Texture description and segmentation through fractal geometry

Fractal geometry is receiving increased attention as a model for natural phenomena. In this paper we first present a new method for estimating the fractal dimension from image surfaces and show that it performs better at describing and segmenting generated fractal sets. Since the fractal dimension alone is not sufficient to characterize natural textures, we define a new class of texture measures based on the concept of lacunarity and use them, together with the fractal dimension, to describe and segment natural texture images.     

Rock unit discrimination on Landsat TM,SIR-C and Radarsat images using spectral and textural information

The paper presents results for spectral and textural analysis of the rock units in Landsat Thematic Mapper (TM) images, dual-band (L and C) and dual-polarization (HH and HV) Shuttle Imaging Radar (SIR)-C images, and C-band HH polarization Standard Beam 4 and Extended High Incidence Beam 3 Radarsat images from a study area between California and Arizona, USA. Fractal dimension, lacunarity and grey-level co-occurrence matrix (GLCM) textural feature images were created from the SIR-C and Radarsat images. Fractal dimensions were calculated using a differential box counting method and lacunarity measures were obtained using a new grey-scale lacunarity estimation method for 36 sample images extracted from the SIR-C and Radarsat images. The fractal dimension and lacunarity curves and class signature separability analysis show that, for rock unit discrimination using image textural features in the study area, the SIR-C L-HH image is more suitable than other SIR-C images and Radarsat images, and that co-polarization (HH) generally provides more textural information than cross-polarization (HV) in the study area. The study also shows that lacunarity measures can reveal the scaling properties of radar image textures for rock units. The combination of spectral information from Landsat TM images and textural information from radar images improves the image classification accuracy of rock units in the study area.     

Performance analysis of textural features for characterization and classification of SAR images

A new method has been presented to compare the performance of textural features for characterization and classification of SAR (Synthetic Aperture Radar) images. In contrast to the conventional comparative studies based on classification accuracy, this method emphasizes the sensitivity of texture measures for grey level transformation and multiplicative noise of different speckle levels. Texture features based on grey level run length, texture spectrum, power spectrum, fractal dimension and co-occurrence have been considered. A number of image samples of built-up, barren land, orchard and sand regions were considered for the study. The interpretation of the results is expected to provide useful information for the remote sensing community, which employs textural features for segmentation and classification of satellite images.     

A Simplified Gravitational Model for Texture Analysis

Textures are among the most important features in the field of image analysis. This paper presents an innovative methodology to extract information from them, converting an image into a simplified dynamical system in gravitational collapse process whose collapsing states are described by using the lacunarity method. The paper compares the proposed approach to other classical methods using Brodatz textures and a second texture database as benchmark. The best classification results using the standard parameters of the method were 97.00 % and 54.10 % of success rate (percentage of samples correctly classified) for both databases, respectively. These results prove that the presented approach is an efficient tool for texture analysis.     

近景数码影像中墙面纹理自动拼接方法的研究

影像拼接是从近景数码影像中生成墙面纹理需要解决的问题。然而由于现有的拼接软件通常不适用于处理几何变形大的近景影像,但通过对从近景数码影像中自动拼接墙面纹理的方法进行的研究发现,在相机的位置离建筑物相对较远,且大致沿一条直线进行拍摄时,当原始影像预先纠正到同一个铅垂面上后,由于各影像之间的大小比例接近于1,因此对于此种情况的拼接而言,就只需要通过选取拼接点来确定相邻影像之间沿X轴和Y轴方向的平移量,即可实现拼接。然而对于比较狭窄的街道(如步行街)来说,则由于摄站与建筑物之间的距离比较小,使影像的倾角增大,导致影像的几何变形增大,所以不适于采用选取拼接点的方法进行拼接。针对此种情况的拼接,提出了一种类似于航带法生成正射影像的条带法,用来进行墙面纹理的自动拼接,并且结合影像间同名水平直线的约束和加密匹配同名点两种方法来对拼接结果进行优化。最后针对地面实拍的序列影像进行了实验。实验结果证明,该方法能够显著消除相邻影像间的几何差异,从而真正实现了影像的无缝拼接。     

