currently, screen content images (SCIs) are widely used in our modern society. However, since SCIs have distinctly different properties compared to natural images, traditional quality assessment methods of natural images cannot precisely evaluate the quality of SCIs. Thus, we propose a blind quality evaluation method for SCIs based on regionalized structural features that are closely relevant to the intrinsic quality of SCIs. Firstly, the features of textual and pictorial regions of SCIs are extracted separately. For textual regions, since they contain noticeable structural information, we propose improved histograms of oriented gradients extracted from multi-order derivatives as structural features. For pictorial regions, since human vision is sensitive to texture information and luminance variation, we adopt texture as the structural feature; meanwhile, luminance is used as the auxiliary feature. The local derivative pattern and the shearlet local binary pattern are used to extract texture in the spatial and shearlet domains, respectively. Secondly, to derive the quality of textual and pictorial regions, two mapping functions are respectively trained from their features to subjective values. Finally, an activity weighting strategy is proposed to combine the quality of textual and pictorial regions. Experimental results show that the proposed method achieves better performance than the state-of-the-art methods.
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Gao, Peng, Ian MacKay, Andrea Gruber, Joshua Krantz, Leonardo Piccolo, Giovanni Lucchetta, Riccardo Pelaccia, Leonardo Orazi, and Davide Masato. "Wetting Characteristics of Laser-Ablated Hierarchical Textures Replicated by Micro Injection Molding." Micromachines 14, no. 4 (April 16, 2023): 863. http://dx.doi.org/10.3390/mi14040863.
Повний текст джерелаАнотація:
Texturing can be used to functionalize the surface of plastic parts and, in particular, to modify the interaction with fluids. Wetting functionalization can be used for microfluidics, medical devices, scaffolds, and more. In this research, hierarchical textures were generated on steel mold inserts using femtosecond laser ablation to transfer on plastic parts surface via injection molding. Different textures were designed to study the effects of various hierarchical geometries on the wetting behavior. The textures are designed to create wetting functionalization while avoiding high aspect ratio features, which are complex to replicate and difficult to manufacture at scale. Nano-scale ripples were generated over the micro-scale texture by creating laser-induced periodic surface structures. The textured molds were then replicated by micro-injection molding using polypropylene and poly(methyl methacrylate). The static wetting behavior was investigated on steel inserts and molded parts and compared to the theoretical values obtained from the Cassie–Baxter and Wenzel models. The experimental results showed correlations between texture design, injection molding replication, and wetting properties. The wetting behavior on the polypropylene parts followed the Cassie–Baxter model, while for PMMA, a composite wetting state of Cassie–Baxter and Wenzel was observed.
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Chen, L. R., X. Z. Xiao, L. Yu, H. J. Chu, and H. L. Duan. "Texture evolution and mechanical behaviour of irradiated face-centred cubic metals." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474, no. 2210 (February 2018): 20170604. http://dx.doi.org/10.1098/rspa.2017.0604.
Повний текст джерелаАнотація:
A physically based theoretical model is proposed to investigate the mechanical behaviour and crystallographic texture evolution of irradiated face-centred cubic metals. This model is capable of capturing the main features of irradiated polycrystalline materials including irradiation hardening, post-yield softening and plasticity localization. Numerical results show a good agreement with experimental data for both unirradiated and irradiated stress–strain relationships. The study of crystallographic texture reveals that the initial randomly distributed texture of unirradiated metals under tensile loading can evolve into a mixture of [111] and [100] textures. Regarding the irradiated case, crystallographic texture develops in a different way, and an extra part of [110] texture evolves into [100] and [111] textures. Thus, [100] and [111] textures become dominant more quickly compared with those of the unirradiated case for the reason that [100] and [111]-oriented crystals have higher strength, and their plastic deformation behaviours are more active than other oriented crystals. It can be concluded that irradiation-induced defects can affect both the mechanical behaviour and texture evolution of metals, both of which are closely related to irradiation hardening.
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Han, Jiaying, Yiqi Jiang, Xinrui Li, and Qing Li. "Conical Grinding Wheel Ultrasonic-Assisted Grinding Micro-Texture Surface Formation Mechanism." Machines 11, no. 4 (March 27, 2023): 428. http://dx.doi.org/10.3390/machines11040428.
Повний текст джерелаАнотація:
The rotating ultrasonic-assisted grinding (RUAG) experiment of the conical grinding wheel generated the intermittent pit-shaped micro-texture on the surface of the workpiece, reducing thermal damage and improving the lubrication characteristics compared with conventional grinding (CG). To further optimize the surface properties, this paper studied the formation mechanism of micro-texture. This study used as basis the theory that micro-debris volume equals the macroscopic material removal one to establish the mathematical equation of grinding depth. Thereafter, formulas of micro-texture feature parameters, including pit length, pit depth, and texture spacing were deduced. The solved microscopic grinding depth was alternatingly positive and negative, indicating that the alternating separation between the grinding grain and workpiece caused intermittent pits in the grinding. Through response surface analysis (RSA), this paper analyzed the relationships among macroscopic grinding depth, micro-texture feature parameters, and machining parameters (i.e., amplitude, feed rate, and rotational speed). Single-factor experiments of machining parameters, with finite element simulation and experiment methods, were performed to verify the theoretical micro-texture features. The simulated program formed three-dimensional surfaces with micro-textures. Their measurement results were consistent with the theoretical ones. Experimental results proved that the range of pit length covers the theoretical ones, further verifying the accuracy of the grinding depth model. For this grinding wheel, the 8–10 μm amplitude was optimal for better roughness, lubrication, and thermal damage. Roughness was improved when increasing the rotational speed or reducing the feed rate based on the experiment. If the rotational speed and feed rate exceed the limiting values, then continuous grinding will break down the abrasive grains and even damage the cubic boron nitride (CBN) coating. Experimental results likewise showed that the pit shape was closely related to the surface properties, which deserves further investigation.
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Rout, Nibedita, Jinlian Hu, George Baciu, Priyabrata Pattanaik, K. Nakkeeran, and Asimananda Khandual. "Color and Texture Analysis of Textiles Using Image Acquisition and Spectral Analysis in Calibrated Sphere Imaging System-II." Electronics 12, no. 9 (May 6, 2023): 2135. http://dx.doi.org/10.3390/electronics12092135.
