Local binary patterns (LBP) are considered among the most computationally efficient high-performance texture features. However, the LBP method is very sensitive to image noise and is unable to ...capture macrostructure information. To best address these disadvantages, in this paper, we introduce a novel descriptor for texture classification, the median robust extended LBP (MRELBP). Different from the traditional LBP and many LBP variants, MRELBP compares regional image medians rather than raw image intensities. A multiscale LBP type descriptor is computed by efficiently comparing image medians over a novel sampling scheme, which can capture both microstructure and macrostructure texture information. A comprehensive evaluation on benchmark data sets reveals MRELBP's high performance-robust to gray scale variations, rotation changes and noise-but at a low computational cost. MRELBP produces the best classification scores of 99.82%, 99.38%, and 99.77% on three popular Outex test suites. More importantly, MRELBP is shown to be highly robust to image noise, including Gaussian noise, Gaussian blur, salt-and-pepper noise, and random pixel corruption.
Although deep neural network methods achieved much success in compressed sensing image reconstruction in recent years, they still have some issues, especially in preserving texture details. In this ...article, we propose a new dual-path attention network for compressed sensing image reconstruction, which is composed of a structure path, a texture path and a texture attention module. Motivated by the classical paradigm of image structure-texture decomposition, the structure path aims to reconstruct the dominant structure component of the original image, and the texture path targets at recovering the remaining texture details. To better bridge the information between two paths, the texture attention module is designed to deliver the useful structure information to the texture path and predict the texture region, thereby facilitating the recovery of texture details. Two paths are optimized with a unified loss function. In the testing phase, given the measurement vector of a new image, it can be well reconstructed by carrying out the well trained dual-path attention network and integrating the outputs of the structure path and the texture path. Experimental results on the SET5, SET11 and BSD68 testing datasets demonstrate that the proposed method achieves comparable or better results compared with some state-of-the-art deep learning based methods and conventional iterative optimization based methods in terms of reconstruction quality and robustness to noise.
The real world exhibits an abundance of non-stationary textures. Examples include textures with large scale structures, as well as spatially variant and inhomogeneous textures. While existing ...example-based texture synthesis methods can cope well with stationary textures, non-stationary textures still pose a considerable challenge, which remains unresolved. In this paper, we propose a new approach for example-based non-stationary texture synthesis. Our approach uses a generative adversarial network (GAN), trained to double the spatial extent of texture blocks extracted from a specific texture exemplar. Once trained, the fully convolutional generator is able to expand the size of the entire exemplar, as well as of any of its sub-blocks. We demonstrate that this conceptually simple approach is highly effective for capturing large scale structures, as well as other non-stationary attributes of the input exemplar. As a result, it can cope with challenging textures, which, to our knowledge, no other existing method can handle.
Many applications in image analysis require the accurate classification of complex patterns including both color and texture, e.g., in content image retrieval, biometrics, and the inspection of ...fabrics, wood, steel, ceramics, and fruits, among others. A new method for pattern classification using both color and texture information is proposed in this paper. The proposed method includes the following steps: division of each image into global and local samples, texture and color feature extraction from samples using a Haralick statistics and binary quaternion-moment-preserving method, a classification stage using support vector machine, and a final stage of post-processing employing a bagging ensemble. One of the main contributions of this method is the image partition, allowing image representation into global and local features. This partition captures most of the information present in the image for colored texture classification allowing improved results. The proposed method was tested on four databases extensively used in color–texture classification: the Brodatz, VisTex, Outex, and KTH-TIPS2b databases, yielding correct classification rates of 97.63%, 97.13%, 90.78%, and 92.90%, respectively. The use of the post-processing stage improved those results to 99.88%, 100%, 98.97%, and 95.75%, respectively. We compared our results to the best previously published results on the same databases finding significant improvements in all cases.
Texture plays an important role in cartoon illustrations to display object materials and enrich visual experiences. Unfortunately, manually designing and drawing an appropriate texture is not easy ...even for proficient artists, let alone novice or amateur people. While there exist tons of textures on the Internet, it is not easy to pick an appropriate one using traditional text-based search engines. Although several texture pickers have been proposed, they still require the users to browse the textures by themselves, which is still labor-intensive and time-consuming. In this article, an automatic texture recommendation system is proposed for recommending multiple textures to replace a set of user-specified regions in a cartoon illustration with visually pleasant look. Two measurements, the suitability measurement and the style-consistency measurement, are proposed to make sure that the recommended textures are suitable for cartoon illustration and at the same time mutually consistent in style. The suitability is measured based on the synthesizability, cartoonity, and region fitness of textures. The style-consistency is predicted using a learning-based solution since it is subjective to judge whether two textures are consistent in style. An optimization problem is formulated and solved via the genetic algorithm. Our method is validated on various cartoon illustrations, and convincing results are obtained.