基于曲线的纹理映射

本文提出一种纹理映射的新方法。二维平面中的一条曲线与三维曲面上的一条曲线相对应，两条曲线上的点再相对应。当对应曲线足够多时，就可获理完成纹理映射所需的足够信息。     

一种去除运动目标重影的图像镶嵌方法研究

针对图像镶嵌过程中如果图像序列中存在运动目标就会引起重影的问题,文中提出了一种新的去除运动目标重影的图像镶嵌方法。算法首先对视频图像进行了运动分割,在图像匹配阶段采用边缘特征点进行匹配,并由马尔可夫随机场模型生成运动目标的二值模板,剔除掉运动目标二值模板上的边缘点,从而保证图像匹配的准确率。在镶嵌阶段使用活动轮廓模型生成一条最优镶嵌线,产生的镶嵌线充分考虑了图像的边缘特征和梯度信息,确保了镶嵌后图像两边纹理差异更小。从连续视频帧中选取多帧图像进行实际的图像镶嵌,实验结果表明文中算法取得了较好的效果。     

反正切色调分化模型的铅笔画绘制

目的 在现阶段采用计算机模拟绘制真实铅笔画往往不能够逼近真实效果,主要原因之一是没有考虑到真实铅笔画中的色调较灰度图像有着更明显的暗灰亮层次变化的特性,基于这一问题提出一种新的铅笔画自动生成算法。方法 一方面在外轮廓提取中采取了LAB空间色差图与纹理图结合的方法,取代了传统基于梯度图的方法;另一方面保留了纹理色调连续变化的特点,且为了体现色调的分层效果提出了反正切色调分化模型,其核心思想是基于图像暗灰亮图层的像素比例来自适应地调整像素灰度值以达到色调分层的目的。结果 本文算法能够避开噪点的影响更好地展示细节,同时解决了直接分层结果中色调变化突然的缺陷,实现了亮度分层与色调连续变化的结合。结论 新算法在最终效果上较其他算法的优势体现在两方面,一是轮廓线的连续性更好,对比度更强,其次是纹理的效果能够同时兼顾连续性和分层,这是其他算法做不到的,本文算法适用于所有不同分辨率的彩色或灰度图像,且分辨率越高效果越好。     

分维和孔隙度在肝癌超声纹理识别中协同作用

对分维和孔隙度及其组合因子表征超声肝癌图像纹理特征的性能进行了对比研究。以正常肝和肝癌各14幅超声图像为样本,用4种分维和一种孔隙度方法计算分维和孔隙度值。用ROC进行评估,单因子的傅里叶功率谱分维和盒柱平均值孔隙度值有较大的ROC曲线下面积。用SVM对单因子和组合因子进行训练和检验表明,傅里叶功率谱分维与盒柱平均值孔隙度（FPS+LBCM）（4个核）和差分盒计数分维与盒柱平均值孔隙度（DBC+LBCM）（除SIGMOID外）构成的组合因子有比单因子较高的分类准确率。     

Texture classification using fuzzy uncertainty texture spectrum

A new method using fuzzy uncertainty, which measures the uncertainty of the uniform surface in an image, is proposed for texture analysis. A grey-scale image can be transformed into a fuzzy image by the uncertainty definition. The distribution of the membership in a measured fuzzy image, denoted by the fuzzy uncertainty texture spectrum (FUTS), is used as the texture feature for texture analysis. To evaluate the performance of the proposed method. supervised texture classification and rotated texture classification are applied. Experimental results reveal high-accuracy classification rates and show that the proposed method is a good tool for texture analysis.     