Повний текст джерелаАнотація:
The extended application of device-dependent systems’ vision is growing exponentially, but these systems face challenges in precisely imitating the human perception models established by the device-independent systems of the Commission internationale de l’éclairage (CIE). We previously discussed the theoretical treatment and experimental validation of developing a calibrated integrated sphere imaging system to imitate the visible spectroscopy environment. The RGB polynomial function was derived to obtain a meaningful interpretation of color features. In this study, we dyed three different types of textured materials in the same bath with a yellow reactive dye at incremental concentrations to see how their color difference profiles tested. Three typical cotton textures were dyed with three ultra-RGB remozol reactive dyes and their combinations. The color concentration ranges of 1%, 2%, 3%, and 4% were chosen for each dye, followed by their binary and ternary mixtures. The aim was to verify the fundamental spectral feature mapping in various imaging color spaces and spectral domains. The findings are quite interesting and help us to understand the ground truth behind working in two domains. In addition, the trends of color mixing, CIE color difference, CIExy (chromaticity) color gamut, and RGB gamut and their distinguishing features were verified. Human perception accuracy was also compared in both domains to clarify the influence of texture. These fundamental experiments and observations on human perception and calibrated imaging color space could clarify the expected precision in both domains.
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Wang, Min, Congyan Lang, Liqian Liang, Songhe Feng, Tao Wang, and Yutong Gao. "Fine-Grained Semantic Image Synthesis with Object-Attention Generative Adversarial Network." ACM Transactions on Intelligent Systems and Technology 12, no. 5 (October 31, 2021): 1–18. http://dx.doi.org/10.1145/3470008.
Повний текст джерелаАнотація:
Semantic image synthesis is a new rising and challenging vision problem accompanied by the recent promising advances in generative adversarial networks. The existing semantic image synthesis methods only consider the global information provided by the semantic segmentation mask, such as class label, global layout, and location, so the generative models cannot capture the rich local fine-grained information of the images (e.g., object structure, contour, and texture). To address this issue, we adopt a multi-scale feature fusion algorithm to refine the generated images by learning the fine-grained information of the local objects. We propose OA-GAN, a novel object-attention generative adversarial network that allows attention-driven, multi-fusion refinement for fine-grained semantic image synthesis. Specifically, the proposed model first generates multi-scale global image features and local object features, respectively, then the local object features are fused into the global image features to improve the correlation between the local and the global. In the process of feature fusion, the global image features and the local object features are fused through the channel-spatial-wise fusion block to learn ‘what’ and ‘where’ to attend in the channel and spatial axes, respectively. The fused features are used to construct correlation filters to obtain feature response maps to determine the locations, contours, and textures of the objects. Extensive quantitative and qualitative experiments on COCO-Stuff, ADE20K and Cityscapes datasets demonstrate that our OA-GAN significantly outperforms the state-of-the-art methods.
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Liu, Xiaoliang, Xiaoming Huang, Jian Zhang, and Weitao Sun. "Influence mechanism of surface texture on the output performance of an ultrasonic motor." Industrial Lubrication and Tribology 73, no. 5 (June 10, 2021): 750–55. http://dx.doi.org/10.1108/ilt-02-2021-0053.
Повний текст джерелаАнотація:
Purpose The purpose of this study is to investigate the influence mechanism of different interface component surface textures on the ultrasonic motor (USM) output performance. Design/methodology/approach The energy transmission mechanism of the traveling-wave ultrasonic motor 60 (TRUM-60) was numerically and experimentally investigated by fabricating dimple textures with different feature types on the friction material and the stator. Findings Textured friction material can increase the contact range effectively, and thus, can improve the friction characteristics of the interface and the output performance of the TRUM-60. The experimental results verified the expected influence mechanism and demonstrated that the use of either a textured friction material or stator has a very different effect on USM output performance. A textured PI-based friction material improved the TRUM-60 output performance, resulting in a maximum energy conversion efficiency of 57.11%. However, a textured stator degraded the TRUM-60 output performance, resulting in a minimum energy conversion efficiency of only 44.92%. Originality/value The results of this study provide a theoretical foundation for improved USM designs with textured interfaces.
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Rout, Nibedita, George Baciu, Priyabrata Pattanaik, K. Nakkeeran, and Asimananda Khandual. "Color and Texture Analysis of Textiles Using Image Acquisition and Spectral Analysis in Calibrated Sphere Imaging System-I." Electronics 11, no. 23 (November 24, 2022): 3887. http://dx.doi.org/10.3390/electronics11233887.
Повний текст джерелаАнотація:
Numerous imaging applications and analyses demand human perception, and color space transformation of device-dependent tri-band color interpretation (RGB) to device-independent CIE color space standards needs human intervention. The imaging acquisition environment, theoretical conversion errors, viewing geometry, well-defined illumination uniformity, and calibration protocols limit their precision and applicability. It is unfortunate that in most image processing applications, the spectral data are either unavailable or immeasurable. This study is based on developing a novel integrating sphere imaging system and experimentation with textiles’ controlled variation of texture and color. It proposes a simple calibration technique and describes how unique digital color signatures can be derived from calibrated RGB derivatives to extract the best features for color and texture. Additionally, an alter-ego of reflectance function, missing in the imaging domain, is suggested that could be helpful for visualization, identification, and application for qualitative and quantitative color-texture analysis. Our further investigation revealed promising colorimetric results while validating color characterization and different color combinations over three textures.
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Igual, Francisco D., Rafael Mayo, Timothy DR Hartley, Ümit V. Çatalyürek, Antonio Ruiz, and Manuel Ujaldon. "Color and texture analysis using emerging parallel architectures." International Journal of High Performance Computing Applications 25, no. 4 (December 22, 2010): 404–27. http://dx.doi.org/10.1177/1094342010390340.
Повний текст джерелаАнотація:
While image texture is effective for use in pattern-recognition and image-analysis algorithms, textural features are time-consuming to calculate on standard CPUs. Therefore, we present novel implementations of textural-feature algorithms on graphics processors (GPUs), enabling fast color and texture analysis. Since different textural-feature calculations exhibit diverse characteristics, we focus on using general and algorithm-specific techniques to exploit the inherent parallelism and computational power of a GPU. Common operations required during the textural-feature pipeline range from streaming computations to recursive procedures, from arithmetically intensive transcendental functions to matrix operations. Some of these kernels are well-suited to GPUs, while others require considerable programming effort to fully exploit the memory hierarchy due to their memory-usage patterns. In this paper, different strategies for computing textural features on GPUs are compared with counterpart implementations on multicore CPUs, and experimental results show GPU results reaching a speedup of 500 times for certain operations.
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Ogleznev, V. V. "The Problem of Definition of Legal Concepts with “Open Texture”." Pravosudie / Justice 2, no. 1 (March 19, 2020): 150–61. http://dx.doi.org/10.37399/issn2686-9241.2020.1.150-161.
