用于人体运动合成的运动纹理模型

本文提出了一种新的用于人体运动合成的运动纹理（Motion Texture)模型。该模型使用统计方法自动分析人体运动捕捉数据，并且可以合成与原始数据在统计特性上相同的人体运动。运动纹理模型是一个两层统计模型，其中包含的两层分别由一组运动基元和这些基元的统计分布组成。模型中用线性动态系统来表示单个运动基元。运动基元的统计分布由相关转移矩阵来描述。本文详细地讨论了如何通过最大似然准则来学习运动纹理模型的方法，并给出了如何用运动纹理模型自动合成复杂的人体运动序列的算法。通过使用运动纹理模型，可以实现对复杂人体运动在不同层次上的编辑。既可以在运动基元层次上修改运动细节，也可以在更高的层次上实现编辑。本文给出了使用运动纹理模型合成舞蹈动作的一些实验。这些实验验证了模型的有效性。     

基于改进动态纹理模型的人体运动分析

人体运动分析是计算机视觉领域最活跃的研究课题之一。文中提出2种描述人体运动序列的改进动态纹理模型:二值动态纹理模型和张量子空间动态纹理模型。假设二值图像服从Bernoulli分布,二值动态纹理模型使用二值主成分分析来学习训练模型的参数。张量子空间动态纹理模型将图像看作张量, 引入张量子空间分析的方法分别对其行向量和列向量进行降维,将其转化为低维灰度图像,然后用动态纹理模型描述灰度图像序列。在人体行为和步态数据库上的实验结果验证2种改进动态纹理模型的有效性。     

基于基元分布的误差可调纹理分类算法

提出一种基于纹理基元分布统计的纹理分类算法，选定一组代表像素变化的基元序列，计算每一个基元在纹理图像中的覆盖比例，用得到的纹理基元属性分布作为描述参数；由于相似纹理其属性也是相似的，同类纹理必然有接近的基元分布参数，计算参与实验的纹理样本的基元分布的互方差及互相关，与代表相似程度的阈值比较判断，由获得的共性来锁定同类纹理；为使同类纹理具有可参照的标准，产生针对每一类纹理的标准类分布。对Brodatz的111纹理不同相似程度的分类结果表明，该方法保证了统计结果与视觉判断的一致性，可用于纹理的分类及识别。     

线状纹理的一种属性关系图描述方法及其应用

纹理特征是图像分析的重要线索，纹理分析方法可以分为统计方法和结构方法，两者各有优劣，结合统计分析方法和结构分析方法两个方面的优点，提出了一种线状纹理的属性关系图（attributed relational graphs,ARG）描述方法，用属性关系图来描述图像纹理，并应用于图像检索中，属性关系图描述方法用图的结构直观地描述了图像的特征，具有很强的表达能力，提取直线段作为线状纹理的基元，并引入了基元间具有平移、旋转和伸缩不变性的关系属性，使纹理图像的识别和检索具有好的抗噪声能力，实验证明该方法取得了令人满意的识别和检索效果。     

交互式纹理变形算法

基于纹理基元的交互式纹理变形算法的目标是产生一个变形序列，使得源纹理图像能够自然地变形到给定的目标纹理图像．由于很多纹理图像都含有重复出现的结构性基元，因此可以设计出一种交互式的系统来方便用户实现变形效果．(1)用户在给定的两幅纹理图像中各选择一个感兴趣的基元；(2)两幅纹理中所有相似的基元图案会被自动检测．和定位；(3)通过最小化基元匹配的总距离求出两幅图像中基元的配对；(4)结合基元的配对信息和用户指定的特：征点信息生成变形序列．实验表明，在少许交互的使用下，可以对许多输入的纹理图像获得很好的变形效果。     

基于纹理基元空间分布特征的图像检索算法*

针对纹理统计法和结构法各自存在的问题,提出了一种基于纹理基元空间分布特征的图像检索算法。首先借鉴方块编码的思想来定义图像的纹理基元,然后在对纹理基元的统计分布研究的基础上,针对每一种纹理基元构造纹理基元空间分布图,提出采用纹理基元空间分布特征矢量对图像内容进行描述。实验结果表明,该算法既有效利用了图像的纹理信息,又考虑了纹理的空间分布信息,具有较好的检索效果。     

一种快速有效的图像纹理谱描述子

提出了一种基于纹理基元等价类的纹理谱描述子来描述图像的纹理特征．该纹理谱刻画邻域内像素灰度变化模式，以纹理谱直方图方式表示图像内容．与Gabor纹理特征的对比实验表明，文中的纹理谱描述子特征提取速度快、检索准确率高．最后给出了实验结果和性能评价．     

