Deformation-based interactive texture design using energy optimization

In this paper, we present a novel interactive texture design scheme based on deformation and energy optimization. Given a small sample texture, the design process starts with applying a set of deformation operations to the sample texture to obtain a set of deformed textures. Then local changes to those deformed textures are further made by replacing their local regions with the texture elements interactively selected from other textures. Such a deform–select–replace process is iterated many times until the desired deformed textures are obtained. Finally the deformed textures are composed to form a large texture with graph-cut optimization. By combining the graph-cut algorithm with an energy optimization process, interactive selections of local texture elements are done simply through indicating the positions of texture elements very roughly with a brush tool. Our experimental results demonstrate that the proposed technique can be used for designing a large variety of versatile textures from a single small sample texture, increasing or decreasing the density of texture elements, as well as for synthesizing textures from multiple sources.     

Completion-based texture design using deformation

In this paper, we present a novel approach for designing a variety of large textures from a single small sample texture. Firstly, the original small texture is segmented into layers, each of which contains one particular texture element. Secondly, each layer is deformed using a set of chaotic-based transformation operations. Thirdly, all the deformed layers are added together to form a new texture, which is a natural variation of the original sample texture. Since each layer is deformed independently, adding the deformed layers together usually results in a texture with overlapping regions and holes. We employ the graphcut algorithm and an example-based image inpainting technique to seamlessly patch the overlapping regions and to fill the holes. Moreover, an optimized graphcut synthesis algorithm and a new cyclic texture synthesis technique are also developed for efficiently creating large seamless textures. As a result, our approach shows particular strength in generating a large variety of textures from a single sample texture while avoiding highly repetitive patterns. Our experiments demonstrate that the proposed technique can also be used for other texture synthesis applications, such as texture synthesis from multiple samples.     

Texture Repairing by Unified Low Rank Optimization

In this paper, we show how to harness both low-rank and sparse structures in regular or near-regular textures for image completion. Our method is based on a unified formulation for both random and contiguous corruption. In addition to the low rank property of texture, the algorithm also uses the sparse assumption of the natural image: because the natural image is piecewise smooth, it is sparse in certain transformed domain (such as Fourier or wavelet transform). We combine low-rank and sparsity properties of the texture image together in the proposed algorithm. Our algorithm based on convex optimization can automatically and correctly repair the global structure of a corrupted texture, even without precise information about the regions to be completed. This algorithm integrates texture rectification and repairing into one optimization problem. Through extensive simulations, we show our method can complete and repair textures corrupted by errors with both random and contiguous supports better than existing low-rank matrix recovery methods. Our method demonstrates significant advantage over local patch based texture synthesis techniques in dealing with large corruption, non-uniform texture, and large perspective deformation.     

An Optimal Control Approach for Texture Metamorphosis

In this paper, we introduce a new texture metamorphosis approach for interpolating texture samples from a source texture into a target texture. We use a new energy optimization scheme derived from optimal control principles which exploits the structure of the metamorphosis optimality conditions. Our approach considers the change in pixel position and pixel appearance in a single framework. In contrast to previous techniques that compute a global warping based on feature masks of textures, our approach allows to transform one texture into another by considering both intensity values and structural features of textures simultaneously. We demonstrate the usefulness of our approach for different textures, such as stochastic, semi‐structural and regular textures, with different levels of complexities. Our method produces visually appealing transformation sequences with no user interaction.     

Invariant and adaptive geometrical texture features for defect detection and classification

Automatic texture defect detection is highly important for many fields of visual inspection. We introduce novel, geometrical texture features for this task, which are Euclidean motion invariant and texture adaptive: An algebraic function (rational, Padé, or polynomial) is integrated over intensities in local, circular neighborhoods on the image including an anisotropical texture analysis. Adaptiveness is achieved through the optimization of this feature kernel and further coefficients based on a simplex energy minimization, constrained by a measure of texture discrimination (Fisher criterion). A backpropagation trained, multilayer perceptron network classifies the textures locally. Our approach contains new properties, usually not common in feature theories: Theoretically implicit, multiple invariances and an automatic and specific adaptation of the features to the texture images. Experiments with a fabric data set and Brodatz textures reveal up to 98.6% recognition accuracy.     