Повний текст джерелаАнотація:
Introduction: The article discusses the problems associated with the definition of legal concepts which have the feature of “open texture”. The introduction presents the nature and meaning of “open texture”, which is understood as a special kind of indeterminacy. Such concepts are considered in the form in which they were postulated in the works of the Austrian linguistic philosopher Friedrich Waismann and the British legal philosopher Herbert Hart. Theoretical Basis. Methods. It is contested that, in Hart’s interpretation, “open texture” appears in legal concepts in borderline cases, when the meaning of the term of “concept” becomes indeterminate, unclear, uncertain, and we do not know whether or not it should be applied. Such cases should be distinguished from clear-cut cases where such doubt does not arise. The methodological basis of the study is Hart’s thesis stating that legal concepts have “core” and “penumbra” of meaning. The “core” meaning indicates a set of certain conditions, in which the use of the term “concept” is clear, while a “penumbra” meaning refers to conditions in which the its use becomes less clear. “Open texture” in this case, is an irreducible feature of legal concepts. Results. The main result of the study is the assertion that “open texture” as an irreducible feature of legal concepts, can be disproved by changing its definition. It is shown that the most appropriate kind of definition of open-textured legal concepts is the definition or contextual definition, widely used in analytical philosophy.
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Ogleznev, V. V. "The Problem of Definition of Legal Concepts with “Open Texture”." Pravosudie / Justice 2, no. 1 (March 19, 2020): 150–61. http://dx.doi.org/10.37399/issn2686-9241.2020.1.150-161.
Повний текст джерелаАнотація:
Introduction: The article discusses the problems associated with the definition of legal concepts which have the feature of “open texture”. The introduction presents the nature and meaning of “open texture”, which is understood as a special kind of indeterminacy. Such concepts are considered in the form in which they were postulated in the works of the Austrian linguistic philosopher Friedrich Waismann and the British legal philosopher Herbert Hart. Theoretical Basis. Methods. It is contested that, in Hart’s interpretation, “open texture” appears in legal concepts in borderline cases, when the meaning of the term of “concept” becomes indeterminate, unclear, uncertain, and we do not know whether or not it should be applied. Such cases should be distinguished from clear-cut cases where such doubt does not arise. The methodological basis of the study is Hart’s thesis stating that legal concepts have “core” and “penumbra” of meaning. The “core” meaning indicates a set of certain conditions, in which the use of the term “concept” is clear, while a “penumbra” meaning refers to conditions in which the its use becomes less clear. “Open texture” in this case, is an irreducible feature of legal concepts. Results. The main result of the study is the assertion that “open texture” as an irreducible feature of legal concepts, can be disproved by changing its definition. It is shown that the most appropriate kind of definition of open-textured legal concepts is the definition or contextual definition, widely used in analytical philosophy.
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Yang, Yu Fen, Zhen Nan Han, and Jin Bao Li. "The Correlation of Assessment Parameters on Geometrical Features of SurfaceTexture." Advanced Materials Research 145 (October 2010): 49–56. http://dx.doi.org/10.4028/www.scientific.net/amr.145.49.
Повний текст джерелаАнотація:
The method quantitatively analyzing the correlation of assessment parameters on geometrical features of the surface texture with the approach degree theory of fuzzy mathematics is put forward, and the issue that the information provided by assessment parameters on indicating microscopic geometrical features of the surface texture is only analyzed qualitatively not quantitatively is solved accordingly. The experimental results clarify the correlation and the operational performance of the various assessment parameters; what’s more, it provides the reliable theoretical basis for the reasonable choice of the assessment parameters on microscopic geometrical features of the surface texture.
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Naseem, Imran, Duc-Son Pham, and Svetha Venkatesh. "A novel information theoretic approach to wavelet feature selection for texture classification." Computers & Electrical Engineering 39, no. 2 (February 2013): 319–25. http://dx.doi.org/10.1016/j.compeleceng.2012.11.003.
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Susan, Seba, and Madasu Hanmandlu. "Difference theoretic feature set for scale-, illumination- and rotation-invariant texture classification." IET Image Processing 7, no. 8 (November 1, 2013): 725–32. http://dx.doi.org/10.1049/iet-ipr.2012.0527.
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Chen, Chang, and Hua Zhu. "Visual-inertial SLAM method based on optical flow in a GPS-denied environment." Industrial Robot: An International Journal 45, no. 3 (May 21, 2018): 401–6. http://dx.doi.org/10.1108/ir-01-2018-0002.
Повний текст джерелаАнотація:
Purpose This study aims to present a visual-inertial simultaneous localization and mapping (SLAM) method for accurate positioning and navigation of mobile robots in the event of global positioning system (GPS) signal failure in buildings, trees and other obstacles. Design/methodology/approach In this framework, a feature extraction method distributes features on the image under texture-less scenes. The assumption of constant luminosity is improved, and the features are tracked by the optical flow to enhance the stability of the system. The camera data and inertial measurement unit data are tightly coupled to estimate the pose by nonlinear optimization. Findings The method is successfully performed on the mobile robot and steadily extracts the features on low texture environments and tracks features. The end-to-end error is 1.375 m with respect to the total length of 762 m. The authors achieve better relative pose error, scale and CPU load than ORB-SLAM2 on EuRoC data sets. Originality/value The main contribution of this study is the theoretical derivation and experimental application of a new visual-inertial SLAM method that has excellent accuracy and stability on weak texture scenes.
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Jurczyszyn, Kamil, Tomasz Gedrange, and Marcin Kozakiewicz. "Theoretical Background to Automated Diagnosing of Oral Leukoplakia: A Preliminary Report." Journal of Healthcare Engineering 2020 (September 14, 2020): 1–7. http://dx.doi.org/10.1155/2020/8831161.
Повний текст джерелаАнотація:
Oral leukoplakia represents the most common oral potentially malignant disorder, so early diagnosis of leukoplakia is important. The aim of this study is to propose an effective texture analysis algorithm for oral leukoplakia diagnosis. Thirty-five patients affected by leukoplakia were included in this study. Intraoral photography of normal oral mucosa and leukoplakia were taken and processed for texture analysis. Two features of texture, run length matrix and co-occurrence matrix, were analyzed. Difference was checked by ANOVA. Factor analysis and classification by the artificial neural network were performed. Results revealed easy possible differentiation leukoplakia from normal mucosa (p<0.05). Neural network discrimination shows full leukoplakia recognition (sensitivity 100%) and specificity 97%. This objective analysis in the neural network revealed that involving 3 textural features into optical analysis of the oral mucosa leads to proper diagnosis of leukoplakia. Application of texture analysis for leukoplakia is a promising diagnostic method.
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Kaneko, Hiroshi. "Fractal feature and texture analysis." Systems and Computers in Japan 19, no. 8 (August 1988): 28–37. http://dx.doi.org/10.1002/scj.4690190803.
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Hu, Yanzhu, Song Wang, and Xinbo Ai. "Research of the Vibration Source Tracking in Phase-Sensitive Optical Time-Domain Reflectometry Signals Based by Image Processing Method." Algorithms 11, no. 8 (August 2, 2018): 117. http://dx.doi.org/10.3390/a11080117.
