基于纹理特征的自然图像鉴别方法*

采用图像的纹理特性建立模型,实现了自然图像和人工图像（主要是简笔画、表格、函数图像）的鉴别。首先,计算两类图像的纹理特征,统计并分析数据找出规律;其次,选择图像的平滑度、一致性和图像熵作为代表性的纹理特征,组建数学模型;最后,利用三个特征的线性组合构成鉴别函数,并用模拟退火算法确定权值。实验表明,该方法可以很好地鉴别两类图像,识别效率较高。     

Texture discrimination based upon an assumed stochastic texture model

A new approach to texture discrimination is described. This approach is based upon an assumed stochastic model for texture in imagery and is an approximation to the statistically optimum maximum likelihood classifier. The construction and properties of the stochastic texture model are described and a digital filtering implementation of the resulting maximum likelihood texture discriminant is provided. The efficacy of this approach is demonstrated through experimental results obtained with simulated texture data. A comparison is provided with more conventional texture discriminants under identical conditions. The implications to texture discrimination in realworld imagery are discussed.     

基于方向导数纹理描述子的纹理图像聚类

在深入分析CS—LBP、ICS—LBP、FCS—LBP、ECS—LBP的基础上,提出了一种4维的快速纹理描述子。该描述子计算4个方向的方向导数,在维数由CS—LBP、ICS—LBP、FCS—LBP的16维降到了4维的同时,较CS—LBP、ICS—LBP、FCS—LBP、ECS—LBP具有更好的区分能力,对纹理图像的聚类准确度更高,描述子计算时间平均在CS—LBP、ICS—LBP、FCS—LBP、ECS—LBP的以内。     

Fractional partial differential equation denoising models for texture image

In this paper,a set of fractional partial differential equations based on fractional total variation and fractional steepest descent approach are proposed to address the problem of traditional drawbacks of PM and ROF multi-scale denoising for texture image.By extending Green,Gauss,Stokes and Euler-Lagrange formulas to fractional field,we can find that the integer formulas are just their special case of fractional ones.In order to improve the denoising capability,we proposed 4 fractional partial differential equation based multiscale denoising models,and then discussed their stabilities and convergence rate.Theoretic deduction and experimental evaluation demonstrate the stability and astringency of fractional steepest descent approach,and fractional nonlinearly multi-scale denoising capability and best value of parameters are discussed also.The experiments results prove that the ability for preserving high-frequency edge and complex texture information of the proposed denoising models are obviously superior to traditional integral based algorithms,especially for texture detail rich images.     

Learning texture discrimination masks

A neural network texture classification method is proposed in this paper. The approach is introduced as a generalization of the multichannel filtering method. Instead of using a general filter bank, a neural network is trained to find a minimal set of specific filters, so that both the feature extraction and classification tasks are performed by the same unified network. The authors compute the error rates for different network parameters, and show the convergence speed of training and node pruning algorithms. The proposed method is demonstrated in several texture classification experiments. It is successfully applied in the tasks of locating barcodes in the images and segmenting a printed page into text, graphics, and background. Compared with the traditional multichannel filtering method, the neural network approach allows one to perform the same texture classification or segmentation task more efficiently. Extensions of the method, as well as its limitations, are discussed in the paper     

A random walk procedure for texture discrimination

We consider the problem of texture discrimination. Random walks are performed in a plain domain D bounded by an absorbing boundary ? and the absorption distribution is calculated. Measurements derived from such distributions are the features used for discrimination. Both problems of texture discrimination and edge segment detection can be solved using the same random walk approach. The border distributions and their differences with respect to a homogeneous image can classify two different images as having similar or dissimilar textures. The existence of an edge segment is concluded if the boundary distribution for a given window (subimage) differs significantly from the boundary distribution for a homogeneous (uniform grey level) window. The random walk procedure has been implemented and results of texture discrimination are shown. A comparison is made between results obtained using the random walk approach and the first-or second-order statistics, respectively. The random walk procedure is intended mainly for the texture discrimination problem, and its possible application to the edge detection problem (as shown in this paper) is just a by-product.     

专业级的纹理制作软件Infinity Texture

这一段时期,笔者一直在从事虚拟现实以及游戏场景的开发制作。在贴图纹理的处理上,应用于网络的虚拟现实技术,像Vrml三维场景要求贴图尽可能的小,以减少浏览下载的时间。一个重要的技巧就是利用四方连续的图片纹理,比如,一面高楼的整个贴图,当然不会是整张的图片,而仅是一张小门窗的四方连续图片,一伸展开,就装扮了无论多么高大的建筑物,大大缩小了     

Model-based texture measures

Many measures have been developed to quantify the structural properties of image texture. This correspondence explores their applicability to textures generated by several standard models.     

A study of Gaussian mixture models of color and texture features for image classification and segmentation

The aims of this paper are two-fold: to define Gaussian mixture models (GMMs) of colored texture on several feature spaces and to compare the performance of these models in various classification tasks, both with each other and with other models popular in the literature. We construct GMMs over a variety of different color and texture feature spaces, with a view to the retrieval of textured color images from databases. We compare supervised classification results for different choices of color and texture features using the Vistex database, and explore the best set of features and the best GMM configuration for this task. In addition we introduce several methods for combining the ‘color’ and ‘structure’ information in order to improve the classification performances. We then apply the resulting models to the classification of texture databases and to the classification of man-made and natural areas in aerial images. We compare the GMM model with other models in the literature, and show an overall improvement in performance.     

