基于样本学习的肖像画自动生成算法

提出了一种基于样本学习的人脸肖像画自动生成算法，文章采用非均匀的马尔科夫随机场模型来描述肖像画与人脸图像之间的统计关系，并使用基于训练样本的非参数化的概率表示，在贝叶斯优化的框架下设计了迭代采样算法，可以自动的从人脸图像生成特定风格的肖像画，在该方法中，使用非均匀的统计模型是保持肖像中人脸结构准确性的关键，文中所提供的例子表明了该文方法的有效性。     

基于灰度投影法的乐器板材纹理分析方法

在对乐器板材进行选择时,需要对其纹理进行判别,以便选择可发出优质音色的板材。通常对板材的选择是人工目测挑选的,这样大大降低了乐器制作的效率,对所选板材质量的优劣评价缺少科学理论依据。借助计算机图像处理的方法可自动对板材的规格进行判断。通过对板材纹理的分析,应用灰度投影算法与均值取值法相结合的方式,给出了一种可以自动对板材纹理间宽度进行计算的方法。实验证明,该方法可减小人工测量的误差,加快板材的选取过程,具有较好准确性与鲁棒性。     

用OpenGL实现纹理自动生长

介绍了一种利用较小的存储空间和计算时间,实现在大面积网格数据上动态贴纹理的方法。利用此方法使用适当的纹理函数,结合OpenGL的自动纹理扩展可在动画过程中自动的生成纹理贴图。     

非真实感铅笔画艺术效果研究

改进了非真实感铅笔画艺术效果的绘制算法,利用图像分割、线积分卷积技术确定线条纹理的方向,模拟得到局部区域铅笔画的笔划纹理。采用轮廓线增强、灰度色调控制最终铅笔画艺术效果的边缘信息和灰度色调。通过色彩传输技术得到具有彩色铅笔画的艺术效果。     

基于纹理和草图的图像铅笔画绘制

针对目前铅笔画生成方法中的线条不够灵活、纹理缺少方向感的问题,提出了一种基于带方向的纹理和线条草图将一幅图像转换为铅笔画风格的方法。首先,对输入图像进行直方图匹配得到图像的色调图,并将图像分割为多个区域,对每个区域,根据其颜色和形状计算其色调和方向,以此决定铅笔纹理的色调和方向;然后,通过可调整的线性卷积方法得到铅笔画的线条草图;最后,将纹理和草图结合得到铅笔画效果。运用提出的方法对不同类型的自然图像进行了铅笔画的转换,并与已有的线卷积积分方法和基于色调的方法进行了对比。实验结果表明带方向的区域纹理能更好地模拟手工铅笔画纹理的方向,可调整的线条能够更好地模拟手工铅笔画的线条的随意性和灵活性。     

A unified framework for designing textures using energy optimization

A unified framework is proposed for designing textures using energy optimization and deformation. Our interactive scheme has the ability to globally change the visual properties of texture elements, and locally change texture elements with little user interaction. Given a small sample texture, the design process starts with applying a set of global deformation operations (rotation, translation, mirror, scale and flip) to the sample texture to obtain a set of deformed textures automatically. Then we further make the local deformation to the deformed textures interactively by replacing the local-texture elements regions from other textures. By utilizing the energy optimization method, interactive selections and deformations of local-texture elements are accomplished simply through indicating the positions of texture elements very roughly with a brush tool. Finally the deformed textures are further utilized to create large textures with the fast layer-based texture deformation algorithm, and the wavelet-based energy optimization. Our experimental results demonstrate that the proposed approach can help design a large variety of textures from a small example, change the locations of texture elements, increase or decrease the density of texture elements, and design cyclic marbling textures.     

Periodic pattern of texture analysis and synthesis based on texels distribution

Recently, sample-based texture synthesis techniques have drawn significant attention from researchers. These existing approaches mainly use the Markov Random Field (MRF) or texture features as texture model to analyze the local properties of sample textures. Indeed, human perception is sensitive to structure and periodicity. In this paper, we perform texture synthesis by taking into account the distribution of texels. Given a sample texture, the analysis procedure consists in segmenting texture into individual texels, and detecting each texel in order to analyze their neighborhood relationships by constructing connectivity. Then the synthesis process consists in reproducing a new large texture directly on a user-specified canvas by recomposing segmented texels, which synthesizes two-dimensional texel arrangements based on the previously constructed neighborhood relationships of texels. Results show that the proposed method is successful in generating textures visually indistinguishable to the sample textures. Moreover, the method especially deals with the near-regular textures, which well preserves underlying structural regularity.     

基于纹理规则性分析的图像填补方法

针对目前图像填补中存在的很难自动分析和提取前景、背景,然后合理地恢复图像等问题,提出一种利用纹理规则性分析和纹理合成技术进行有效图像填补的方法.为了实现自动化的提取,将前景视为近似规则纹理的场景区域,首先引入纹理规则性分析方法,利用近似规则纹理具有强烈的信号规则性的特点自动有效地提取前景信息;然后通过合成优先级的思想将纹理合成技术和图像修补技术结合起来,提出一个合理的背景填补方法以恢复图像.实验结果证明,该方法可以自动有效地提取具有近似规则纹理特征的前景或背景内容,并合理地恢复图像.     