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This paper aims to improve the source tracking efficiency of distributed vibration signals generated by phase-sensitive optical time-domain reflectometry (Φ-OTDR). Considering the two dimensions (time and length) of Φ-OTDR signals, the authors saved and processed these signals as images after particle filtering. The filtering method could save 0.1% of hard drive space without sacrificing the original features of the signals. Then, an integrated feature extraction method was proposed to further process the generated image. The method combines three individual extraction methods, namely, texture feature extraction, shape feature extraction and intrinsic feature extraction. Subsequently, the signal of each frame image was recognized to track the vibration source. To verify the effect of the proposed method, several experiments were carried out to compare it with popular and traditional approaches. The results show that: Hard drive space is greatly conserved by saving the distributed vibration signals as images; the proposed particle filter is a desirable way to screen the vibration signals for monitoring; the integrated feature extraction outperforms the individual extraction methods for texture features, shape features and intrinsic features; the proposed method has a better effect than other popular integrated feature extraction methods; and, the signal source tracking method has little impact on the positioning accuracy of the vibration source. The research findings provide important insights into the source tracking of Φ-OTDR signals.
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Ostapovich, Kirill V., and Peter V. Trusov. "Reduced Statistical Representation of Crystallographic Textures Based on Symmetry-Invariant Clustering of Lattice Orientations." Crystals 11, no. 4 (March 26, 2021): 336. http://dx.doi.org/10.3390/cryst11040336.
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As proven in numerous experimental and theoretical studies, physical and mechanical properties of materials are determined by their internal structure. In the particular case of polycrystalline metals and alloys, an important role is given to the orientation distributions of crystalline lattices, or, in other words, crystallographic textures. Physically reasonable models of texture formation are highly demanded in modern Material Science and Engineering since they can provide an efficient tool for designing polycrystalline products with improved operational characteristics. Models of interest can be obtained on the basis of statistical formulations of multilevel approaches and crystal elasto–visco–plasticity theories (in particular, Taylor–Bishop–Hill models and their various modifications are appropriate here). In such a framework, a representative volume element of a polycrystal is numerically implemented as a finite aggregate of crystallites (grains or subgrains) with a homogenized response at the macro-scale. Quantitative texture analysis of this aggregate requires estimating statistically stable features of the orientation distribution. The present paper introduces a clustering-based approach for executing this task with regard to preferred orientations. The proposed procedure operates with a weighted sample of orientations representing the aggregate and divides it into clusters, i.e., disjoint subsets of close elements. The closeness criterion is supposed to be defined with the help of a special pseudometric distance, which takes rotational symmetry of the crystalline lattice into account. A specific illustrative example is provided for better understanding the developed procedure. The texture in the clustered aggregate can be described reductively in terms of effective characteristics of distinguished clusters. Several possible reduced-form representations are considered and investigated from the viewpoint of aggregating elastic properties in application to some numerically simulated textures.
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Huang, Qicai. "Adaptive Extraction of Oil Painting Texture Features Based on Reaction Diffusion Equation." Advances in Mathematical Physics 2021 (November 3, 2021): 1–11. http://dx.doi.org/10.1155/2021/4464985.
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The oil painting retrieval technology based on the reaction diffusion equation has attracted widespread attention in the fields of oil painting processing and pattern recognition. The description and extraction of oil painting information and the classification method of oil paintings are two important processes in content-based oil painting retrieval. Inspired by the restoration and decomposition functional model of equal oil painting, we propose a reaction diffusion equation model. The new model contains two reaction diffusion equations with different principal parts. One principal part is total variation diffusion, which is used to remove noise. The other main part is thermal diffusion, which is used to modify the source term of the denoising reaction-diffusion equation to achieve the effect of protecting the texture of the oil painting. The interaction of the two reaction-diffusion equations finally achieves denoising while maintaining the boundaries and textures. Under the framework of the above reaction diffusion equation model, we introduce Laplace flow to replace the original total variation flow, so that the new denoising reaction diffusion equation combines the isotropic diffusion and total variation flow of the thermal reaction diffusion equation to achieve the effect of adaptive theoretical research. Using regularization methods and methods, we, respectively, get the well-posedness of the two model solutions, which provides the necessary preparation for numerical calculations. Based on the statistical theory and classification principles of support vector machines, combined with the characteristics of oil painting classification, the research and analysis are carried out from the three important aspects of kernel function, training algorithm, and multiclass classifier algorithm that affect the classification effect and speed. Numerical experiments show that the given filter model has a better processing effect on images with different types and different degrees of noise pollution. On this basis, an oil painting classification system based on texture features is designed, combined with an improved gray-level cooccurrence matrix algorithm and a multiclass support vector machine classification model, to extract, train, and classify oil paintings. Experiments with three types of oil paintings prove that the system can achieve a good oil painting classification effect. Different from the original model, the new model is based on the framework of reaction-diffusion equations. In addition, the new model has good effects in removing step effects, maintaining boundaries and denoising, especially in maintaining texture.
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Yang, Jian, and Jingfeng Guo. "Image Texture Feature Extraction Method Based on Regional Average Binary Gray Level Difference Co-occurrence Matrix." International Journal of Virtual Reality 10, no. 3 (January 1, 2011): 73–79. http://dx.doi.org/10.20870/ijvr.2011.10.3.2823.
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Texture feature is a measure method about relationship among the pixels in local area, reflecting the changes of image space gray levels. This paper presents a texture feature extraction method based on regional average binary gray level difference co-occurrence matrix, which combined the texture structural analysis method with statistical method. Firstly, we calculate the average binary gray level difference of eight-neighbors of a pixel to get the average binary gray level difference image which expresses the variation pattern of the regional gray levels. Secondly, the regional co-occurrence matrix is constructed by using these average binary gray level differences. Finally, we extract the second-order statistic parameters reflecting the image texture feature from the regional co-occurrence matrix. Theoretical analysis and experimental results show that the image texture feature extraction method has certain accuracy and validity
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Wu, Ling, Yuanjuan Gong, Xiaoping Bai, Wei Wang, and Zhuo Wang. "Nondestructive Determination of Leaf Nitrogen Content in Corn by Hyperspectral Imaging Using Spectral and Texture Fusion." Applied Sciences 13, no. 3 (February 1, 2023): 1910. http://dx.doi.org/10.3390/app13031910.
