Anisotropic Solid Texture Synthesis Using Orthogonal 2D Views

We propose a novel parametric approach which aims at the synthesis of anisotropic solid textures from the analysis of a single 2D exemplar. This approach is an extension of the pyramidal scheme of Portilla and Simoncelli. It proceeds in three main steps: first, a 2D analysis of the example is performed which produces a set of reference statistics. Then, 3D reference statistics are inferred from the 2D ones thanks to specific anisotropy assumptions. The final step aims at the synthesis itself: the 3D target statistics are imposed on a random 3D block according to a specific multi resolution pyramidal scheme. The approach is applied to the synthesis of solid textures representative of the structure of dense pre-graphitic carbons. The samples are lattice fringe images obtained by high resolution transmission electronic microscopy (HRTEM). HRTEM samples with increasing structural order are used for the experimental evaluation. The produced solid textures exhibit anisotropy properties similar to those observed in the HRTEM samples. Such an approach can easily be extended to any 3D anisotropic structures showing stacks of layers such as wood grain images, seismic data, etc.

In this paper, we present an improved synthesis algorithm for both 2D and solid textures. Based on the wavelet-based multi-resolution pyramids and the improved optimization of texture synthesis, a coarse texture was synthesized as a skeleton. By adding detail information to the coarse texture, the higher resolution results can be generated. This skeletal growth-like procedure makes the result more controllable. In addition, to make full use of the spatial relationships among texture pixels, wavelet coefficients rather than traditional RGB channels are used to search the nearest neighbor pixels. Experimental study shows that the method plays well for both 2D and solid texture synthesis.

Compared with 2D texture method, solid texturing can achieve better realistic results. A novel solid texture synthesis method based on 2D exemplar and procedural noise is presented. First, the movement path for exemplar in 3D space is designed using procedural noise. Next, all the pixels of exemplar are used to color the 3D space through their trajectory so that the solid texture is synthesized. Experimental results show that our method is able to efficiently synthesize 3D texture to simulate the natural properties of the given 2D exemplar. The method is simple and stable, and takes on a few seconds on an ordinary PC.

3D solid textures are important data for computer graphics applications because they are useful for synthesizing photo realistic patterns such as stone, smoke, water, clouds, fire, and wood. Unlike typical 2D textures, 3D solid textures are represented in voxel forms, which use three-dimensional coordinates. Various kinds of research have been conducted for pattern analysis of 2D textures to help development of computer applications. However, pattern analysis of 3D solid textures has not been investigated sufficiently even though 3D data related software is emerging. In this research, we have extended higher order local autocorrelation (HLAC) 2D mask patterns to 3D mask patterns so that pattern analysis software can handle not only 2D texture images but also 3D solid textures. Our experimental system can classify and look for 3D solid textures with similar patterns from a 3D solid texture database by using 3D HLAC masks. We have examined (1) similarity retrieval of 3D solid textures and (2) pattern classification of 3D solid textures by using the system.

We describe the use of traditional stereological methods to synthesize 3D solid textures from 2D images of existing materials. We first illustrate our approach for aggregate materials of spherical particles, and then extend the technique to apply to particles of arbitrary shapes. We demonstrate the effectiveness of the approach with side-by-side comparisons of a real material and a synthetic model with its appearance parameters derived from its physical counterpart. Unlike ad hoc methods for texture synthesis, stereology provides a disciplined, systematic basis for predicting material structure with well-defined assumptions.

Synthesizing solid particle textures via a visual hull algorithm

Many image acquisition techniques used in biomedical imaging, material analysis, and structural geology are capable of acquiring 3-D solid images. Computational analysis of these images is complex but necessary since it is difficult for humans to visualize and quantify their detailed 3-D content. One of the most common methods to analyze 3-D data is to characterize the volumetric texture patterns. Texture analysis generally consists of encoding the local organization of image scales and directions, which can be extremely diverse in 3-D. Current state-of-the- art techniques face many challenges when working with 3-D solid texture, where most approaches are not able to consistently characterize both scale and directional information. 3-D Riesz- wavelets can deal with both properties. One key property of Riesz filterbanks is steerability, which can be used to locally align the filters and compare textures with arbitrary (local) orientations. This paper proposes and compares three novel local alignment criteria for higher-order 3-D Riesz-wavelet transforms. The estimations of local texture orientations are based on higher- order extensions of regularized structure tensors. An experimental evaluation of the proposed methods for the classification of synthetic 3-D solid textures with alterations (such as rotations and noise) demonstrated the importance of local directional information for robust and accurate solid texture recognition. These alignment methods improved the accuracy of the unaligned Riesz descriptors up to 0.63, from 0.32 to 0.95 over 1 in the rotated data, which is better than all other techniques that are published and tested on the same database.