Fast variational design of multiresolution curves and surfaces with B-spline wavelets

Multiresolution modeling provides a powerful tool for complex shape editing. To achieve a better control of deformations and a more intuitive interface, variational principles have been used in such multiresolution models. However, when handling multiresolution constraints, the existing methods often result in solving large optimization systems. Hence, the computational time may become too excessive to satisfy the requirements for interactive design in CAD. In this paper, we present a fast approach for interactive variational design of multiresolution models. By converting all constraints at different levels to a target level, the optimization problem is formulated and solved at the lower level. Thus, the unknown coefficients of the optimization system are significantly reduced. This improves the efficiency of variational design. Meanwhile, to avoid smoothing out the details of the shape in variational modeling, we optimize the change in the deformation energy instead of the total energy of the deformed shape. Several examples and the experimental results are given to demonstrate the effectiveness and efficiency of this approach.     

Accelerated Parallel Texture Optimization

Texture optimization is a texture synthesis method that can efficiently reproduce various features of exemplar textures. However, its slow synthesis speed limits its usage in many interactive or real time applications. In this paper, we propose a parallel texture optimization algorithm to run on GPUs. In our algorithm, k-coherence search and principle component analysis （PCA） are used for hardware acceleration, and two acceleration techniques are further developed to speed up our GPU-based texture optimization. With a reasonable precomputation cost, the online synthesis speed of our algorithm is 4000＋ times faster than that of the original texture optimization algorithm and thus our algorithm is capable of interactive applications. The advantages of the new scheme are demonstrated by applying it to interactive editing of flow-guided synthesis.     

Synthesis of Color Textures for Multimedia Applications

An algorithm for synthesizing color textures from a small set of parameters is presented in this paper. The synthesis algorithm is based on the 2-D moving average model, and realistic textures resembling many real textures can be synthesized using this algorithm. A maximum likelihood estimation algorithm to estimate parameters from a sample texture is also presented. By combining the estimation and synthesis algorithms, a color texture can be synthesized from a sample texture without human intervention. Using the estimated parameters, a texture larger than the original image can be synthesized from a small texture sample. The synthesis algorithm does not require an expensive iterative algorithm, and the quality of synthesized textures may be acceptable for many multimedia applications. In the experiment, various textures suitable for multimedia applications are synthesized from parameters estimated from real textures.     

Radon transform orientation estimation for rotation invariant texture analysis

This paper presents a new approach to rotation invariant texture classification. The proposed approach benefits from the fact that most of the texture patterns either have directionality (anisotropic textures) or are not with a specific direction (isotropic textures). The wavelet energy features of the directional textures change significantly when the image is rotated. However, for the isotropic images, the wavelet features are not sensitive to rotation. Therefore, for the directional textures, it is essential to calculate the wavelet features along a specific direction. In the proposed approach, the Radon transform is first employed to detect the principal direction of the texture. Then, the texture is rotated to place its principal direction at 0 degrees. A wavelet transform is applied to the rotated image to extract texture features. This approach provides a features space with small intraclass variability and, therefore, good separation between different classes. The performance of the method is evaluated using three texture sets. Experimental results show the superiority of the proposed approach compared with some existing methods.     

Visualization of Large‐Scale Urban Models through Multi‐Level Relief Impostors

In this paper, we present an efficient approach for the interactive rendering of large‐scale urban models, which can be integrated seamlessly with virtual globe applications. Our scheme fills the gap between standard approaches for distant views of digital terrains and the polygonal models required for close‐up views. Our work is oriented towards city models with real photographic textures of the building facades. At the heart of our approach is a multi‐resolution tree of the scene defining multi‐level relief impostors. Key ingredients of our approach include the pre‐computation of a small set of zenithal and oblique relief maps that capture the geometry and appearance of the buildings inside each node, a rendering algorithm combining relief mapping with projective texture mapping which uses only a small subset of the pre‐computed relief maps, and the use of wavelet compression to simulate two additional levels of the tree. Our scheme runs considerably faster than polygonal‐based approaches while producing images with higher quality than competing relief‐mapping techniques. We show both analytically and empirically that multi‐level relief impostors are suitable for interactive navigation through large urban models.     