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The nitrogen content is an important indicator affecting corn plants’ growth status. Most of the standard hyperspectral imaging-based techniques for nondestructive detection of crop nitrogen content use a single feature as the input variable of the model, which reduces the generalization ability of the prediction model. To this end, a prediction model for the nitrogen content of corn leaves based on the fusion of image and spectral features is proposed. In this study, corn leaves at the modulation stage were studied, samples with different nitrogen levels were numbered, and their hyperspectral data in the wavelength range of 400~1100 nm were collected. The average spectrum of the models was used as valid spectral information. First-order derivatives, standard normal variables transformation (SNV), Savitzky-Golay (S-G) smoothing, and normalization were selected to preprocess the spectral features. The CARS-SPA algorithm was used to screen sensitive spectral variables. The gray level co-currency matrix (GLCM) was chosen to extract the texture image features of the test samples. Corn leaf spectral and texture image features were fused and modeled as target features. Partial least squares regression (PLSR) and support vector machine regression (SVR) were used to predict corn leaves’ nitrogen content. The results showed that the image and spectral-based fusion models improved the prediction performance to some extent compared to the univariate models. The PLSR model based on feature fusion predicted the best results, in which the RP2 and RMSEP were 0.987 and 0.047. This method provides a reliable theoretical basis and technical support for developing nondestructive and accurate detection of nitrogen content in corn leaves.
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Zhang, R., Y. He, X. Yi, G. Lu, and X. Guo. "POSE-FCN SUPERPIXEL SEGMENTATION FOR BUILDING FACADES BASED ON 2D TEXTURE AND 3D LOCAL POSE-VARIED SEMANTIC FEATURES." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-2-2022 (May 17, 2022): 203–10. http://dx.doi.org/10.5194/isprs-annals-v-2-2022-203-2022.
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Abstract. The extraction of building facades based on image sequences has a great contribution to the construction of digital realistic cities. The superpixel segmentation algorithm is a pre-processing tool for segmentation because of its advantages of fast speed, universality and great accuracy. However, the 2D features are less reliable because building facades usually have complex texture and geometric feature. It is difficult to obtain accurate detail information of the façades by clustering the superpixels. Moreover, the process of acquiring building image sequences is easily disturbed by environmental factors, which also leads to the poor results of the superpixel segmentation. In this paper, 3D local pose-varied semantic features of buildings are defined for this problem, which are computed by 3D point clouds generated from multi-view images of buildings based on SfM and PMVS. Then, multi-modal superpixels with integration of 2D texture and 3D pose-varied semantic features are computed by using fully convolutional networks. The new method is compared with traditional superpixel segmentation method by standard superpixel segmentation result evaluation metrics such as achievable segmentation accuracy , boundary recall, and undersegmentation error. The method achieve accurate segmentation results and effectively exclude the influence of complex texture and environmental factors. In summary, The multi-modal superpixels obtained by the integration have better reliability and provide a new idea for the superpixel segmentation of building facades, which has important theoretical and practical significance.
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Ramazankhani, Fahimeh, Mahdi Yazdian-Dehkord, and Mehdi Rezaeian. "Feature Fusion and NRML Metric Learning for Facial Kinship Verification." JUCS - Journal of Universal Computer Science 29, no. 4 (April 28, 2023): 326–48. http://dx.doi.org/10.3897/jucs.89254.
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Features extracted from facial images are used in various fields such as kinship verification. The kinship verification system determines the kin or non-kin relation between a pair of facial images by analysing their facial features. In this research, different texture and color features have been used along with the metric learning method, to verify the kinship for the four kinship relations of father-son, father-daughter, mother-son and mother-daughter. First, by fusing effective features, NRML metric learning used to generate the discriminative feature vector, then SVM classifier used to verify to kinship relations. To measure the accuracy of the proposed method, KinFaceW-I and KinFaceW-II databases have been used. The results of the evaluations show that the feature fusion and NRML metric learning methods have been able to improve the performance of the kinship verification system. In addition to the proposed approach, the effect of feature extraction from the image blocks or the whole image is investigated and the results are presented. The results indicate that feature extraction in block form, can be effective in improving the final accuracy of kinship verification.
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Ghosh, Soulib, S. K. Khalid Hassan, Ali Hussain Khan, Ankur Manna, Showmik Bhowmik, and Ram Sarkar. "Application of texture-based features for text non-text classification in printed document images with novel feature selection algorithm." Soft Computing 26, no. 2 (October 6, 2021): 891–909. http://dx.doi.org/10.1007/s00500-021-06260-9.
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Mustafa, Saleem, Arfan Jaffar, Muhammad Waseem Iqbal, Asma Abubakar, Abdullah S. Alshahrani, and Ahmed Alghamdi. "Hybrid Color Texture Features Classification Through ANN for Melanoma." Intelligent Automation & Soft Computing 35, no. 2 (2023): 2205–18. http://dx.doi.org/10.32604/iasc.2023.029549.
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Zhao, Wenju, Fangfang Ma, Haiying Yu, and Zhaozhao Li. "Inversion Model of Salt Content in Alfalfa-Covered Soil Based on a Combination of UAV Spectral and Texture Information." Agriculture 13, no. 8 (August 1, 2023): 1530. http://dx.doi.org/10.3390/agriculture13081530.
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This study aimed to investigate how the combination of texture information and spectral index affects the accuracy of the soil salinity inversion model. Taking the Bianwan Farm in Jiuquan City, Gansu Province, China as the research area, the multi-spectral data and soil salinity data at 0–15 cm, 15–30 cm and 30–50 cm depths in the sampling area under alfalfa coverage were collected, and spectral reflectance and texture features were obtained from a multispectral image. Moreover, the red-edge band was introduced to improve the spectral index, and gray correlation analysis was utilized to screen sensitive features. Five types of alfalfa-covered soil salinity machine learning inversion models based on random forest (RF) and extreme learning machine (ELM) algorithms were constructed, using the salinity index (SIs), vegetation index (VIs), salinity index + vegetation index (SIs + VIs), vegetation index + texture feature (VIs + TFs), and vegetation index + texture index (VIs + TIs). The determination coefficient R2, root-mean-square error (RMSE) and mean absolute error (MAE) were used to evaluate each model’s performance. The results show that the VIs model is more accurate than the SIs and SIs +VIs models. Combining texture information with VIs improves the inversion accuracy, and the VIs + TIs model has the best inversion effect. From the perspective of inversion depth, the inversion effect for 0–15 cm soil salinity was significantly better than that for other depths, and was the best inversion depth under alfalfa cover. The average R2 of the RF model was 10% higher than that of the ELM. The RF algorithm has high inversion accuracy and stability and performs better than ELM. These findings can serve as a theoretical basis for the efficient inversion of soil salinity and management of saline–alkali lands.
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Liu, Jinping, Jiezhou He, Zhaohui Tang, Pengfei Xu, Wuxia Zhang, and Weihua Gui. "Characterization of Complex Image Spatial Structures Based on Symmetrical Weibull Distribution Model for Texture Pattern Classification." Complexity 2018 (December 2, 2018): 1–23. http://dx.doi.org/10.1155/2018/6163787.