•An UTGO Steel with superior magnetic properties was prepared with glassless GO Steel.•Elaborated nucleation characteristics of {0kl} 〈1 0 0〉 oriented recrystallized grains.•Systematically discussed ...{0kl} 〈1 0 0〉 orientation evolution and influencing factors.
In this paper, an ultra-thin grain-oriented silicon steel (UTGO steel) strip was prepared via rolling a commercial glassless grain-oriented silicon steel plate to 0.075 mm and then annealing it at 850 °C for less than 30 min in a protective atmosphere. The resultant UTGO steel strip has magnetic induction B800 higher than 1.80 T and iron loss P1.5/400 lower than 12.0 W/kg. These superior magnetic properties are due to predominant {0kl} 〈1 0 0〉 texture and appropriate microstructure. The study shows that preferential {0kl} 〈1 0 0〉 nucleation occurs either inside shear bands or at in-grain deformation-induced boundaries. The nucleation behavior varies at different nucleation sites. Nuclei inside shear bands are denser and show preferential transitional orientation along {0kl} 〈1 0 0〉, while new grains dispersed at in-grain split boundaries have shown orientations which are suggested to inherit corresponding initial grain orientation prior to rolling. Nucleation behavior is also highly dependent on the orientation of the roll-deformed matrix. Our study finds that shear banding nucleation of {0kl} 〈1 0 0〉 (including Goss and {0 2 1} 〈1 0 0〉) is reduced when the orientation of the deformed matrix deviates from {1 1 1} 〈1 1 2〉. With the extension of annealing time, the growth of {0kl} 〈1 0 0〉 nuclei in cluster is restricted because of orientation pinning effect, whereas the growth of grains with orientations other than {0kl} 〈1 0 0〉 is promoted. This phenomenon weakens the dominance of {0kl} 〈1 0 0〉 texture, causing deterioration in magnetic properties of the final silicon steel.
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Visual textures have played a key role in image understanding because they convey important semantics of images, and because texture representations that pool local image descriptors in an orderless ...manner have had a tremendous impact in diverse applications. In this paper we make several contributions to texture understanding. First, instead of focusing on texture instance and material category recognition, we propose a human-interpretable vocabulary of texture attributes to describe common texture patterns, complemented by a new
describable texture dataset
for benchmarking. Second, we look at the problem of recognizing materials and texture attributes in realistic imaging conditions, including when textures appear in clutter, developing corresponding benchmarks on top of the recently proposed OpenSurfaces dataset. Third, we revisit classic texture represenations, including bag-of-visual-words and the Fisher vectors, in the context of deep learning and show that these have excellent efficiency and generalization properties if the convolutional layers of a deep model are used as filter banks. We obtain in this manner state-of-the-art performance in numerous datasets well beyond textures, an efficient method to apply deep features to image regions, as well as benefit in transferring features from one domain to another.
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We propose T13 = Z13⋊Z3 as the underlying non-Abelian discrete family symmetry of the asymmetric texture presented in M. H. Rahat, P. Ramond, and B. Xu, Phys. Rev. D 98, 055030 (2018).. Its mod 13 ...arithmetic distinguishes each Yukawa matrix element of the texture. We construct a model of effective interactions that singles out the asymmetry and equates, without fine-tuning, the products of down-quark and charged-lepton masses at a GUT-like scale.
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Understanding the influence of surface properties (roughness, grooves, discrete textures/dimples) on the performance of hydrodynamically lubricated contacts has been the aim of numerous studies. A ...variety of different numerical models have been employed by many researchers in order to find optimal texturing parameters (shape, size, distribution) for best performance enhancement in terms of load carrying capacity, film thickness, friction and wear. However, the large number of different modeling techniques and complexity in the patterns make finding the optimum texture a challenging task and have led to contrary conclusions. This article outlines the research effort on surface texturing worldwide, reviews the key findings and, in particular, provides a comparative summary of different modeling techniques for fluid flow, cavitation and micro-hydrodynamic effects.
•This article facilitates the application of textures for hydrodynamic contacts.•Functions of surface textures, texture design and modeling techniques are discussed.•Textures must be designed for a given application and range of operating conditions.•Robust models allow the evaluation of texture designs prior to being manufactured.•The three major challenges in numerical modeling are discussed.
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Nonlocal texture similarity and local intensity smoothness are both essential for solving most image inpainting problems. In this paper, we propose a novel image inpainting algorithm that is capable ...of reproducing the underlying textural details using a nonlocal texture measure and also smoothing pixel intensity seamlessly in order to achieve natural-looking inpainted images. For matching texture, we propose a Gaussian-weighted nonlocal texture similarity measure to obtain multiple candidate patches for each target patch. To compute the pixel intensity, we apply the α-trimmed mean filter to the candidate patches to inpaint the target patch pixel-by-pixel. The proposed algorithm is compared with four current image inpainting algorithms under different scenarios, including object removal, texture synthesis, and error concealment. Experimental results show that the proposed algorithm outperforms the existing algorithms when inpainting large missing regions in images with texture and geometric structures.
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