Perspective-aware texture analysis and synthesis

This paper presents a novel texture synthesis scheme for anisotropic 2D textures based on perspective feature analysis and energy optimization. Given an example texture, the synthesis process starts with analyzing the texel (TEXture ELement) scale variations to obtain the perspective map (scale map). Feature mask and simple user-assisted scale extraction operations including slant and tilt angles assignment and scale value editing are applied. The scale map represents the global variations of the texel scales in the sample texture. Then, we extend 2D texture optimization techniques to synthesize these kinds of perspectively featured textures. The non-parametric texture optimization approach is integrated with histogram matching, which forces the global statics of the texel scale variations of the synthesized texture to match those of the example. We also demonstrate that our method is well-suited for image completion of a perspectively featured texture region in a digital photo.     

Extending natural textures with multi-scale synthesis

This paper presents a texture synthesis algorithm that was designed for the tile-less generation of large images of arbitrary size from small sample images. The synthesised texture shows features that are visually similar to the sample over a wide frequency range. The development of the algorithm aimed at achieving high quality results for a large range of natural textures, incorporation of the original samples in the synthesis product, ease of use and good texturing speed even with input sample data two magnitudes larger than used by previous techniques. Like other algorithms we utilise an implicit texture model by copying arbitrary shaped texture patches from the sample to the destination over a multi-scale image pyramid. Our method combines the advantages of different previous techniques with respect to quality. A mixture of exhaustive searching, massive parallel computing and the well-known LBG-algorithm ensures a good balance between texturing quality and speed.     

Elastic Tubes: Modeling Elastic Deformation of Hollow Tubes

The Cosserat theory of elastic rods has been used in a wide range of application domains to model and simulate the elastic deformation of thin rods. It is physically accurate and its implementations are efficient for interactive simulation. However, one requirement of using Cosserat rod theory is that the tubular object must have rigid cross‐sections that are small compared to its length. This requirement make it difficult for the approach to model elastic deformation of rods with large, non‐rigid cross‐sections that can change shape during rod deformation, in particular, hollow tubes. Our approach achieves this task using a hybrid model that binds a mesh elastically to a reference Cosserat rod. The mesh represents the surface of the hollow tube while the reference rod models bending, twisting, shearing and stretching of the tube. The cross‐sections of the tube may take on any arbitrary shape. The binding is established by a mapping between mesh vertices and the rod's directors. Deformation of the elastic tube is accomplished in two phases. First, the reference rod is deformed according to Cosserat theory. Next, the mesh is deformed using Laplacian deformation according to its mapping to the rod and its surface elastic energy. This hybrid approach allows the tube to deform in a physically correct manner in relation to the bending, twisting, shearing, and stretching of the reference rod. It also allows the surface to deform realistically and efficiently according to surface elastic energy and the shape of the reference rod. In this way, the deformation of elastic hollow tubes with large, non‐rigid cross‐sections can be simulated accurately and efficiently.     

基于Wang Tile的改进纹理合成算法

Wang Tiles由4个纹理块构建而成,并为Tiles拼接做准备。因为Wang Tiles是事先制作的,所以它是一种实时纹理合成方法。然而,在Tiles内部和相邻Tiles拐角处存在着匹配问题,因此,提出了一种改进Wang Tile的新方法,该方法使用一个新的样图来替换边界匹配样图的中心,并采用Image Quilting算法查找替换路径,从而生成Tile集合,继而合成大块纹理。实验结果表明,所绘制的纹理不仅成功克服了匹配问题,而且在某种程度上增强了纹理多样性。     

Associating visual textures with human perceptions using genetic algorithms

This paper deals with an approach allowing to associate visual textures with given human perceptions. Hereby, based on a forward model associating human perceptions for given visual textures, the deduction of an reverse process is presented which is able to associate and characterize visual textures for given human perceptions. For doing so, we propose a constraint-based genetic algorithm approach, which is able to minimize a specific optimization problem containing constraints in form of band-widths for valid individuals (low level features extracted from textures) in a population. The constraints are determined by relationships between (low level) features characterizing textures in form of high-dimensional approximation models. Additionally, in each iteration step checking for valid individuals is carried out with a texture/non-texture classifier or by using a convex hull over a set of valid textures. The whole approach is evaluated based on a real-world texture set used as a start population in the genetic algorithm and by defining various kinds of human perceptions (for which textures are sought) represented by adjective vectors in the aesthetic space. The generated individuals (low level feature vectors) have a high level of fitness (they are quite close to the pre-defined adjective vectors) and a small distance to the initial population. The textures synthesized based on the generated individuals are visualized and compared with textures synthesized by a time-intensive direct texture mixing and re-combination method based on a real-world texture data base.     