Повний текст джерелаАнотація:
Texture pattern classification has long been an essential issue in computer vision (CV). However, texture is a kind of perceptual concept of human beings in scene observation or content understanding, which cannot be defined or described clearly in CV. Visually, the visual appearance of the complex spatial structure (CSS) of texture pattern (TP) generally depends on the random organization (or layout) of local homogeneous fragments (LHFs) in the imaged surface. Hence, it is essential to investigate the latent statistical distribution (LSD) behavior of LHFs for distinctive CSS feature characterization to achieve good classification performance. This work presents an image statistical modeling-based TP identification (ISM-TPI) method. It firstly makes a theoretical explanation of the Weibull distribution (WD) behavior of the LHFs of the imaged surface in the imaging process based on the sequential fragmentation theory (SFT), which consequently derives a symmetrical WD model (SWDM) to characterize the LSD of the TP’s SS. Multidirectional and multiscale TP features are then characterized by the SWDM parameters based on the oriented differential operators; in other words, texture images are convolved with multiscale and multidirectional Gaussian derivative filters (GDFs), including the steerable isotropic GDFs (SIGDFs) and the oriented anisotropic GDFs (OAGDFs), for the omnidirectional and multiscale SS detail exhibition with low computational complexity. Finally, SWDM-based TP feature parameters, demonstrated to be directly related to the human vision perception system with significant physical perception meaning, are extracted and used to TP classification with a partial least squares-discriminant analysis- (PLS-DA-) based classifier. The effectiveness of the proposed ISM-TPI method is verified by extensive experiments on three texture image databases. The classification results demonstrate the superiority of the proposed methods over several state-of-the-art TP classification methods.
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Yang, Yu-Guang, Yong-Ce Zhang, Xiu-Bo Chen, Yi-Hua Zhou, and Wei-Min Shi. "Eliminating the texture features in visually meaningful cipher images." Information Sciences 429 (March 2018): 102–19. http://dx.doi.org/10.1016/j.ins.2017.11.009.
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Richards, Chloe, Asma Slaimi, Noel E. O’Connor, Alan Barrett, Sandra Kwiatkowska, and Fiona Regan. "Bio-inspired Surface Texture Modification as a Viable Feature of Future Aquatic Antifouling Strategies: A Review." International Journal of Molecular Sciences 21, no. 14 (July 17, 2020): 5063. http://dx.doi.org/10.3390/ijms21145063.
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The imitation of natural systems to produce effective antifouling materials is often referred to as “biomimetics”. The world of biomimetics is a multidisciplinary one, needing careful understanding of “biological structures”, processes and principles of various organisms found in nature and based on this, designing nanodevices and nanomaterials that are of commercial interest to industry. Looking to the marine environment for bioinspired surfaces offers researchers a wealth of topographies to explore. Particular attention has been given to the evaluation of textures based on marine organisms tested in either the laboratory or the field. The findings of the review relate to the numbers of studies on textured surfaces demonstrating antifouling potential which are significant. However, many of these are only tested in the laboratory, where it is acknowledged a very different response to fouling is observed.
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Martey, Ezekiel Mensah, Hang Lei, Xiaoyu Li, and Obed Appiah. "Image Representation Using Stacked Colour Histogram." Algorithms 14, no. 8 (July 30, 2021): 228. http://dx.doi.org/10.3390/a14080228.
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Image representation plays a vital role in the realisation of Content-Based Image Retrieval (CBIR) system. The representation is performed because pixel-by-pixel matching for image retrieval is impracticable as a result of the rigid nature of such an approach. In CBIR therefore, colour, shape and texture and other visual features are used to represent images for effective retrieval task. Among these visual features, the colour and texture are pretty remarkable in defining the content of the image. However, combining these features does not necessarily guarantee better retrieval accuracy due to image transformations such rotation, scaling, and translation that an image would have gone through. More so, concerns about feature vector representation taking ample memory space affect the running time of the retrieval task. To address these problems, we propose a new colour scheme called Stack Colour Histogram (SCH) which inherently extracts colour and neighbourhood information into a descriptor for indexing images. SCH performs recurrent mean filtering of the image to be indexed. The recurrent blurring in this proposed method works by repeatedly filtering (transforming) the image. The output of a transformation serves as the input for the next transformation, and in each case a histogram is generated. The histograms are summed up bin-by-bin and the resulted vector used to index the image. The image blurring process uses pixel’s neighbourhood information, making the proposed SCH exhibit the inherent textural information of the image that has been indexed. The SCH was extensively tested on the Coil100, Outext, Batik and Corel10K datasets. The Coil100, Outext, and Batik datasets are generally used to assess image texture descriptors, while Corel10K is used for heterogeneous descriptors. The experimental results show that our proposed descriptor significantly improves retrieval and classification rate when compared with (CMTH, MTH, TCM, CTM and NRFUCTM) which are the start-of-the-art descriptors for images with textural features.
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Alim, Affan, Imran Naseem, Roberto Togneri, and Mohammed Bennamoun. "The most discriminant subbands for face recognition: A novel information-theoretic framework." International Journal of Wavelets, Multiresolution and Information Processing 16, no. 05 (September 2018): 1850040. http://dx.doi.org/10.1142/s0219691318500406.
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In this paper, we propose a consolidated framework for the automatic selection of the most discriminant subbands for the problem of face recognition. Essentially, the face images are transformed into textures using the linear binary pattern (LBP) approach, these texturized-faces undergo the wavelet packet decomposition resulting in several subband images. We propose to use the energy features to effectively represent these subband images. The underlying statistical patterns of the data are harnessed in form of information-theoretic metrics to select the most discriminant subbands. The proposed algorithms are extensively evaluated on several standard databases and are shown to always pick the most significant subbands resulting in better performance. The proposed algorithms are entirely generic and do not depend on the selection of features or/and classifiers.
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Muntean, Delia Doris, Maria Bădărînză, Paul Andrei Ștefan, Manuela Lavinia Lenghel, Georgeta Mihaela Rusu, Csaba Csutak, Paul Alexandru Coroian, Roxana Adelina Lupean, and Daniela Fodor. "The Diagnostic Value of MRI-Based Radiomic Analysis of Lacrimal Glands in Patients with Sjögren’s Syndrome." International Journal of Molecular Sciences 23, no. 17 (September 2, 2022): 10051. http://dx.doi.org/10.3390/ijms231710051.
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This study aimed to assess the effectiveness of MRI-based texture features of the lacrimal glands (LG) in augmenting the imaging differentiation between primary Sjögren’s Syndrome (pSS) affected LG and healthy LG, as well as to emphasize the possible importance of radiomics in pSS early-imaging diagnosis. The MRI examinations of 23 patients diagnosed with pSS and 23 healthy controls were retrospectively included. Texture features of both LG were extracted from a coronal post-contrast T1-weighted sequence, using a dedicated software. The ability of texture features to discriminate between healthy and pSS lacrimal glands was performed through univariate, multivariate, and receiver operating characteristics analysis. Two quantitative textural analysis features, RunLengthNonUniformityNormalized (RLNonUN) and Maximum2DDiameterColumn (Max2DDC), were independent predictors of pSS-affected glands (p < 0.001). Their combined ability was able to identify pSS LG with 91.67% sensitivity and 83.33% specificity. MRI-based texture features have the potential to function as quantitative additional criteria that could increase the diagnostic accuracy of pSS-affected LG.