SAR图像机动目标的多尺度间隙度特征及其鉴别处理算法

提出了一种新的基于多尺度间隙度特征的高分辨率SAR图像机动目标鉴别算法,用以去除检测阶段的自然杂波虚警。给出了间隙度特征的物理概念,以及二值化图像和SAR灰度图像的间隙度计算方法,并基于多尺度间隙度特征实现SAR图像车辆目标鉴别处理。最后,利用MSTAR数据库中的车辆目标和自然地物数据验证了该算法,结果显示该特征具有较好的鉴别性能。     

基于毯子维和缺项的虹膜特征提取算法

虹膜特征提取是虹膜识别的一个关键环节。文中提出一种毯子维和缺项相结合的虹膜特征提取算法。利用虹膜纹理的自相似和丰富的变化细节所具有的分形几何特性,采用纵向扩展毯子维表达不同分辨能力下虹膜纹理的变化及其辐射排列特点。通过引入缺项进一步提取不同纹理和分形表现却具有相同分形维数的虹膜特征。两者的结合能够更加全面地反映虹膜纹理的细腻变化。将归一化虹膜图像灰度值的毯子维及缺项用于虹膜分类,提高对虹膜的分类能力。针对CASIA-IrisV3-Interval数据库的仿真结果表明,纵向扩展毯子维结合缺项能够有效、快速提取虹膜的纹理信息,所获得的特征具有高的虹膜识别性能。     

拓扑图格独立分量分析和谱聚类支持的纹理探测

提出的纹理探测方法首先采用拓扑图格独立分量分析(TICA)分别对每个观测纹理进行学习,获得分离基．分离基的作用相当于滤波器,并通过最大响应准则得到选择．从不同纹理选择的滤波器构成一个滤波器集;为了计算探测点的纹理特征,测试图像被分解为滤波器通道。最后,探测点被视为谱图的顶点,根据谱聚类(SC)对谱图的切分结果,递归地分离出探测点．实验结果表明,这一方法行之有效。     

基于高分辨遥感数据的胡杨与柽柳树冠提取

胡杨、柽柳是干旱荒漠区生境的指示种,其树冠提取是荒漠生境遥感定量监测的基础。以塔里木河下游胡杨、柽柳为研究对象,基于QuickBird数据,使用光谱单数据源SVM、光谱结合纹理SVM、面向对象分类和最大似然分类法提取树冠。结果表明:1光谱结合纹理SVM比光谱单源SVM分类精度高9.65%,冠幅估测精度高7.18%,表明高分辨影像上纹理是提高分类精度的重要因素;2面向对象分类法精度最高,分类总体精度86.47%,较光谱单源SVM提高15.67%,较光谱结合纹理SVM提高6.02%,较最大似然法提高22.58%,其冠幅估测精度达87.45%。它兼顾面向对象影像分割与支持向量机方法优点,有效利用分割对象光谱、纹理和空间等信息,较好地解决了其他方法"同物异谱、异物同谱"造成提取树冠破碎的问题,使树冠提取具有较好的稳定性和较高精度。     

图像纹理特征相似性度量的研究

纹理是图像的重要属性,基于纹理特征检索图像是当前的研究热点,对图像的纹理进行相似性比较是进行图像检索的关键.根据纹理的特点,本文将通用的向量空间模型进行拓展,构建了一个针对簇集进行相似性匹配的模型-聚类空间模型,对图像纹理相似性进行度量,并据此实现了无需分割的多纹理图像检索.我们分别针对单纹理图像和自然图像库进进行了实验,获得的实验结果与人类视觉认知的结果一致.     