基于三角剖分的人脸纹理映射

采用通用的三维人脸模型和任意的人脸纹理图像,基于Delaunay三角剖分,提出了一种灵活的3D人脸的纹理映射方法。该方法对人脸特征点集做三角剖分,在纹理图像和三维网格之间建立了一个准确的拓扑同构映射关系,从而得到高真实度的纹理映射。该算法不受网格调整精度的影响,同时适用于不同的纹理映射到同一三维人脸模型上。     

交互式着色的彩色铅笔画生成方法

提出了一种基于卷积算子的铅笔滤镜生成算法,能够方便快速地产生用户所需要的各种类型的铅笔滤镜;在此基础上,设计了一种新的基于局部色彩扩散原理的交互式彩色铅笔画生成算法。实验证明,该算法在保持铅笔纹理的物理特性及绘画特征的同时,能够快速将输入图像转化为彩色铅笔画效果,取得了较满意的效果。     

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     

一种新型纹理图像生成方法研究

利用人工神经网络BP算法 ,提出了一种新型纹理图像生成方法。其技术关键在于正确选取能够全面描述纹理的特征函数 ,并建立有效的人工神经网络模型。生成纹理图像时只需给出原始纹理图像 ,系统将自动计算图像的特征函数并做为BP网络输入 ,便可输出图像的灰度向量 ,生成纹理图像。实验结果表明 ,该方法适合于生成多种纹理图像 ,取得了令人满意的效果。     

基于概念的自然纹理分类

纹理是图像的重要视觉特征,纹理分类是图像分析、计算机视觉等领域一个重要的研究课题。文章不同于以往的纹理分类方法,提出了一种基于概念的纹理分类方法。该方法以中文自然语言中常用的纹理描述词作为纹理概念,给出了10个基本概念的纹理分类,然后利用Gabor滤波参数和SVM对自然纹理图像进行分类,实现了图像的纹理视觉特征到纹理概念的转换,部分解决了纹理概念与纹理参数之间的“语义鸿沟”问题。     

基于直方图比率特征的虹膜图像分类方法

针对虹膜纹理的模式分类问题,提出了一种直方图比率特征的虹膜纹理分类方法。该方法利用虹膜图像的直方图信息,提取虹膜灰度等级对,通过灰度等级对自相关策略。计算虹膜纹理的直方图比率特征,实现了虹膜纹理的最大化分类。在相同的实验条件下,对不同样本的虹膜图像进行了仿真实验,结果表明：直方图比率方法较传统的直方图方法平均提高了3．05％的识别率。     

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.     

Retrieval of translated, rotated and scaled color textures

A new method for color texture retrieval using color and edge features is proposed in this study. The proposed method unifies color and edge features rather than simply analyzing only color characteristics. First, the distributions of color and local edge patterns are used to derive a similarity measure for a pair of textures. Then, a retrieval method based on the similarity measure is proposed to retrieve texture images from a database of color textures. Finally, the similarity measure is extended to retrieve texture regions from a database of natural images. Since the proposed feature distributions can resist variations in translation, rotation and scale, our method has the ability to retrieve texture images or regions that change in translation, rotation and/or scale. The effectiveness and practicability of the proposed method have been demonstrated by various experiments.     

Self Tuning Texture Optimization

The goal of example‐based texture synthesis methods is to generate arbitrarily large textures from limited exemplars in order to fit the exact dimensions and resolution required for a specific modeling task. The challenge is to faithfully capture all of the visual characteristics of the exemplar texture, without introducing obvious repetitions or unnatural looking visual elements. While existing non‐parametric synthesis methods have made remarkable progress towards this goal, most such methods have been demonstrated only on relatively low‐resolution exemplars. Real‐world high resolution textures often contain texture details at multiple scales, which these methods have difficulty reproducing faithfully. In this work, we present a new general‐purpose and fully automatic self‐tuning non‐parametric texture synthesis method that extends Texture Optimization by introducing several key improvements that result in superior synthesis ability. Our method is able to self‐tune its various parameters and weights and focuses on addressing three challenging aspects of texture synthesis: (i) irregular large scale structures are faithfully reproduced through the use of automatically generated and weighted guidance channels; (ii) repetition and smoothing of texture patches is avoided by new spatial uniformity constraints; (iii) a smart initialization strategy is used in order to improve the synthesis of regular and near‐regular textures, without affecting textures that do not exhibit regularities. We demonstrate the versatility and robustness of our completely automatic approach on a variety of challenging high‐resolution texture exemplars.     

方向性纹理织物疵点检测方法研究

通过分析方向性织物纹理的特点,提出了一种织物疵点检测新的方法。首先根据正常纹理Hough变换确定织物纹理的纹路方向;然后采用方向性小波对织物纹理图像进行方向性的分解,并在此基础上从分解后的各细节子图中提取子窗口的特征;最后通过BP神经网络进行织物疵点识别。实验结果表明了该方法的有效性。