大尺寸纹理的实时合成

提出一种纹理合成方法,可实时高质量地生成大纹理.它先基于纹理特征变化的周期性分析,得到合适的纹理块尺寸,以使所划分的纹理块能高效反映这种周期性变化,便于生成高质量的纹理;然后,它在目标纹理上均衡地分布纹理块,使得垂直方向和水平方向上相邻的纹理块之间都留有一个块尺寸大小的空白区域,再对空白区域进行填充,以完成目标纹理的生成.显然,布块操作和填充操作均可并行地进行.同时,为每个纹理块预先生成可与其邻接匹配的纹理块集合,以便在填充计算时可用简便的集合求交计算来进行邻域约束的搜索,并将这种求交计算放在CPU中进行,而将邻接纹理块在重叠区域的缝合计算放到GPU中进行,以综合利用CPU和GPU的优势.实验表明,新方法可在一般微机上以45帧/秒的速度高质量地实时合成1024*1024的大纹理,而这是已有技术难以达到的.     

CA与生命游戏演化的组合模型*

基于摩尔奇偶型CA,提出一种结合生命游戏的演化模型.首先设置初始构型进行摩尔奇偶型规则演化,再引入生命游戏规则,按游戏规则演化;最后依此重复摩尔奇偶规则和生命游戏规则,直到演化整个元胞空间.证实在摩尔奇偶型CA的基础上,结合生命游戏在很大程度上减小了演化过程对初始构型的依赖,出现更为丰富的图元以及基于图元和演化框架的动态图案,且演化过程仍然符合摩尔奇偶型CA的演化规律.结合模型可以避免演化过度依赖初始构型以及演化过程的单调性,而且组合演化模型可以应用于图案生成,生成保持风格的规则几何纹理图案,丰富图案的生成方法.     

基于分布式优化的协同干扰任务分配研究

为保证目标区域干扰覆盖和最小能量消耗的优化目标,建立协同干扰任务分配模型。在分布式协同优化框架下,将集中式任务分配问题,转换为各个虚任务区内小规模的分布式优化问题,采用分解-协调优化模式和启发式遗传算法相结合的方法,实现对各个子区域优化问题的二次迭代求解。仿真结果表明,分布式协同优化方法能够有效降低协同干扰任务分配问题的求解规模,避免“维数灾”,具有可行性。     

布氏硬度自动测量中多圆检测方法

圆检测应用广泛,是布氏硬度自动测量的关键。针对圆检测中存在圆分裂、多个圆、不完整圆的情况,提出一种基于交叉圆合并、凸包点迭代纯化最小二乘拟合的圆检测方法。首先对图像进行纹理增强并二值化,其次提取有效区域的最小外接矩形,并得到圆弧与最小外接矩形的三个切点,得到初始圆,然后合并有交叉的圆。最后求合并圆的轮廓点与其凸包的交集,进行迭代纯化最小二乘拟合,最终得到亚像素级的圆半径值。最后通过实际应用测试,验证了论文方法的有效性。     

基于偏离映射的纹理传输算法研究

研究二维图像纹理传输问题,目前在二维纹理映射的传统算法中,主要通过基于纹理合成的传输算法实现纹理映射的效果.针对传统算法中基于纹理拼接导致迭代次数多、计算复杂度高的问题,为提高纹理映射的速度,提出了一种基于光照明模型实现纹理映射,产生纹理传输效果的算法.该算法主要采用Phong光照明模型对目标图像进行预处理形成虚拟表面,根据纹理图像和目标图像计算偏离映射角度,其次,参考图像的纹理特征根据偏离角度映射到目标图像中.实验结果表明,改进的纹理传输算法运行速度快,易于实现,避免了耗时的纹理合成计算,可针对局部目标区域进行局部映射,具有一定的实用性.     

基于蜂窝结构的传感器网络覆盖问题求解算法

在无线传感器网络中,求解能够完全覆盖目标区域的最小覆盖集是个NP难问题.在传感器节点数目较多时,目前只能通过近似算法求解.蜂窝结构是覆盖二维平面的最佳拓扑结构,但不能直接用于求解无线传感器网络的覆盖问题.提出了一种基于蜂窝结构的覆盖问题求解算法,在该算法迭代求解过程的每一阶段,选出一个节点加入到初始为空的节点集合中,并使得该节点集合的拓扑结构接近于蜂窝结构,直至该节点集合成为覆盖集.该算法在最坏情况下的时间复杂度为O(n3),这里n为传感器节点总数.实验结果表明该算法可在很短的时间内执行完,在所得覆盖集的大小方面要优于现有的覆盖问题求解算法.     