基于纹理基元的图象分割

纹理分割是图象处理的基本问题之一．针对广泛的纹理图象，需要一个高效、鲁棒的分割方法，因此提出了一种基于纹理基元的纹理图象分割算法．首先，以Harr小波为变换工具，得到具有方向性的纹理子图象；然后给出了一种新的纹理基元提取方法，并在此基础上，应用统计方法和矢量场，对纹理区域进行由粗到细的分割．通过这种方法不仅可以对纹理图象进行分割，还可以对同一区域的纹理结构进行描述，从而有利于在这种分割方法基础上，进行更高层次的图象处理．     

在线高斯混合模型和纹理支持的运动分割

运动分割是基于视频的运动分析中的基本问题．通过颜色和纹理特征的线性组合，实现了一种新的检测运动目标的算法．在线高斯混合模型不仅用于对背景进行更新，而且也用于计算像素颜色差异和颜色权值；纹理特征用于描述局部区域内的结构信息，提高了运动检测算法的鲁棒性．对不同场景的运动分割结果表明，该算法是高效和实用的．     

基于属性关系直方图统计的线状纹理图像检索方法

提出一种基于属性关系图ARG描述的线状纹理图像检索方法。针对一般的ARG图匹配算法运算量大、检索速度慢的问题，在用ARG描述线状纹理特征的基础上，通过计算纹理基元属性关系直方图之间的归一化距离来衡量图像的相似度，大大提高了运算速度。应用于鞋底花纹图像库的实验结果表明，该方法对于线状纹理特征具有较强的描述能力，对于平移、旋转和伸缩具有较好的不变性、检索速度和检索结果均能满足应用要求。     

Superpixel-based class-semantic texton occurrences for natural roadside vegetation segmentation

Vegetation segmentation from roadside data is a field that has received relatively little attention in present studies, but can be of great potentials in a wide range of real-world applications, such as road safety assessment and vegetation condition monitoring. In this paper, we present a novel approach that generates class-semantic color–texture textons and aggregates superpixel-based texton occurrences for vegetation segmentation in natural roadside images. Pixel-level class-semantic textons are learnt by generating two individual sets of bag-of-word visual dictionaries from color and filter bank texture features separately for each object class using manually cropped training data. For a testing image, it is first oversegmented into a set of homogeneous superpixels. The color and texture features of all pixels in each superpixel are extracted and further mapped to one of the learnt textons using the nearest distance metric, resulting in a color and a texture texton occurrence matrix. The color and texture texton occurrences are aggregated using a linear mixing method over each superpixel and the segmentation is finally achieved using a simple yet effective majority voting strategy. Evaluations on two datasets such as video data collected by the Department of Transport and Main Roads, Queensland, Australia, and a public roadside grass dataset show high accuracy of the proposed approach. We also demonstrate the effectiveness of the approach for vegetation segmentation in real-world scenarios.     

Texture Splicing

We propose a new texture editing operation called texture splicing. For this operation, we regard a texture as having repetitive elements (textons) seamlessly distributed in a particular pattern. Taking two textures as input, texture splicing generates a new texture by selecting the texton appearance from one texture and distribution from the other. Texture splicing involves self‐similarity search to extract the distribution, distribution warping, context‐dependent warping, and finally, texture refinement to preserve overall appearance. We show a variety of results to illustrate this operation.     

Accurate spatio-temporal reconstruction of missing data in dynamic scenes

In this paper, the accurate method for texture reconstruction with non-desirable moving objects into dynamic scenes is proposed. This task is concerned to editor off-line functions, and the main criteria are the accuracy and visibility of the reconstructed results. The method is based on a spatio-temporal analysis and includes two stages. The first stage uses a feature points tracking to locate the rigid objects accurately under the assumption of their affine motion model. The second stage involves the accurate reconstruction of video sequence based on texture maps of smoothness, structural properties, and isotropy. These parameters are estimated by three separate neural networks of a back propagation. The background reconstruction is realized by a tile method using a single texton, a line, or a field of textons. The proposed technique was tested into reconstructed regions with a frame area up to 8–20%. The experimental results demonstrate more accurate inpainting owing to the improved motion estimations and the modified texture parameters.     