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Pereira Gonçalves, M. A., Carlos Escorihuela-Sayalero, Pablo Garca-Fernández, Javier Junquera, and Jorge Íñiguez. "Theoretical guidelines to create and tune electric skyrmion bubbles." Science Advances 5, no. 2 (February 2019): eaau7023. http://dx.doi.org/10.1126/sciadv.aau7023.
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Researchers have long wondered whether ferroelectrics may present topological textures akin to magnetic skyrmions and chiral bubbles, the results being modest thus far. An electric equivalent of a typical magnetic skyrmion would rely on a counterpart of the Dzyaloshinskii-Moriya interaction and seems all but impossible; further, the exotic ferroelectric orders reported to date rely on specific composites and superlattices, limiting their generality and properties. Here, we propose an original approach to write topological textures in simple ferroelectrics in a customary manner. Our second-principles simulations of columnar nanodomains, in prototype material PbTiO3, show we can harness the Bloch-type structure of the domain wall to create objects with the usual skyrmion-defining features as well as unusual ones—including isotopological and topological transitions driven by external fields and temperature—and potentially very small sizes. Our results suggest countless possibilities for creating and manipulating such electric textures, effectively inaugurating the field of topological ferroelectrics.
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Ju, Hye Min, Byung-Chul Kim, Ilhan Lim, Byung Hyun Byun, and Sang-Keun Woo. "Estimation of an Image Biomarker for Distant Recurrence Prediction in NSCLC Using Proliferation-Related Genes." International Journal of Molecular Sciences 24, no. 3 (February 1, 2023): 2794. http://dx.doi.org/10.3390/ijms24032794.
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This study aimed to identify a distant-recurrence image biomarker in NSCLC by investigating correlations between heterogeneity functional gene expression and fluorine-18-2-fluoro-2-deoxy-D-glucose positron emission tomography (18F-FDG PET) image features of NSCLC patients. RNA-sequencing data and 18F-FDG PET images of 53 patients with NSCLC (19 with distant recurrence and 34 without recurrence) from The Cancer Imaging Archive and The Cancer Genome Atlas Program databases were used in a combined analysis. Weighted correlation network analysis was performed to identify gene groups related to distant recurrence. Genes were selected for functions related to distant recurrence. In total, 47 image features were extracted from PET images as radiomics. The relationship between gene expression and image features was estimated using a hypergeometric distribution test with the Pearson correlation method. The distant recurrence prediction model was validated by a random forest (RF) algorithm using image texture features and related gene expression. In total, 37 gene modules were identified by gene-expression pattern with weighted gene co-expression network analysis. The gene modules with the highest significance were selected (p-value < 0.05). Nine genes with high protein–protein interaction and area under the curve (AUC) were identified as hub genes involved in the proliferation function, which plays an important role in distant recurrence of cancer. Four image features (GLRLM_SRHGE, GLRLM_HGRE, SUVmean, and GLZLM_GLNU) and six genes were identified to be correlated (p-value < 0.1). AUCs (accuracy: 0.59, AUC: 0.729) from the 47 image texture features and AUCs (accuracy: 0.767, AUC: 0.808) from hub genes were calculated using the RF algorithm. AUCs (accuracy: 0.783, AUC: 0.912) from the four image texture features and six correlated genes and AUCs (accuracy: 0.738, AUC: 0.779) from only the four image texture features were calculated using the RF algorithm. The four image texture features validated by heterogeneity group gene expression were found to be related to cancer heterogeneity. The identification of these image texture features demonstrated that advanced prediction of NSCLC distant recurrence is possible using the image biomarker.
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Peng, Yongfang, Shengwei Tian, Long Yu, Yalong Lv, and Ruijin Wang. "A Joint Approach to Detect Malicious URL Based on Attention Mechanism." International Journal of Computational Intelligence and Applications 18, no. 03 (September 2019): 1950021. http://dx.doi.org/10.1142/s1469026819500214.
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To improve the accuracy and automation of malware Uniform Resource Locator (URL) recognition, a joint approach of Convolutional neural network (CNN) and Long-short term memory (LSTM) based on the Attention mechanism (JCLA) is proposed to identify and detect malicious URL. Firstly, the URL features including texture information, lexical information and host information are extracted and filtered, and pre-processed with encode. Then, the feature matrix more relevant to the output are chose according to the weight of the attention mechanism and input to the constructed parallel processing model called CNN_LSTM, combinating CNN and LSTM to get local features. Next, the extracted local features are merged to calculate the global features of the URLs to be detected. Finally, the URLs are classified by the SoftMax classifier using global features, the accuracy of the model in malicious URL recgonition is 98.26%. The experimental results show that the JCLA model proposed in this paper is better than the traditional deep learning model or CNN_LSTM combined model for detecting malicious URLs.
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Wang, Xuejian, Michael C. Fairhurst, and Anne M. P. Canuto. "Improving multi-view facial expression recognition through two novel texture-based feature representations." Intelligent Data Analysis 24, no. 6 (December 18, 2020): 1455–76. http://dx.doi.org/10.3233/ida-194798.
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Although several automatic computer systems have been proposed to address facial expression recognition problems, the majority of them still fail to cope with some requirements of many practical application scenarios. In this paper, one of the most influential and common issues raised in practical application scenarios when applying automatic facial expression recognition system, head pose variation, is comprehensively explored and investigated. In order to do this, two novel texture feature representations are proposed for implementing multi-view facial expression recognition systems in practical environments. These representations combine the block-based techniques with Local Ternary Pattern-based features, providing a more informative and efficient feature representation of the facial images. In addition, an in-house multi-view facial expression database has been designed and collected to allow us to conduct a detailed research study of the effect of out-of-plane pose angles on the performance of a multi-view facial expression recognition system. Along with the proposed in-house dataset, the proposed system is tested on two well-known facial expression databases, CK+ and BU-3DFE datasets. The obtained results shows that the proposed system outperforms current state-of-the-art 2D facial expression systems in the presence of pose variations.
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Oriekhova, Larysa, Petro Andriichuk, Tetyana Shnurenko, Volodymyr Horobets, Valentina Sinelnikova, and Ivan Sinelnikov. "The Research of Computer Simulation of Textual Dimension in the Context of the Musical Discourse." Postmodern Openings 13, no. 3 (August 8, 2022): 310–22. http://dx.doi.org/10.18662/po/13.3/491.
