<title>Texture synthesis based on transition probabilities and localized correlation</title>

Empirical and analytical methods of selecting a characteristic structure of pairwise pixel interactions in Gibbs random field texture models are compared. Simple thresholding of partial interaction energies recovers a basic structure for modelling spatially homogeneous stochastic textures. Computationally intensive empirical sequential learning reduces the size of a basic structure and complements it by a fine structure describing characteristic minor details of regular textures. The empirical selection implicitly assumes that characteristic structures should include only statistically independent interactions. It can be approximated using analytical estimates of energies. Experiments show that a combined analytical-empirical sequential learning finds reduced basic and fine interaction structures much faster than the purely empirical one, but the sequential selection based on relative interaction energies may deteriorate basic structures of stochastic textures with strongly interdependent characteristic interactions.

Traditional fractal pattern design has some disadvantages such as inability to effectively reflect the characteristics of real scenery and texture. We propose a novel pattern design technique combining fractal geometry and image texture synthesis to solve these problems. We have improved Wei and Levoy (2000)’s texture synthesis algorithm by first using two-dimensional autocorrelation function to analyze the structure and distribution of textures, and then determining the size of L neighborhood. Several special fractal sets were adopted and HSL (Hue, Saturation, and Light) color space was chosen. The fractal structure was used to manipulate the texture synthesis in HSL color space where the pattern’s color can be adjusted conveniently. Experiments showed that patterns with different styles and different color characteristics can be more efficiently generated using the new technique.

Analytical approaches, based on digitised 2D texture models, for an automatic solid (3D) texture synthesis have been recently introduced to Computer Graphics. However, these approaches cannot provide satisfactory solutions in the usual case of natural anisotropic textures (wood grain for example). Indeed, solid texture synthesis requires particular care, and sometimes external knowledge to "guess" the internal structure of solid textures because only 2D texture models are used for analysis. By making some basic assumptions about the internal structure of solid textures, we propose a very efficient method based on a hybrid analysis (spectral and histogram) for an automatic synthesis of solid textures. This new method allows us to obtain high precision solid textures (closely resembling initial models) in a large number of cases, including the difficult case of anisotropic textures.

The main objective of this paper was to estimate and analyse transition-probability matrices for all 16 of Poland’s NUTS-2 level regions (voivodeship level). The analysis is conducted in terms of the transitions among six expenditure classes (per capita and per equivalent unit), focusing on poverty classes. The period of analysis was two years: 2015 and 2016. The basic aim was to identify both those regions in which the probability of staying in poverty was the highest and the general level of mobility among expenditure classes. The study uses a two-year panel sub-sample of unidentified unit data from the Central Statistical Office (CSO), specifically the data concerning household budget surveys. To account for differences in household size and demographic structure, the study used expenditures per capita and expenditures per equivalent unit simultaneously. To estimate the elements of the transition matrices, a classic maximum-likelihood estimator was used. The analysis used Shorrocks’ and Bartholomew’s mobility indices to assess the general mobility level and the Gini index to assess the inequality level. The results show that the one-year probability of staying in the same poverty class varies among regions and is lower for expenditures per equivalent units. The highest probabilities were identified in Podkarpackie (expenditures per capita) and Opolskie (expenditures per equivalent unit), and the lowest probabilities in Kujawsko-Pomorskie (expenditures per capita) and Małopolskie (expenditures per equivalent unit). The highest level of general mobility was noted in Małopolskie, for both categories of expenditures.

We propose a new method to enhance the cloth stain, such as the weak coffee stain in image. Our method is composed of two steps. At the first step, we obtain the principal component images by making the RGB components of each pixel to be uncorrelated. At this step, the texture structure is still left in the uncorrelated images, so it is difficult to recognize the stain. Then, at the second step, we reduce the texture structure by using Principal Component Analysis. We divide the uncorrelated images into the blocks, and those blocks are approximated by several principal eigen vectors which represent the texture structure. The approximation image represents the average texture structure without the cloth stain. So, the cloth stain is recognized without endeavor in the residual image which is obtained by subtracting the approximation image from the principal component image. As our method is robust for the number of observation data, we need only one color image whose size is not so big and whose resolution is not so high. The results of our experiment under different conditions show that the proposed method is effective for the image enhancement of the small weak cloth stain.