Capsule endoscopy image analysis using texture information from various colour models

Wireless capsule endoscopy (WCE) is a novel imaging technique that is gradually gaining ground as it enables the non-invasive and efficacious visualization of the digestive track, and especially the entire small bowel including its middle part. However, the task of reviewing the vast amount of images produced by a WCE examination is a burden for the physicians. To tackle this major drawback, an innovative scheme for discriminating endoscopic images related to one of the most common intestinal diseases, ulceration, is presented here. This new approach focuses on colour-texture features in order to investigate how the structure information of healthy and abnormal tissue is distributed on RGB, HSV and CIE Lab colour spaces. The WCE images are pre-processed using bidimensional ensemble empirical mode decomposition so as to facilitate differential lacunarity analysis to extract the texture patterns of normal and ulcerous regions. Experimental results demonstrated promising classification performance (mean accuracy>95%), exhibiting a high potential towards automatic WCE image analysis.     

Texture measures for carpet wear assessment

Two standard approaches for texture analysis make use of numerical features of the second-order gray-level statistics, and on first-order statistics of gray-level differences, respectively. Feature sets of these types, all designed analogously, were used to analyze four sets of carpet samples exposed to different degrees of wear. It was found that some of the features extracted from the spatial gray-level-dependence matrix, neighboring gray-level-dependence matrix, gray-level difference method, and the gray-level run-length method allowed discrimination of degrees of wear in wool carpet. The methods developed could be of use in quality control     

An evolutionary patch pattern approach for texture discrimination

A new evolutionary approach is presented, based on implicit pattern–process relationships. For implementing this approach, any gray level texture image is decomposed into a progressive sequence of binary patch patterns that describe a process of change from background to foreground domination. Each of the binary patterns throughout these sequences is parameterized, using several metrics that describe, for example, its fragmentation level, both for the background (e.g., white) and foreground (e.g., black) patch patterns. Any texture type is then assumed to have a unique evolutionary path represented by a distinctive region in the feature space of metrics characterizing these patterns and their change. Application of hierarchical clustering based on a few (3 or 4) metrics representing characteristic stages in the patterns’ change process allowed us to accurately discriminate between 50 samples of 10 Brodatz texture types.     

Texture discrimination by projective invariants

We investigate the use of projective invariants for discriminating textured surfaces under projective transformation. A theorem is presented which shows that statistics of cross ratios of groups of four collinear points are a projective invariant. The distance between normalized vectors is used as the similarity measure for texture discrimination.     

Scale-orientation histogram for texture image retrieval

In this paper, a Scale-orientation histogram is defined for analyzing the “directionality” and “periodicity”, which are two of the most important deterministic dimensions in human texture perception. This histogram is applied to texture retrieval in a case study, and the experimental results illustrate its effectiveness.     

一种织物彩色图像的分割算法

提出了一种针对织物彩色图像的分割算法。该算法在颜色聚类的基础上,把每种颜色用 不同的序号来表示,生成一个与原图同大小的序号矩阵,然后根据矩阵中序号的分布进行初始分割并 产生种子区域,再把初始分割未能确定的像素指派到种子区域中,最后根据颜色信息合并过分割的区 域,并对区域边缘进行平滑,得到最终的分割结果。实验表明,该算法能较有效地实现各类彩色织物 图像的分割。     

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     

Spectral texture for improved class discrimination in complex terrain

Abstract

A spatial co-occurrence algorithm has been used to derive image texture from Landsat Multispectral Scanner (MSS) data to increase classification accuracy in a moderate relief, boreal environment in eastern Canada. The aim was to investigate ‘data-driven improvements’, including those available through digital elevation modelling. Overall classification accuracy using MSS data alone was 59·1 per cent when compared to a biophysical inventory of the area compiled primarily by aerial photointerpretation. This increased to 66·2 per cent with MSS plus texture and to 89·8 per cent when MSS data were analysed with geomorphometry extracted from a digital elevation model (DEM). The introduction of MSS texture resulted in statistically significant increases in individual class accuracies in classes that were also well defined using the geomorphometric and integrated data sets. This suggested that some of the additional information provided by geomorphometry was also contained in spectral texture. It was also noted that individual texture orientations resulted in higher class accuracies than average texture measures; this is probably related to structural (slope/aspect) characteristics of specific vegetation communities.     

MaZda--a software package for image texture analysis

MaZda, a software package for 2D and 3D image texture analysis is presented. It provides a complete path for quantitative analysis of image textures, including computation of texture features, procedures for feature selection and extraction, algorithms for data classification, various data visualization and image segmentation tools. Initially, MaZda was aimed at analysis of magnetic resonance image textures. However, it revealed its effectiveness in analysis of other types of textured images, including X-ray and camera images. The software was utilized by numerous researchers in diverse applications. It was proven to be an efficient and reliable tool for quantitative image analysis, even in more accurate and objective medical diagnosis. MaZda was also successfully used in food industry to assess food product quality. MaZda can be downloaded for public use from the Institute of Electronics, Technical University of Lodz webpage.     

卡通纹理分解和全变分梯度算法实现图像恢复

为了研究图像恢复技术,提出采用卡通纹理分解和基于全变分的广义加速临近梯度算法实现图像恢复。将原始模糊图像分解成卡通部分和纹理部分,卡通部分主要是图像的低频成分受噪声干扰小,纹理部分主要是图像的高频成分受噪声干扰大,采用基于全变分的广义加速临近梯度算法进行图像去模糊和去噪,卡通部分选择较小的正则化参数,纹理部分选择较大的正则化参数,将恢复的卡通部分和纹理部分进行合成得到恢复图像。通过对两张标准测试图像的MATLAB实验仿真,证明了该方法不仅收敛速度快而且效果比一般的临近梯度算法要好,尤其适合于恢复模糊度不是很高的图像。