复杂区域刺绣问题的建模与求解

含有孔洞的图案是刺绣中经常遇到的图形。刺绣技术的特殊性要求这种图案的填充必须满足其特有的规则。如何为这种图形构成的复杂区域选择一条正确的填充路径是刺绣CAD系统中的一个重要算法。本文对该问题进行了建模,并提出了一种基于启发式搜索的模型求解方法。该方法在寻找路径的过程中可以满足任意设置出、入口点及针迹方向的问题。经过实验,这种方法可以求得较好的刺绣路径。     

平面及空间区域渐变无结构网格的自动生成

１．前 言 网格生成是许多数值计算首先要解决的问题．规则区域上的均匀网格,比较容易生成．但许多工程实际问题,求解区域边界形状极不规则,且由于物理参数的剧烈变化及解的性态复杂,对求解区域的网格疏密变化有某些特殊的要求．如何在非规则区域上自动生成符合使用者特殊要求的网格,是科学计算过程中人们所关心的问题． 本文基于 Ｄｅｌａｕｎａｙ三角划分原理,在二维（三维）区域给出一种自动生成渐变无结构三角形（四面体）网格的方法．对事先给定的指定结点集合和对应的间隔值集合,算法将首先自动生成全部边界结点和内部结点,然…     

旋转的Wang Tiles纹理合成算法

针对Wang Tiles存在的样图利用不完全、切割路径非最优、中心和拐角区域不匹配等问题,提出一种旋转的Wang Tiles纹理合成算法.使用4个正方形的子图块构造一个旋转的Wang Tile初始框架,把纹理重叠区域分为两种类型并引入基于边结构的最短路径求解方法快速确定切割路径,生成旋转的Wang Tiles集合后,采用随机的正菱形填充方法合成纹理.实验结果表明,对于多种类型的样图纹理,该算法都能够实时地获得质量较高的合成纹理.     

二维半自由曲面造型技术与雕刻系统

描述二维自由曲面的造型技术，提出了基于区域填充拉伸、纹理映射、线状模型的造型方法，并对中国水墨画的浮雕造型方法进行了描述。自然物体表面模型的生成技术是二维半模型造型的技术关键，采用由影调到物体表面外形的恢复技术，有效地解决了从实体图象到模型表面的重建问题。     

Wavelet packet neural networks for texture classification

Texture can be defined as a local statistical pattern of texture primitives in observer’s domain of interest. Texture classification aims to assign texture labels to unknown textures, according to training samples and classification rules. This paper describes the usage of wavelet packet neural networks (WPNN) for texture classification problem. The proposed schema composed of a wavelet packet feature extractor and a multi-layer perceptron classifier. Entropy and energy features are integrated wavelet feature extractor. The performed experimental studies show the effectiveness of the WPNN structure. The overall success rate is about 95%.     

语义规则驱动的中国古代建筑造型

根据中国古代建筑具有完整的营造规则约束的特点，提出了一种语义规则驱动的快速建模方法．根据中国古代建筑的建造规范，从中提取出若干造型规则，并且参数化表示主要的建筑结构特征，利用它们来控制模型的生成．定义了模型的构造模块——组件，通过迭代解释造型规则来控制组件的变换和组合，从而构造出一系列的中国古代建筑模型．通过扩充纹理库，可以得到不同效果的建筑模型．     

Texture mixing and texture movie synthesis using statisticallearning

We present an algorithm based on statistical learning for synthesizing static and time-varying textures matching the appearance of an input texture. Our algorithm is general and automatic and it works well on various types of textures, including 1D sound textures, 2D texture images, and 3D texture movies. The same method is also used to generate 2D texture mixtures that simultaneously capture the appearance of a number of different input textures. In our approach, input textures are treated as sample signals generated by a stochastic process. We first construct a tree representing a hierarchical multiscale transform of the signal using wavelets. From this tree, new random trees are generated by learning and sampling the conditional probabilities of the paths in the original tree. Transformation of these random trees back into signals results in new random textures. In the case of 2D texture synthesis, our algorithm produces results that are generally as good as or better than those produced by previously described methods in this field. For texture mixtures, our results are better and more general than those produced by earlier methods. For texture movies, we present the first algorithm that is able to automatically generate movie clips of dynamic phenomena such as waterfalls, fire flames, a school of jellyfish, a crowd of people, etc. Our results indicate that the proposed technique is effective and robust     

Wavelet domain association rules for efficient texture classification

The wavelet domain association rules method is proposed for efficient texture characterization. The concept of association rules to capture the frequently occurring local intensity variation in textures. The frequency of occurrence of these local patterns within a region is used as texture features. Since texture is basically a multi-scale phenomenon, multi-resolution approaches such as wavelets, are expected to perform efficiently for texture analysis. Thus, this study proposes a new algorithm which uses the wavelet domain association rules for texture classification. Essentially, this work is an extension version of an early work of the Rushing et al. [10], [11], where the generation of intensity domain association rules generation was proposed for efficient texture characterization. The wavelet domain and the intensity domain (gray scale) association rules were generated for performance comparison purposes. As a result, Rushing et al. [10], [11] demonstrated that intensity domain association rules performs much more accurate results than those of the methods which were compared in the Rushing et al. work. Moreover, the performed experimental studies showed the effectiveness of the wavelet domain association rules than the intensity domain association rules for texture classification problem. The overall success rate is about 97%.     