Texture synthesis via the matching compatibility between patches

A new patch-based texture synthesis method is presented in this paper. By the method, a set of patches that can be matched with a sampled patch for growing textures effectively, called the matching compatibility between patches, is generated first for each patch, and the set is further optimized by culling the patches that may cause synthesis conflicts. In this way, similarity measurement calculation for selecting suitable patches in texture synthesis can be greatly saved, and synthesis conflicts between neighbouring patches are substantially reduced. Furthermore, retrace computation is integrated in the synthesis process to improve the texture quality. As a result, the new method can produce high quality textures as texture optimization, the best method to date for producing good textures, and run in a time complexity linear to the size of the output texture. Experimental results show that the new method can interactively generate a large texture in 1024 × 1024 pixels, which is very difficult to achieve by existing methods. Supported by the National Basic Research Program of China (Grant No. 2009CB320802), the National Natural Science Foundation of China (Grant Nos. 60773026, 60833007), the National High-Tech Research & Development Program of China (Grant Nos. 2006AA01Z306, 2008AA01Z301), and the Research Grant of University of Macau     

Texture Particles

This paper presents an analytical extension of texture synthesis techniques based on the distribution of elementary texture components. Our approach is similar to the bombing, cellular, macrostructured and lapped textures techniques, but provides the user with more control on both the texture analysis and synthesis phases. Therefore, high quality results can be obtained for a large number of structured or stochastic textures (bricks, marble, lawn, etc.). The analysis consists in decomposing textures into elementary components — that we call ``texture particles'' — and for which we analyze their specific spatial arrangements. The synthesis then consists in recomposing similar textures directly on arbitrary surfaces by taking into account the previously computed arrangements, extended to 3D surfaces. Compared to ``pixel‐based'' analysis and synthesis methods, which have been recently generalized to arbitrary surfaces, our approach has three major advantages: (1) it is fast, which allows the user to interactively control the synthesis process. This further allows us to propose a large number of tools, granting a high degree of artistic freedom to the user. (2) It avoids the visual deterioration of the texture components by preserving their shapes as well as their spatial arrangements. (3) The texture particles can be not only images, but also 3D geometric elements, which extends significantly the domain of application.     

Efficient texture synthesis using strict Wang Tiles

Wang Tiles are constructed from four texture samples, arranged so they can always match a choice of other tiles at two edges. Because they are precomputed, Wang Tiles are a very efficient way to generate textures on the fly. But matching problems occur within tiles and at the corners of adjacent tiles. By replacing the edge-matching texture samples with a new sample in the center of the tile, and using the graph cut path-finding algorithm, we overcome these problems and introduce additional texture diversity. Our s-Wang Tiles are a stricter interpretation of the original Wang Tile design, and our tile set is also smaller than that required by ω-Tiles: only eight different tiles are required for a non-repetitive titling.     

RETRACTED ARTICLE: Texture-guided volumetric deformation and visualization using 3D moving least squares

Examining and manipulating the large volumetric data attract great interest for various applications. For such purpose, we first extend the 2D moving least squares (MLS) technique into 3D, and propose a texture-guided deformation technique for creating visualization styles through interactive manipulations of volumetric models using 3D MLS. Our framework includes focus+context (F+C) visualization for simultaneously showing the entire model after magnification, and the cut-away or illustrative visualization for providing a better understanding of anatomical and biological structures. Both visualization styles are widely applied in the graphics areas. We present a mechanism for defining features using high-dimensional texture information, and design an interface for visualizing, selecting and extracting features/objects of interest. Methods of the interactive or automatic generation of 3D control points are proposed for the flexible and plausible deformation. We describe a GPU-based implementation to achieve real-time performance of the deformation techniques and the manipulation operators. Different from physical deformation models, our framework is goal-oriented and user-guided. We demonstrate the robustness and efficiency of our framework using various volumetric datasets.