毛发的结构造型方法

人物的绘制是当今计算机图形学中的一项重要任务,特别吸引人的是头发的生成技术.头发生成技术的难点在于其精细的形状和庞大的数量.文章针对头发提出了弯曲纹元和三维纹理体的概念,并给出了它们的数学表达式以及对不同发型的描述方法.弯曲纹元和三维纹理体是一般三维纹理的结构.带三维纹理的景物是由弯曲纹元和三维纹理体构成的,三维纹理体可再分成多个弯曲纹元,因而指明了三维纹理空间的结构,并解决了三维纹理曲面的造型问题.文章通过不同例子表明了结构造型方法在描述头发中的应用价值.     

Bilinear Accelerated Filter Approximation

Our method approximates exact texture filtering for arbitrary scales and translations of an image while taking into account the performance characteristics of modern GPUs. Our algorithm is fast because it accesses textures with a high degree of spatial locality. Using bilinear samples guarantees that the texels we read are in a regular pattern and that we use a hardware accelerated path. We control the texel weights by manipulating the u, v parameters of each sample and the blend factor between the samples. Our method is similar in quality to Cardinality‐Constrained Texture Filtering [ MS13 ] but runs two times faster.     

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.     

纹理合成中的邻域大小自动识别算法

在许多采用马尔可夫随机场模型的基于样图的纹理合成算法中，邻域大小决定着这些合成算法的纹理合成质量和合成速度。但目前该参数是由用户根据目测的纹元大小进行试探性的设置，往往需要多次试探才能获得较为理想的邻域大小，设置起来非常不便。文章运用统计分析的方法，提出了一种邻域大小自动识别的算法。实验表明，在一个较宽的纹理范围内，该算法能够自动地识别出理想的邻域大小。     

纹理元提取与纹理合成的自由参数估计

该文分析了常见的两类纹理:随机性纹理与结构性纹理的特性,针对其不同的统计特征,采用两种方法提取纹理元。对随机性纹理采用变尺度窗口特征跟踪的方法提取纹理元;对结构性纹理,利用其具备较为明显的边界特性,采用基于图像分割的方法提取纹理元。为避免图像噪声和自然边界不连续造成的提取误差,使用Bayes分类进行二次精细分割加以修正。实验证明,该文提出的方法对两类纹理元有较好的提取效果,同时可以用来估计纹理合成时的自由参数。     

Knowledge Discovery in an Oceanographic Database

Knowledge discovery from image data is a multi-step iterative process. This paper describes the procedure we have used to develop a knowledge discovery system that classifies regions of the ocean floor based on textural features extracted from acoustic imagery. The image is subdivided into rectangular cells called texture elements (texels); a gray-level co-occurence matrix (GLCM) is computed for each texel in four directions. Secondary texture features are then computed from the GLCM resulting in a feature vector representation of each texel instance. Alternatively, a region-growing approach is used to identify irregularly shaped regions of varying size which have a homogenous texture and for which the texture features are computed. The Bayesian classifier Autoclass is used to cluster the instances. Feature extraction is one of the major tasks in knowledge discovery from images. The initial goal of this research was to identify regions of the image characterized by sand waves. Experiments were designed to use expert judgements to select the most effective set of features, to identify the best texel size, and to determine the number of meaningful classes in the data. The region-growing approach has proven to be more successful than the texel-based approach. This method provides a fast and accurate method for identifying provinces in the ocean floor of interest to geologists.     