Resolution Independent NPR-Style 3D Line Textures

This work introduces a technique for interactive walk-throughs of non-photorealistically rendered (NPR) scenes using three-dimensional (3D) line primitives to define architectural features of the model, as well as indicate textural qualities. Line primitives are not typically used in this manner in favour of texture mapping techniques which can encapsulate a great deal of information in a single texture map, and take advantage of GPU optimizations for accelerated rendering. However, texture mapped images may not maintain the visual quality or aesthetic appeal that is possible when using 3D lines to simulate NPR scenes such as hand-drawn illustrations or architectural renderings. In addition, line textures can be modified interactively, for instance changing the sketchy quality of the lines. The technique introduced here extracts feature edges from a model, and using these edges, generates a reduced set of line textures which indicate material properties while maintaining interactive frame rates. A clipping algorithm is presented to enable 3D lines to reside only in the interior of the 3D model without exposing the underlying triangulated mesh. The resulting system produces interactive illustrations with high visual quality that are free from animation artifacts.     

基于改进最低水平线方法与遗传算法的矩形件排样优化算法

传统的最低水平线方法用于矩形件排样时可能产生较多未被利用的空白区域,造 成不必要的材料浪费。针对此缺陷,在搜索过程中引入启发式判断,实现空白区域的填充处理, 提高板材利用率。在应用遗传算法优化矩形件排样顺序时,在进化过程中采用分阶段设置遗传 算子的方法,改善算法的搜索性能与效果。通过改进最低水平线方法与基于分阶段遗传算子的 遗传算法相结合,共同求解矩形件排样问题。排样测试数据表明,所提出的矩形件排样优化算 法能够有效改善排样效果,提高材料利用率。     

An optimum feature extraction method for texture classification

Texture can be defined as a local statistical pattern of texture primitives in observer’s domain of interest. Texture classification aims to assign texture labels to unknown textures, according to training samples and classification rules. In this paper a novel method, which is an intelligent system for texture classification is introduced. It used a combination of genetic algorithm, discrete wavelet transform and neural network for optimum feature extraction from texture images. An algorithm called the intelligent system, which processes the pattern recognition approximation, is developed. We tested the proposed method with several texture images. The overall success rate is about 95%.     

Voxelized Minkowski sum computation on the GPU with robust culling

We present a new approach for computing the voxelized Minkowski sum (excluding any enclosed voids) of two polyhedral objects using programmable Graphics Processing Units (GPUs). We first cull out surface primitives that will not contribute to the final boundary of the Minkowski sum, analyzing and adaptively bounding the rounding errors of the culling algorithm to solve the floating point error problem. The remaining surface primitives are then rendered to depth textures along six orthogonal directions to generate an initial solid voxelization of the Minkowski sum. Finally we employ fast flood fill to find all the outside voxels. We generate both solid and surface voxelizations of Minkowski sums without enclosed voids and support high volumetric resolution of 1024³ with low video memory cost. The whole algorithm runs on the GPU and is at least one order of magnitude faster than existing boundary representation (B-rep) based algorithms. It avoids the large number of 3D Boolean operations needed in most existing algorithms and is easy to implement. The voxelized Minkowski sums can be used in a variety of applications including motion planning and penetration depth computation.     

求解矩形和圆形装填问题的最大穴度算法

在超大规模集成电路设计，裁缝裁剪布料，玻璃切割等工作中提出了矩形和圆形装填问题，即把不同大小的矩形块和圆饼装入一个矩形容器中，以最大化容器的面积利用率为优化目标。对这一问题，可采用模拟退火，遗传算法等国际流行算法进行求解，但这些方法计算时间较长，计算结果的优度也不甚理想。利用人类的智慧和经验，提出了一种求解此问题的最大穴度算法。并对3个随机生成的测试实例进行了实算测试。所得结果的平均面积利用率为90．80％，平均计算时阊为8．38s。测试结果表明，算法对求解矩形和圆形装填问题是行之有效的。     

Textures revisited

We describe texture generation methods for complex objects. Recently developed 3D scanning devices and high-resolution cameras can capture the complex geometry of an object and yield high-resolution images. However, generating a textured model from this input data is still a difficult problem. This task is divided into three subproblems: parameterization, texture combination, and texture restoration. A low-distortion parameterization method is presented, which minimizes geometry stretch energy. Photographs of the object taken from multiple viewpoints under modestly uncontrolled illumination conditions are merged into a seamless texture using our new texture combination method. We also demonstrate a texture restoration method that can fill in missing pixel information when the input photographs do not provide sufficient information to cover the entire surface due to self-occlusion or registration errors. Our methods are fully automatic, except for the registration process between a 3D model and input photographs. We demonstrate the application of our method to human face models for evaluation. The techniques presented in this paper make a consistent and complete pipeline to generate the texture of a complex object.