An ever-growing number of real-world computer vision applications require classification, segmentation, retrieval, or realistic rendering of genuine materials. However, the appearance of real ...materials dramatically changes with illumination and viewing variations. Thus, the only reliable representation of material visual properties requires capturing of its reflectance in as wide range of light and camera position combinations as possible. This is a principle of the recent most advanced texture representation, the bidirectional texture function (BTF). Multispectral BTF is a seven-dimensional function that depends on view and illumination directions as well as on planar texture coordinates. BTF is typically obtained by measurement of thousands of images covering many combinations of illumination and viewing angles. However, the large size of such measurements has prohibited their practical exploitation in any sensible application until recently. During the last few years, the first BTF measurement, compression, modeling, and rendering methods have emerged. In this paper, we categorize, critically survey, and psychophysically compare such approaches, which were published in this newly arising and important computer vision and graphics area.
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.
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.
From a physicochemical perspective, foods like vegan cheese and meat analogues are complex multicomponent gels. The aim of this study was to investigate the effect of processing conditions and ...composition on the textural properties of multicomponent gels containing starch, pea protein isolate (PPI) and emulsion droplets. Mechanical properties were measured, and structural analysis was carried with CLSM and SEM. In the case of particle gels prepared with maize starch (MS), a higher shearing speed decreased Young's modulus, fracture stress and fracture strain due to break up of the starch granules. In polymer gels prepared with potato starch (PS), structure and mechanical properties were not much affected by processing conditions. The addition of emulsion droplets increased the Young's modulus of MS gels and decreased that of PS gels. In PS gels, the fracture stress decreased further for smaller oil droplets. The addition of emulsion droplets was also found to decrease adhesiveness, cohesiveness and chewiness, regardless of the matrix structure. With protein addition into PS gels, an increase in Young's modulus and a decrease in fracture strain were observed. These results show that processing conditions and composition can be used to modulate the physical properties of complex food systems.
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•Protein and oil addition alters the structure and texture of starch-based gels.•Stronger shearing weakens starch particle gels, with slight impact on polymer gels.•Higher shearing speed and matrix viscosity lower the emulsion droplet size.•Additional protein increases emulsion-filled starch gel stiffness and lowers fracture strain.•Emulsion droplets reduce starch-protein gel adhesiveness and chewiness in any matrix.
Texture synthesis is a fundamental problem in computer graphics that would benefit various applications. Existing methods are effective in handling 2D image textures. In contrast, many real-world ...textures contain meso-structure in the 3D geometry space, such as grass, leaves, and fabrics, which cannot be effectively modeled using only 2D image textures. We propose a novel texture synthesis method with Neural Radiance Fields (NeRF) to capture and synthesize textures from given multi-view images. In the proposed NeRF texture representation, a scene with fine geometric details is disentangled into the meso-structure textures and the underlying base shape. This allows textures with meso-structure to be effectively learned as latent features situated on the base shape, which are fed into a NeRF decoder trained simultaneously to represent the rich view-dependent appearance. Using this implicit representation, we can synthesize NeRF-based textures through patch matching of latent features. However, inconsistencies between the metrics of the reconstructed content space and the latent feature space may compromise the synthesis quality. To enhance matching performance, we further regularize the distribution of latent features by incorporating a clustering constraint. In addition to generating NeRF textures over a planar domain, our method can also synthesize NeRF textures over curved surfaces, which are practically useful. Experimental results and evaluations demonstrate the effectiveness of our approach.
•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.
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.
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.
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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