采用邻域直方图匹配的矢量纹理图案合成

矢量纹理图案是指由一种或几种矢量图案元素按照一定的规则进行分布而形成的纹理图案。这种纹理图案在日常生活中随处可见,对其进行自动合成是非真实感绘制中的一个重要研究课题。本文提出一种基于样本的矢量纹理图案合成方法,可以根据一小块样本图案自动合成与该样本图案有类似分布规则的大面积图案。在此方法中,我们采用一种“邻域直方图”的策略来进行元素邻域信息的匹配与合成：将矢量元素的邻域空间用以该元素为中心的射线束和同心圆簇划分为一定数量的小格;统计每个小格中出现的邻域元素数量就可以形成一幅直方图;以此直方图信息作为当前元素的邻域信息在样本图案中进行匹配搜索,从而完成图案的合成。该方法不仅可以应用于仅有一种图案元素的分布合成,还可扩展至多种元素图案的复杂分布合成。与以往的矢量纹理图案合成方法相比,本文方法不需要复杂的元素关系分析和频繁的元素位置比较,实现简单,效率较高。实验表明,本文方法对于规则、半规则及随机的图案分布,都可达到较好的合成效果,这对于非真实感绘制中的风格图案生成、笔划分布计算等都将具有很好的应用意义。     

Layered textures for image-based rendering

An extension to texture mapping is given in this paper for improving the efficiency of image-based rendering. For a depth image with an orthogonal displacement at each pixel, it is decomposed by the displacement into a series of layered textures (LTs) with each one having the same displacement for all its texels. Meanwhile, some texels of the layered textures are interpolated for obtaining a continuous 3D approximation of the model represented in the depth image. Thus, the plane-to-plane texture mapping can be used to map these layered textures to produce novel views and the advantages can be obtained as follows: accelerating the rendering speed, supporting the 3D surface details and view motion parallax, and avoiding the expensive task of hole-filling in the rendering stage. Experimental results show the new method can produce high-quality images and run faster than many famous image-based rendering techniques.     

Adaptive scale filtering: a general method for obtaining shape fromtexture

Introduces adaptive scale filtering, a general method for deriving shape from texture under perspective projection without recourse to prior segmentation of the image into geometric texture elements (texels), and without thresholding of filtered images. If texels on a given surface can be identified in an image then the orientation of that surface can be obtained. However, there is no general characterization of texels for arbitrary textures. Furthermore, even if the size and shape of texels on the surface is invariant with regard to position, perspective projection ensures that the size and shape of the corresponding image texels vary by orders of magnitude. Commencing with an initial set F_O of identical image filters, adaptive scale filtering iteratively derives a set F_N which contains a unique filter for each image position. Each element of F_N is tuned to the three-dimensional structure of the surface; that is, all image filters in F_N back-project to an identical shape and size on the surface. Thus image texels of various sizes, but associated with a single spatial scale on the surface, can be identified in different parts of the image. When combined with conventional shape from texture methods, edges derived using F_N provide accurate estimates of surface orientation. Results for planar surfaces are presented     

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.     

Fast Hybrid Approach for Texturing Point Models

We present three methods for texturing point models from sample textures. The first method, the point parameterization method, uses a fast distortion‐bounded parameterization algorithm to flatten the point model's surface into one or more 2D patches. The sample texture is mapped onto these patches and alpha blending is used to minimize the discontinuity in the gaps between the patches. The second method is based on neighborhood matching where a color is assigned to each point by searching the best match within an irregular neighborhood. The hybrid method combines the former two methods, capitalizing on the advantages of both. The point parameterization method is used first to color most of the points, and the point neighborhood‐matching method is then applied to the points belonging to the gaps between the parameterized patches to minimize the discontinuity. We opt for fast texture generation, while some discontinuities may appear in the gaps of anisotropic textures.     

Parameterization‐Aware MIP‐Mapping

We present a method of generating mipmaps that takes into account the distortions due to the parameterization of a surface. Existing algorithms for generating mipmaps assume that the texture is isometrically mapped to the surface and ignore the actual surface parameterization. Our method correctly downsamples warped textures by assigning texels weights proportional to their area on a surface. We also provide a least‐squares approach to filtering over these warped domains that takes into account the postfilter used by the GPU. Our method improves texture filtering for most models but only modifies mipmap generation, requires no modification of art assets or rasterization algorithms, and does not affect run‐time performance.     

