基于纹理方向的描述和对具有方向性纹理图象的分类与分割

为进一步进行纹理特征分析，从纹理的方向性入手，给出了纹理方向的数学定义式，合理选择差异函数，构造了具有物理意义的纹理方向描述特征向量，数据处理方面，运用模糊贴近度的概念，结合改进后的属性均值聚类算法，对一类具有方向性的纹理图象进行分类与分割实验，取得了较好的结果，试验表明，该方法对纹理的方向性有很好的描述能力。     

Texture segmentation using wavelet transform

Texture analysis such as segmentation and classification plays a vital role in computer vision and pattern recognition and is widely applied to many areas such as industrial automation, bio-medical image processing and remote sensing. This paper describes a novel technique of feature extraction for characterization and segmentation of texture at multiple scales based on block by block comparison of wavelet co-occurrence features. The performance of this segmentation algorithm is superior to traditional single resolution techniques such as texture spectrum, co-occurrences, local linear transforms, etc. The results of the proposed algorithm are found to be satisfactory.     

一种基于纹理模式的汽车牌照定位方法

针对车牌区域内字符串具有丰富的纹理特征,采用纹理模式分析方法,充分利用这些纹理特征,突出车牌区域,削弱伪车牌区域。提出了一种基于纹理模式的车牌定位方法。试验结果表明,此方法能较准确地定位车牌,便于进一步的车牌字符分割。     

傅氏变换的自配准性质及其在纹理识别和图象分割中的应用

图象按纹理一致性进行辨识和分割是图象分析中的一个重要问题 ,有着广泛的实际应用 .讨论了傅氏变换应用于纹理识别的机理 ,并基于此提出了一种图象分割算法 .图象中的纹理线条呈现出很多方向 ,并随机地分布在图象的各个位置 ,然而对于它的傅氏变换幅度谱来说 ,相同方向的线条无论其位置如何 ,它们的贡献会被叠加在一起 ,集中地反映在通过频谱中心垂直于原线条方向的条带上 .这一现象被称为傅氏变换幅度谱的自配准性质 .首先对这一性质进行实验个例的研究和理论分析 ,然后设计算法将其应用于图象的纹理辨识和基于纹理的图象分割实验 ,取得了较为满意的效果 .实验证明 ,得益于自配准性质 ,傅氏变换方法不失为一种有潜力的纹理分析和图象分割方法 ,值得进一步扩展更多的图象应用领域     

Texture segmentation using hierarchical wavelet decomposition

This paper presents a texture segmentation algorithm based on a hierarchical wavelet decomposition. Using Daubechies four-tap filter, an original image is decomposed into three detail images and one approximate image. The decomposition can be recursively applied to the approximate image to generate a lower resolution of the pyramid. The segmentation starts at the lowest resolution using the K-means clustering scheme and textural features obtained from various sub-bands. The result of segmentation is propagated through the pyramid to a higher resolution with continuously improving the segmentation. The lower resolution levels help to build the contour of the segmented texture, while higher levels refine the process, and correct possible errors.     

Texture analysis of aerial photographs

Different image textures manifest themselves by dissimilarity in both the property values and the spatial interrelationships of their component texture primitives. We use this fact in a texture discrimination system.An image is first segmented into closed regions called units. Then, a set of properties is calculated for each of the units. The units along with their respective properties constitute the primitives.The discrimination between texture categories has two parts: the training phase and the classification phase. The primitives and the relationships which are obtained from representative training images are used to develop criteria for the classification phase. During classification, the primitives of the image under test are first used to assign a unit to one of several cluster types. Then, each primitive is assigned to the most likely texture class given its cluster type and the cluster types of its spatially adjacent neighbors.The method is used on three images: a noisy checkerboard, a simulated texture and an aerial photograph.     

Texture recognition via autoregression

This article considers the image processing problem of texture recognition. It is shown that a chi-square test based upon a two-dimensional autoregressive model can be derived and can be used to test for differences between certain types of micro-textures. The chi-square test cannot be used with macro-textures, and another autoregressive-based distance measure is derived which is more suitable for these cases. It is shown experimentally that this distance measure affords a reliable means of classifying a broad class of micro- and macro-textures using a nearest-neighbour type of approach.     

基于特征尺度及多尺度分解的纹理分割

在计算机视觉领域，纹理识别及分割是图像低层次处理中一个重要的问题。文章依据视觉信息处理的生理学理论，提出了一种新的纹理分析算法。该算法通过搜寻纹理图像的特征尺度并配合连续整数尺度滤波上的特征尺度统计对纹理进行分层分析。并基于特征尺度上的分层分析提出一种纹理分割算法。实验结果证明了该算法的有效性。     

一种基于纹理的快速车辆牌照分割方法

主要针对车辆牌照自动识别系统中的一个重要功能块——车牌分割，提出了一种基于车牌字符串纹理特性的车牌快速定位方法。试验结果表明，此方法快速地搜索车牌并准确定位，便于下一步的字符分割和识别。     

Tree pattern mining with tree automata constraints

Most work on pattern mining focuses on simple data structures such as itemsets and sequences of itemsets. However, a lot of recent applications dealing with complex data like chemical compounds, protein structures, XML and Web log databases and social networks, require much more sophisticated data structures such as trees and graphs. In these contexts, interesting patterns involve not only frequent object values (labels) appearing in the graphs (or trees) but also frequent specific topologies found in these structures. Recently, several techniques for tree and graph mining have been proposed in the literature. In this paper, we focus on constraint-based tree pattern mining. We propose to use tree automata as a mechanism to specify user constraints over tree patterns. We present the algorithm CoBMiner which allows user constraints specified by a tree automata to be incorporated in the mining process. An extensive set of experiments executed over synthetic and real data (XML documents and Web usage logs) allows us to conclude that incorporating constraints during the mining process is far more effective than filtering the interesting patterns after the mining process.     