Effective texture classification by texton encoding induced statistical features

Effective and efficient texture feature extraction and classification is an important problem in image understanding and recognition. Recently, texton learning based texture classification approaches have been widely studied, where the textons are usually learned via K-means clustering or sparse coding methods. However, the K-means clustering is too coarse to characterize the complex feature space of textures, while sparse texton learning/encoding is time-consuming due to the l₀-norm or l₁-norm minimization. Moreover, these methods mostly compute the texton histogram as the statistical features for classification, which may not be effective enough. This paper presents an effective and efficient texton learning and encoding scheme for texture classification. First, a regularized least square based texton learning method is developed to learn the dictionary of textons class by class. Second, a fast two-step l₂-norm texton encoding method is proposed to code the input texture feature over the concatenated dictionary of all classes. Third, two types of histogram features are defined and computed from the texton encoding outputs: coding coefficients and coding residuals. Finally, the two histogram features are combined for classification via a nearest subspace classifier. Experimental results on the CUReT, KTH_TIPS and UIUC datasets demonstrated that the proposed method is very promising, especially when the number of available training samples is limited.     

Representing and Recognizing the Visual Appearance of Materials using Three-dimensional Textons

We study the recognition of surfaces made from different materials such as concrete, rug, marble, or leather on the basis of their textural appearance. Such natural textures arise from spatial variation of two surface attributes: (1) reflectance and (2) surface normal. In this paper, we provide a unified model to address both these aspects of natural texture. The main idea is to construct a vocabulary of prototype tiny surface patches with associated local geometric and photometric properties. We call these 3D textons. Examples might be ridges, grooves, spots or stripes or combinations thereof. Associated with each texton is an appearance vector, which characterizes the local irradiance distribution, represented as a set of linear Gaussian derivative filter outputs, under different lighting and viewing conditions.Given a large collection of images of different materials, a clustering approach is used to acquire a small (on the order of 100) 3D texton vocabulary. Given a few (1 to 4) images of any material, it can be characterized using these textons. We demonstrate the application of this representation for recognition of the material viewed under novel lighting and viewing conditions. We also illustrate how the 3D texton model can be used to predict the appearance of materials under novel conditions.     

Experiments in segmenting texton patterns using localized spatial filters

In this paper we apply a previously developed model of texture segmentation using multiple spatially and spectrally localized filters, known as Gabor filters, to the analysis of textures composed of elementary image features known as textons. It is found that for regularly- or irregularly-spaced texton patterns, the segmentation approach works well, in the sense that it is in accordance with visual segmentation. Differences in texton spacing, size, orientation, and phase are all found to lead to successful segmentations.     

A statistical distribution texton feature for synthetic aperture radar image classification

We propose a novel statistical distribution texton (s-texton) feature for synthetic aperture radar (SAR) image classification. Motivated by the traditional texton feature, the framework of texture analysis, and the importance of statistical distribution in SAR images, the s-texton feature is developed based on the idea that parameter estimation of the statistical distribution can replace the filtering operation in the traditional texture analysis of SAR images. In the process of extracting the s-texton feature, several strategies are adopted, including pre-processing, spatial gridding, parameter estimation, texton clustering, and histogram statistics. Experimental results on TerraSAR data demonstrate the effectiveness of the proposed s-texton feature.     

Supervised image segmentation using Q-Shift Dual-Tree Complex Wavelet Transform coefficients with a texton approach

In this study, we propose a simple and efficient texture-based algorithm for image segmentation. This method constitutes computing textons and bag of words (BOWs) learned by support vector machine (SVM) classifiers. Textons are composed of local magnitude coefficients that arise from the Q-Shift Dual-Tree Complex Wavelet Transform (DT-CWT) combined with color components. In keeping with the needs of our research context, which addresses land cover mapping from remote images, we use a few small texture patches at the training stage, where other supervised methods usually train fully representative textures. We accounted for the scale and rotation invariance issue of the textons, and three different invariance transforms were evaluated on DT-CWT-based features. The largest contribution of this study is the comparison of three classification schemes in the segmentation algorithm. Specifically, we designed a new scheme that was especially competitive and that uses several classifiers, with each classifier adapted to a specific size of analysis window in texton quantification and trained on a reduced data set by random selection. This configuration allows quick SVM convergence and an easy parallelization of the SVM-bank while maintaining a high segmentation accuracy. We compare classification results with textons made using the well-known maximum response filters bank and speed up robust features features as references. We show that DT-CWT textons provide better distinguishing features in the entire set of configurations tested. Benchmarks of our different method configurations were made over two substantial textured mosaic sets, each composed of 100 grey or color mosaics made up of Brodatz or VisTex textures. Lastly, when applied to remote sensing images, our method yields good region segmentation compared to the ENVI commercial software, which demonstrates that the method could be used to generate land cover maps and is suitable for various purposes in image segmentation.