A Geometrical Based Method for Highly Complex Structured Textures Generation

Conventional 2D or 3D texturing methods do not permit an efficient simulation of highly complex structured textures like fire, fur, cotton, etc. More recent techniques, using specific kinds of 3D textures, such as hypertextures or texels based on volume rendering algorithms, are more interesting, for the simulation of such special types of textures. Unfortunately, these techniques remain still restricted because either they need a functional modelling of the object, as it is the case of hypertextures, or they are strictly limited to one specific kind of texture, as it is the case of texels. In this paper we present a new approach for applying a wide range of very different types of highly complex structured textures (fur, fire, water drops, cotton, fume, …) on every kind of objects. This method is particularly based on the geometrical information given by a geometrical model (as the polyhedral or CSG modelling). Like hypertextures or texels, our method uses the volume density rendering, but it isfree of the serious above mentioned restrictions of these methods. In addition, it allows an easy and very intuitive control of the global geometrical shape of generated textures. Its manipulation is simple evenfor a novice user.     

基于PSO的Wang Tiles纹理合成

如何在纹理样图中选择组成Wang Tiles的图像块决定着纹理的合成质量。基于PSO的Wang Tiles纹理合成通过粒子群优化算法在纹理样图中快速搜索边界差异最小的图像块,并用选取的图像块构建Wang Tiles,最后用Wang Tiles纹理合成算法合成纹理。实验表明,该算法合成的纹理具有较少的接缝,比随机选择图像块具有更好的合成效果。     

The facial texture analysis for the automatic portrait drawing

It is important to draw the portrait by the facial textures automatically. In order to analyze the detailed textures located at the neighborhood of the eyes, the texture analyzing method is studied. Firstly, we propose the principle of selecting the feature points and the portrait drawing method by B-spline curves using the self-reference parameter. Then, the wavelet transform is used to analyze the position, the direction and the intensity of the double-fold eyelid textures. Experimental results have shown that the facial texture analyzing method is effective for drawing portraits automatically.     

Preserving the autocovariance of texture tilings using importance sampling

By-example aperiodic tilings are popular texture synthesis techniques that allow a fast, on-the-fly generation of unbounded and non-periodic textures with an appearance matching an arbitrary input sample called the “exemplar”. But by relying on uniform random sampling, these algorithms fail to preserve the autocovariance function, resulting in correlations that do not match the ones in the exemplar. The output can then be perceived as excessively random. In this work, we present a new method which can well preserve the autocovariance function of the exemplar. It consists in fetching contents with an importance sampler taking the explicit autocovariance function as the probability density function (pdf) of the sampler. Our method can be controlled for increasing or decreasing the randomness aspect of the texture. Besides significantly improving synthesis quality for classes of textures characterized by pronounced autocovariance functions, we moreover propose a real-time tiling and blending scheme that permits the generation of high-quality textures faster than former algorithms with minimal downsides by reducing the number of texture fetches.     

基于Ashikhmin和Kong纹理合成算法的改进方法

Ashikhmin和Kong等人分别于2001年和2002年提出的纹理合成算法是当今典型的两种纹理合成算法,虽然前者对大部分自然纹理合成有不错的效果,但对变化较平缓的纹理合成效果不佳,而后者在合成时的时间复杂度较高.在上述两种算法的基础上,结合其优点,提出了一种把搜索范围限制在当前决的邻域的新的合成算法,并取得了理想的效果.     

纹理合成的自相关性判别法及其应用

从加快纹理相似性的判别速度出发,提出了一种纹理合成的自相关性判别法.传统的纹理合成算法随着邻域和样本的增大,计算量将成倍增加,纹理合成速度减慢的劣势逐渐体现出来.因此,算法对样本纹理建立简单的自相关性距离查找表,利用L邻域内像素的自相关性距离作为像素匹配的判别依据,以查找取代传统匹配过程中的繁琐计算,极大地加快了合成速度,可实现动态的、多精度的合成效果调控,以及避免块匹配中易出现纹理接缝的问题.经验证,该算法可在纹理合成、图像修补及纹理检索中应用,并可很好地达到实时的应用要求.     

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.