基于卷缩轮信息的虹膜识别

本文利用图像纹理元的灰度模式及统计特征完成卷缩轮的分割,并在卷缩轮分割的基础上实现对虹膜的识别;同时,运用该方法进行了实验,实验效果很好。     

利用小变换和特征加权进行纹理分割

为了提高纹理图象分割的边缘准确性和区域一致性以及降低分割错误率,提出了一种基于小波变换的利用特征加权来进行纹理分割的方法。该方法包括特征提取、预分割和后分割3个阶段,其中,特征提取在金字塔结构小小以变换的基础上进行;预分割利用均人矣类算法来对原始图象进行初步的分割;后分割则根据预分割的结果对特征进行加权,然后利用最小距离分类器来实现图象的最后分割。与传统的方法相比,该方法在分割错误率、边缘准确性以及区域一致性等方面均有明显的改善。     

An algorithm for the inference of tree grammars

An algorithm for the inference of tree grammars from sample trees is presented. The procedure, which is based on the properties of self-embedding and regularity, produces a reduced tree grammar capable of generating all the samples used in the inference process as well as other trees similar in structure. The characteristics of the algorithm are illustrated by experimental results.Work supported by the Office of Naval Research, Arlington, Virginia, under contract N00014-71-A-0121-0005.     

基于纹理方向整体分布特征的台风自动识别方法

为实现卫星云图上台风的自动识别，提出了一种基于纹理方向整体分布特征的台风云系图象自动识别方法，通过引入矢量矩的概念来表现图象纹理整体分布规律，该识别方法采用全局搜索方式，将一窗口在整幅图象上滑动，首先计算出窗口图象内各点的纹理方向，进而得出窗口图像的矢量矩，将矢量矩与阈值比较来判整幅图象是否为台风云系图像，实验结果表明，该方法能够识别不同类型和不同发展阶段的台风云系图象，能够很好地将台风云系与其他干扰云系区分开，具有较广泛的适应性和较高的识别率。     

基于非乘积型小波的纹理分析

提出了一种基于非乘积型小波的纹理分析方法：首先利用非乘积型小波分解原始图象，然后对高频部分的子图分别进行像素能量计算、纹理滤波以及直方图分割，从而得到粗分类的结果图；再把所有子图合并，并进行适当的后处理；最后利用小波的多尺度特性，决定分析是否终止，从而得到纹理分析结果。本方法是一种无监督分类，精确度高且计算复杂度低。     

基于树状小波分解的纹理图象检索

针对图象检索应具有简单、快速、有效等要求,提出了一种采用树状小波分解特征的纹理图象检索方法,该方法可以在相应的能量准则下,自适应地对图象进行了带分解,同时可利用小波函数分解的多分辨率与多方向特性,来形成能够在一定程度上对图象进行精确描述的特征矢量;在此基础上,又采用基于图象特征值的主分量分析方法,有效降低了特征矢量的维数;另外,基于用户需求的分层检索,还满足了用户不同层次的需求。实验结果表明,该算法快速,有效,具有较强的应用价值。     

Textural filters based on the texture spectrum

Conventional digital filtering techniques, based on classical Fourier analysis (that is, low-pass, high-pass and band-pass), are widely used in digital image processing. Unsatisfaction may be encountered when applying these filters to texture analysis of images, where one needs some specific spatial filters which are able to transform an image in the sense of texture rather than the spectral properties. Such textural filters can be designed in the texture spectrum domain and they are of interest for texture analysis. An example is given in this paper, and has been applied to four of Brodatz's natural images. The result shows a promising potential of the texture spectrum for designing textural filters.     

利用小波变换和FCM算法进行多特征纹理分割

提出了一种基于小波变换和模糊C均值（FCM）算法的两阶段纹理分割方法,各阶段采用不同的纹理特征。而且该特征结合了像素的空域和频域信息。实验表明该方法在分割错误率、边缘准确性及区域一致性方面均有较明显的改善,同时也有较高的分割效率。     

基于纹理特征的汉字字体识别研究

介绍了字体识别的重要性和有待解决的问题，提出了一种利用Gabor滤波器提取版面纹理特征进行字体识别的方法，着重介绍了滤液器设计、纹理特征提取和字体识别的过程。这种方法是与内容无关的，不需要局部微细特征分析，可以解决实际版面样弱印刷质量差、变形多的问题。用于常见字体的识别，取得了较好的效果。