Lacunarity analysis of raster datasets and 1D, 2D,and 3D point patterns

Spatial scale plays an important role in many fields. As a scale-dependent measure for spatial heterogeneity, lacunarity describes the distribution of gaps within a set at multiple scales. In Earth science, environmental science, and ecology, lacunarity has been increasingly used for multiscale modeling of spatial patterns. This paper presents the development and implementation of a geographic information system (GIS) software extension for lacunarity analysis of raster datasets and 1D, 2D, and 3D point patterns. Depending on the application requirement, lacunarity analysis can be performed in two modes: global mode or local mode. The extension works for: (1) binary (1-bit) and grey-scale datasets in any raster format supported by ArcGIS and (2) 1D, 2D, and 3D point datasets as shapefiles or geodatabase feature classes. For more effective measurement of lacunarity for different patterns or processes in raster datasets, the extension allows users to define an area of interest (AOI) in four different ways, including using a polygon in an existing feature layer. Additionally, directionality can be taken into account when grey-scale datasets are used for local lacunarity analysis. The methodology and graphical user interface (GUI) are described. The application of the extension is demonstrated using both simulated and real datasets, including Brodatz texture images, a Spaceborne Imaging Radar (SIR-C) image, simulated 1D points on a drainage network, and 3D random and clustered point patterns. The options of lacunarity analysis and the effects of polyline arrangement on lacunarity of 1D points are also discussed. Results from sample data suggest that the lacunarity analysis extension can be used for efficient modeling of spatial patterns at multiple scales.     

Texture classification and segmentation using wavelet packet frame and Gaussian mixture model

In this paper, we propose a scheme for texture classification and segmentation. The methodology involves an extraction of texture features using the wavelet packet frame decomposition. This is followed by a Gaussian-mixture-based classifier which assigns each pixel to the class. Each subnet of the classifier is modeled by a Gaussian mixture model and each texture image is assigned to the class to which pixels of the image most belong. This scheme shows high recognition accuracy in the classification of Brodatz texture images. It can also be expanded to an unsupervised texture segmentation using a Kullback-Leibler divergence between two Gaussian mixtures. The proposed method was successfully applied to Brodatz mosaic image segmentation and fabric defect detection.     

基于平稳小波变换的图像融合算法

针对IHS（intensity,hue．saturation）融合算法的光谱畸变和正交小波变换融合算法的方块效应问题,提出了一种基于平稳小波变换的图像融合算法,并且探讨了在图像处理中应用灰色系统理论的可行性和有效性。该算法结合IHS变换,对于图像平稳小波分解的高频分量利用灰色关联分析准确、有效地检测出有用的边缘信息,并在确定融合权值时,给予不同的重视程度。实验表明,本文算法能够克服正交小波变换在图像融合中出现的方块效应问题,在保持光谱信息方面具有显著的优越性,并且能够有效提高融合图像的空间分辨能力,又由于新算法引入灰色关联分析进行像素分类,具有良好的抗噪性和可控边缘信息量等优点,使算法灵活、有效,进一步改善了融合图像的质量。     

Mosaicing of camera-captured document images

In this paper, we present a method for composing document mosaics from camera-captured images. We decompose the complexity of solving the 8-dof transformation between image pairs into two problems, that is, rectification and registration. This is achievable under a key assumption that sufficient text content forms orthogonal texture flows on the document surface. First, perspective distortion and rotation are removed from images using the texture flow information. Next, the translation and scaling are resolved by a Hough transform-like voting method. In the image composition part, our contribution is a sharpness based selection process which composes a seamless and blur free mosaic for text content. Experiments show that our approach can produce an accurate, sharp, and high resolution mosaic of a full document page from small image patches captured by a camera with various zooms and poses.     

基于灰度共生矩阵的织物纹理分析

以一种常见的织物纹理为对象,采用灰度共生矩阵的方法进行纹理分析.介绍了灰度共生矩阵的原理及其特征参数,讨论了纹理的灰度共生矩阵特征参数、像素距离以及图像灰度等级对灰度共生矩阵的影响,确定了区分此类正常织物与带疵点织物纹理的灰度共生矩阵构造方法.针对该类正常织物图像进行纹理分析,特征参数值统计,确定了正常织物纹理像素方向、像素距离以及图像灰度等级.取原始织物图像尺寸为128×128,生成灰度共生矩阵的最佳像素距离为2,经直方图均衡化后,最佳灰度等级为16.实验结果表明,按照该规则生成的6个灰度共生矩阵的特征参数,能够准确的判断此类织物图像是否存在疵点.