RPConvformer: A novel Transformer-based deep neural networks for traffic flow prediction

PurposeA method for predicting the material consumption of a sweater is presented before it is knitted. It can be achieved with the five basic models combined with the parameters related to the dimensions of the knitting machine and needles. The paper aims to discuss these issues.Design/methodology/approachBased on the parameters of the needle bar flat knitting machine, the sweater is modeled with five basic structures. The mathematical expression of each basic structure can be derived with corresponding parameters under some consumptions. In following, the predictive weight of the sweater can be formulated with the expression of the length of the basic structures and the linear density of the yarn.FindingsTo evaluate the performance of the proposed scheme, experiments of three types of sweaters on four different knitting machines are carried out. The results show that the proposed method can achieve the performance with the bias values by percentage ranging from −1.54 to −2.84 percent.Research limitations/implicationsDue to the present limited research, more experiments could not be carried out. To improve the performance and robustness of the proposed method, statistical performance measures such as the statistical mean and variance in massive experiments will be studied in the further research.Practical implicationsThe evaluation of the material consumption can be obtained before it is knitted with the known basic parameters related to the machine and yarn.Originality/valueThis paper derives the general expressions of five basic structures based on the corresponding parameters of knitting machine. The predictive weight of the sweater is expressed according to the above basic structures before the sweater is knitted.

Effect of blockage on critical ventilation velocity in longitudinally ventilated tunnel fires

In situ fabrication of CdMoO4/g-C3N4 composites with improved charge separation and photocatalytic activity under visible light irradiation

In this work, we present a novel algorithm to stylize the text without supervision, which provides a flexible and convenient way to invoke fantastic text expressions. Rather than employing the fixed pair of target text and source style images, our unsupervised framework establishes an implicit mapping for them by using an abstract imagery of the style image as bridges. Based on the mapping, we progressively narrow the visual discrepancy between text and style images by the proposed legibility-preserving structure transfer and texture transfer algorithms, which effectively balance the text legibility and style consistency. Furthermore, we explore a seamless composition of the stylized text and a background image, in which the optimal text layout is determined by a context-aware layout design algorithm utilizing cues for both seamlessness and aesthetics. Given the layout, the text can be seamlessly embedded into the background by texture synthesis under a context-aware boundary constraint. Experimental results demonstrate the effectiveness of the proposed method in automatic artistic typography creation and visual-textual presentation synthesis.

We present a wavelet‐based approach for selecting patches in patch‐based texture synthesis. We randomly select the first block that satisfies a minimum error criterion, computed from the wavelet coefficients (using 1D or 2D wavelets) for the overlapping region. We show that our wavelet‐based approach improves texture synthesis for samples where previous work fails, mainly textures with prominent aligned features. Also, it generates similar quality textures when compared against texture synthesis using feature maps with the advantage that our proposed method uses implicit edge information (since it is embedded in the wavelet coefficients) whereas feature maps rely explicitly on edge features. In previous work, the best patches are selected among all possible using a L2 norm on the RGB or grayscale pixel values of boundary zones. The L2 metric provides the raw pixel‐to‐pixel difference, disregarding relevant image structures — such as edges — that are relevant in the human visual system and therefore on synthesis of new textures.

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 -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.

This article focuses on productivity transitions from low to high levels of efficiency over time in the framework of the Farrell efficiency model. This transition probability approach is applied in two empirical applications in order to test the persistence of Farrell efficiency over time. The first application involves the efficiency comparisons in the public sector, while the second in the private sector. Both applications appear to exhibit a high degree of efficiency persistence over time.

One significant problem in texture synthesis based on ω-tile is that it is not suitable for the synthesis of structural texture. In this paper, we propose a robust approach that can generate an ω-tile set of high-quality regular texture. First, all possible sets of 4, 8 and 16 ω-tiles that meet certain criteria are obtained, and then a fast search algorithm is raised to choose the optimal cutting curve. Secondly, spectrum is used here to discriminate the pattern of different texture. After that, we propose the ω-tile based on texture element to improve the quality of the structural texture synthesis. In contrast to previous approaches, this operation insures efficient and realtime texture synthesis of structural texture. The outcomes of experiments verify the high quality and efficiency of the proposed algorithm.

In this paper, we propose a method for single image super-resolution that integrates reconstruction-based and example-based super-resolution. In image enlargement, jagged and blurred artifacts are often generated, particularly along the edges. Under large magnification, the region containing many edges of fine texture becomes blurred, and the reconstructed image looks unnatural. The proposed method consists of (i) a reconstruction process with local regularization and non-local regularization and (ii) a detail estimation process based on texture synthesis. Local regularization suppresses artifacts by estimating a pixel of interest in a local area. Non-local regularization reduces the noise by estimating a pixel of interest as a weighted average of similar pixels in a non-local area. Texture synthesis recovers fine texture by incorporating the optimal patch selected from a dictionary. For example-based super-resolution, patch size based on an importance map is used. These processing steps are performed to preserve the original edge patterns. In addition, back projection is applied to obtain the optimal image. Finally, we show some simulation results with less jagged and blurred artifacts compared to those of conventional methods.