We present the Mokume dataset for solid wood texturing consisting of 190 cube-shaped samples of various hard and softwood species documented by high-resolution exterior photographs, annual ring annotations, and volumetric computed tomography (CT) scans. A subset of samples further includes photographs along slanted cuts through the cube for validation purposes. Using this dataset, we propose a three-stage inverse modeling pipeline to infer solid wood textures using only exterior photographs. Our method begins by evaluating a neural model to localize year rings on the cube face photographs. We then extend these exterior 2D observations into a globally consistent 3D representation by optimizing a procedural growth field using a novel iso-contour loss. Finally, we synthesize a detailed volumetric color texture from the growth field. For this last step, we propose two methods with different efficiency and quality characteristics: a fast inverse procedural texture method, and a neural cellular automaton (NCA). We demonstrate the synergy between the Mokume dataset and the proposed algorithms through comprehensive comparisons with unseen captured data. We also present experiments demonstrating the efficiency of our pipeline's components against ablations and baselines. Our code, the dataset, and reconstructions are available via https://mokumeproject.github.io/.

Geosphere, June 2010, v. 6, p. 237-274, doi:10.1130/GES00513.1, Plate 3 - Maps showing discretized results from three-dimensional (3D) stratigraphy model, solid volume 3D texture class model, and attribute strat_text (see text) for layers 11–16. Thin horizontal and vertical lines portray the grid cell discretization, which consists of 168 rows and 157 columns of square cells 660 ft (~198 m) on a side. The locations of drill holes that are deep enough to penetrate the upper surface of each layer, and thus serve as a point of geologic information for that layer, are shown on the stratigraphy maps. File size is 105 MB.

This paper introduces an approach for synthesizing natural textures, with emphasis on quasi-periodic and structural textures. The process consists of two stages. In the first stage, the basic textural elements (texels) and the basic textural structure are determined. This is achieved by identifying two fundamental frequencies in the texture, for two different orientations. The basic structure is a non-regular mesh that defines the place holders for texels. We call such place holders e-texels (empty texels). In the second stage, a new textural structure is designed from the original one, and its e-texels are filled in by texels obtained from the original patch. Same texture texels are expected to possess a high degree of similarity, thus the new structure could be filled in at random. However, a transition probability approach is used in order to retain local textural characteristics. More specifically, assuming that texel A is the last texel placed in the new structure, the e-texel closest to A is found. The e-texel is replaced by texel B from the old structure if the relative position between A and the e-texel is similar to the relative position between A and B in the old structure. This technique is an extension of a general texture synthesis technique previously developed by the author. The proposed technique is suited for structural textures since blockage effects are eliminated by allowing irregular shape texels to be merged, contrary to the previous general technique where the blocks merged are squares. Results show that the proposed method is successful in synthesizing structural textures.

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.

Salient structural elements are ubiquitous in natural textures, and their distribution exhibits some stochastic distribution features. Current texture synthesis algorithms can neither preserve the integrity of the elements nor capture this distributive information. We present an algorithm to treat this high-level visual information. Here, we address the issue by taking specific care of the structural elements. Our texture synthesis process grows the target texture one structural element at a time. A Markov random field model is assumed for the distribution of the salient structural elements. In the analysis process, the salient structural elements are first extracted, and their topological relationship is constructed. Next, in the synthesis process, to identify an unknown element with neighbors synthesized, we query the sample texture and find the most similar neighbor. The randomness of the target texture can be improved by randomly transforming the structural elements. Experimentation shows that our results pass scrutiny by the high-level visual processing of humans.

Research into texture synthesis is normally concerned with generation of 2D images of texture. However, real-world surface textures comprise rough surface geometry and various reflectance properties. These surface textures are different from 2D still texture as their images can therefore vary dramatically with illumination directions. This paper presents a simple framework for 3D surface texture synthesis. Firstly, we propose a novel 2D texture synthesis algorithm based on wavelet transform that can be efficiently extended to synthesise surface representations in multi-dimensional space. Then, photometric stereo (PS) is used to generate surface gradient and albedo maps from three synthesized surface texture images. The surface gradient maps can be further integrated to produce a surface height map (surface profile). With the albedo and height or gradient maps, new images of a Lambertian surface under arbitrary illuminant directions can be generated. Experiments show that the proposed approach can not only produce 3D surface textures under arbitrary illumination directions, but also has the ability to retain the surface geometry structure.

In thiswork, a newalgorithmis proposed for fast estimation of nonparametricmultivariate kernel density, based on principal direction divisive partitioning (PDDP) of the data space.The goal of the proposed algorithm is to use the finite support property of kernels for fast estimation of density. Compared to earlier approaches, this work explains the need of using boundaries (for partitioning the space) instead of centroids (used in earlier approaches), for better unsupervised nature (less user incorporation), and lesser (or atleast same) computational complexity. In earlier approaches, the finite support of a fixed kernel varies within the space due to the use of cluster centroids. It has been argued that if one uses boundaries (for partitioning) rather than centroids, the finite support of a fixed kernel does not change for a constant precision error. This fact introduces better unsupervision within the estimation framework. Themain contributionof thiswork is the insight gained in the kernel density estimation with the incorporation of clustering algortihm and its application in texture synthesis. Texture synthesis through nonparametric, noncausal, Markov random field (MRF), has been implemented earlier through estimation of and sampling from nonparametric conditional density. The incorporation of the proposed kernel density estimation algorithm within the earlier texture synthesis algorithm reduces the computational complexity with perceptually same results. These results provide the efficacy of the proposed algorithm within the context of natural texture synthesis.

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.