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The relevance of the study is determined by the need to rethink musical art as a value orientation in the period of postmodernity. The purpose of the study is to determine the features of the textual dimension of musical discourse as a feature of the postmodern value perception of musical art in combination with the information and computerization of society. The purpose of the article is to show the basic context of the textual dimension of musical discourse. This research shows a holistic analysis of musical art in the context of discourse analysis and innovative modeling, which in the future is a prospect for creating new ideas for the interpretation of musical works. The study highlights the main components of textual analysis of a musical work, as well as the perception of musical discourse as a manifestation of today’s information of social development. For a comprehensive analysis of musical art it is necessary to understand the basic theoretical and methodological approaches to the study of this concept. Therefore, the principal theoretical approaches of scientists concerning musical discourse in the context of the textual dimension were studied, and theoretical problems in musical discourse analysis were developed. Beneficial to study the complex modeling of the textual dimension of musical art, the methods of analysis and synthesis, research method and discourse analysis method were used. This study provided a basis for determining the effectiveness of computer simulation technology in the context of the musical dimension.
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Wu, Jinlong, Decong Zheng, Zhiming Wu, Haiyan Song, and Xiaoxiang Zhang. "Prediction of Buckwheat Maturity in UAV-RGB Images Based on Recursive Feature Elimination Cross-Validation: A Case Study in Jinzhong, Northern China." Plants 11, no. 23 (November 27, 2022): 3257. http://dx.doi.org/10.3390/plants11233257.
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Buckwheat is an important minor grain crop with medicinal and edible functions. The accurate judgment of buckwheat maturity is beneficial to reduce harvest losses and improve yield. With the rapid development of unmanned aerial vehicle (UAV) technology, it has been widely used to predict the maturity of agricultural products. This paper proposed a method using recursive feature elimination cross-validation (RFECV) combined with multiple regression models to predict the maturity of buckwheat in UAV-RGB images. The images were captured in the buckwheat experimental field of Shanxi Agricultural University in Jinzhong, Northern China, from September to October in 2021. The variety was sweet buckwheat of “Jinqiao No. 1”. In order to deeply mine the feature vectors that highly correlated with the prediction of buckwheat maturity, 22 dimensional features with 5 vegetation indexes, 9 color features, and 8 texture features of buckwheat were selected initially. The RFECV method was adopted to obtain the optimal feature vector dimensions and combinations with six regression models of decision tree regression, linear regression, random forest regression, AdaBoost regression, gradient lifting regression, and extreme random tree regression. The coefficient of determination (R2) and root mean square error (RMSE) were used to analyze the different combinations of the six regression models with different feature spaces. The experimental results show that the single vegetation index performed poorly in the prediction of buckwheat maturity; the prediction result of feature space “5” combined with the gradient lifting regression model performed the best; and the R2 and RMSE were 0.981 and 1.70 respectively. The research results can provide an important theoretical basis for the prediction of the regional maturity of crops.
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Hansen, Martje, and Jens Hessmann. "Researching linguistic features of text genres in a DGS corpus." Sign Language and Linguistics 18, no. 1 (October 19, 2015): 1–40. http://dx.doi.org/10.1075/sll.18.1.01han.
Повний текст джерелаАнотація:
In this article, we investigate the referential and textual functions of finger loci, that is, the pointing towards one or more fingers of the weak hand in signed discourse. The first part of the article deals with the theoretical discussion about referentiality and the related notions of definiteness and specificity in the literature on spoken languages. The second empirical part of the article shows that the use of finger loci in German Sign Language (DGS) varies according to the genre of signed discourse, fulfilling not only referential but also textual functions. Furthermore, formal variations within the use of finger loci reflect referential gradation and specificity.
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D'Elia, Ciro, Simona Ruscino, Maurizio Abbate, Bruno Aiazzi, Stefano Baronti, and Luciano Alparone. "SAR Image Classification Through Information-Theoretic Textural Features, MRF Segmentation, and Object-Oriented Learning Vector Quantization." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7, no. 4 (April 2014): 1116–26. http://dx.doi.org/10.1109/jstars.2014.2304700.
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Rajabova, Mashhura. "Harmonic and Fret Features of "Tanovar" Processing by A. F. Kozlovsky and A. Nabiev." Eurasian music science journal, no. 1 (June 18, 2021): 95–105. http://dx.doi.org/10.52847/eamsj/vol_2021_issue_1/55.
Повний текст джерелаАнотація:
In the modern musicology of our country, the issues of the modal structure of national music in its folk and classical branches are the most relevant. And they require a detailed study of the historical and theoretical aspects of fret thinking both in traditional classical and folk Uzbek music, and in the works of composers of our country. The article deals with some theoretical and historical aspects in the use of the means of musical expression of two composers of Uzbekistan: A. F. Kozlovsky and A. Nabiev. A brief comparative analysis of the stylistic features of orchestral and piano processing, its fret and harmonic organization, the sound representation of Uzbek folk instruments by means of a symphony orchestra and vocals, as well as in the piano texture – in two historical periods of the development of the composer's creativity of Uzbekistan is given.
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HAYASHI, S., T. MINEMOTO, H. TAKAKURA, and Y. HAMAKAWA. "Influence of texture feature size on spherical silicon solar cells." Rare Metals 25, no. 6 (October 2006): 115–20. http://dx.doi.org/10.1016/s1001-0521(07)60056-4.
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Sheu, C. Y., F. Kurz, and P. Angelo. "AUTOMATIC 3D LANE MARKING RECONSTRUCTION USING MULTI-VIEW AERIAL IMAGERY." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-1 (September 26, 2018): 147–54. http://dx.doi.org/10.5194/isprs-annals-iv-1-147-2018.
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<p><strong>Abstract.</strong> The 3D information of road infrastructures are gaining importance with the development of autonomous driving. The exact absolute position and height of lane markings, for example, support lane-accurate localization. Several approaches have been proposed for the 3D reconstruction of line features from multi-view airborne optical imagery. However, standard appearance-based matching approaches for 3D reconstruction are hardly applicable on lane markings due to the similar color profile of all lane markings and the lack of textures in their neighboring areas. We present a workflow for 3D lane markings reconstruction without explicit feature matching process using multi-view aerial imagery. The aim is to optimize the best 3D line location by minimizing the distance from its back projection to the detected 2D line in all the covering images. Firstly, the lane markings are automatically extracted from aerial images using standard line detection algorithms. By projecting these extracted lines onto the Semi-Global Matching (SGM) generated Digital Surface Model (DSM), the approximate 3D line segments are generated. Starting from these approximations, the 3D lines are iteratively refined based on the detected 2D lines in the original images and the viewing geometry. The proposed approach relies on precise detection of 2D lines in image space, a pre-knowledge of the approximate 3D line segments, and it heavily relies on image orientations. Nevertheless, it avoids the problem of non-textured neighborhood and is not limited to lines of finite length. The theoretical precision of 3D reconstruction with the proposed framework is evaluated.</p>