Shape from periodic texture using the eigenvectors of local affinedistortion

Shows how the local slant and tilt angles of regularly textured curved surfaces can be estimated directly, without the need for iterative numerical optimization. We work in the frequency domain and measure texture distortion using the affine distortion of the pattern of spectral peaks. The key theoretical contribution is to show that the directions of the eigenvectors of the affine distortion matrices can be used to estimate local slant and tilt angles of tangent planes to curved surfaces. In particular, the leading eigenvector points in the tilt direction. Although not as geometrically transparent, the direction of the second eigenvector can be used to estimate the slant direction. The required affine distortion matrices are computed using the correspondences between spectral peaks, established on the basis of their energy ordering. We apply the method to a variety of real-world and synthetic imagery     

基于随机纹理分析的越野环境地形识别

针对越野环境下阻碍自主车辆行驶的斜坡类危险地形特点,利用纹理分析法恢复地表面朝向信息。用径向竞争法提取能够反映随机纹理基元尺寸的信息;然后根据等尺寸纹理分布中心在像平面中位置,采用层次聚类法线性拟合地表面转角,并采用加权欧式距离和法计算梯度值,通过标定实验获知地表面倾角。实验表明,该算法能有效地测算地表面转角与倾角,测算误差与人类视觉估计误差相近,且受光照影响小。     

基于无抽样小波变换和MCE训练的纹理分类

提出了一种新的纹理分类的方法,该方法把基于无抽样小波变换的特征提取器和基于欧几里得距离的分类器进行了合并。把方差、偏态系数、峰态系数、三者的联合及谱直方图作为描述纹理图像不相重叠的图像窗的特征。一个使用线性转换矩阵的特征提取器对分类导向的特征做进一步的提取。利用基于欧几里得距离的分类器,每个纹理图像不相重叠的图像窗被确定到属于它的那一类。基于最小分类错误训练方法的特征提取器和分类器设计的合并使分类错误达到了最小化。使用该方法对25类BrodTex纹理图像进行了评估,分类精确度达到90%以上。     

多级小波分解与LBP融合的纹理提取算法

为提高不同光照、不同角度条件下的纹理识别精度,提出了一种利用多级小波分解和多尺度旋转不变LBP融合的纹理提取算法。算法在传统的LBP特征提取基础上,采用多尺度的旋转不变LBP算子分别对多级小波逼近图像提取直方图序列特征向量,与各级小波能量进行加权融合,获取更多的纹理信息,对光照和角度的变化有更高的鲁棒性。仿真结果表明,相对传统的LBP特征提取算法,改进的算法具有更高的纹理识别率。     

Bayesian texture classification and retrieval based on multiscale feature vector

This paper proposes a supervised multiscale Bayesian texture classifier. The classifier exploits the dual-tree complex wavelet transform (DT-CWT) to obtain complex-valued multiscale representations of training texture samples for each texture class. The high-pass subbands of DT-CWT decomposition of a texture image are used to form a multiscale feature vector representing magnitude and phase features. For computational efficiency, the dimensionality of feature vectors is reduced using principal component analysis (PCA). The class conditional probability density function of low-dimensional feature vectors for each texture class is then estimated by using Parzen-window estimate with identical Gaussian kernels and is used to represent the texture class. A query texture image is classified as the corresponding texture class with the highest a posteriori probability according to a Bayesian inferencing. The superior performance and robustness of the proposed classifier is demonstrated for classifying texture images from image databases. The proposed multiscale texture feature vector extracted from both magnitude and phase of DT-CWT subbands of a query image is also shown to be effective for texture retrieval.     

基于内容的条件类熵约束软竞争学习图像分类器

本文提出了一种新型的、以纹理粗糙度二阶矩度量作为图像特征的、以在最优条件类熵基础上的软竞争自组织特征映射为分类器的图像自动分类方法。实验结果表明,该种分 类器能很好地实现对纹理粗糙程度模式的无监督分类,其分类性能要明显好于传统的K-均值分类器。     

基于小波包变换的模糊判决纹理分类

提出一种基于小波包变换的模糊判决纹理分类方法,采用图象的完全树结构小波变换提取多分辨率纹理特征,模糊判决分类器通过引入隶属度函数对待征模糊化,反映了各类纹理样本间存在的差异及随机噪声等畸变因素赞成的抽取特征值存在的不确定性,提高了纹理分类算法对噪声或畸变的鲁棒性,通过实验获得了较满意的结果。     

稀疏纹理的特征提取和分类研究*

提出了对稀疏纹理表面特征的新描述方法,并进行了纹理分类的研究.以往对纹理的研究大多是对2D纹理的研究,一般是通过大量的训练样本来完成纹理特征的提取.通过RANSAC估计两幅纹理的单应约束,提取两幅纹理的对应点,不仅可以提取一般纹理的特征,而且可以提取包含立体信息的纹理特征(如因为光照和视点的变化引起立体纹理的阴影变化等),通过对应点的提取使得不再需要大量的训练样本来进行纹理的特征提取.实验表明该算法可以较准确和快速地进行纹理特征提取和分类,使得纹理分类工作变得有较强的可行性和实用性.     

基于四元数傅里叶梅林变换的旋转不变彩色纹理分类

提出一种基于四元数傅里叶梅林变换(Quaternion Fourier-Mellin Transform,QFMT)的旋转不变彩色纹理分类方法。该方法首先对彩色图像各分量图像进行对数极坐标变换,然后将经过变换后的3幅分量图像表示成四元数,并对其进行四元数傅里叶变换(Quaternion Fourier Transform,QFT),最后对幅度谱分别统计其环形特征量和楔形特征量作为纹理分类的特征向量,利用最近邻分类器进行分类。实验结果表明,本文提出的方法分类准确率更高,且具有良好的旋转不变纹理分析性能。     

基于贝叶斯网络模型的纹理分析及分类

用贝叶斯网络对纹理图像建模,并基于此模型给出了一种纹理分类的方法.把纹理图像在一个窗口内各个像素的灰度值看作贝叶斯网络的一次实现,通过训练得到各类纹理所对应贝叶斯网络的结构和参数,用纹理图像像素点在网络中的条件概率分布作为特征进行纹理分类.实验结果证明了该方法的有效性.     

基于改进决策树分类算法的遥感影像分类研究

针对现有分类器对遥感影像分类结果存不准确的问题,本文提出了一种基于决策树分类器的遥感影像分类方法,该方法以复合决策树Boost Tree思想为基础,首先利用分形理论中的毯模型提取遥感影像的纹理特征,根据遥感影像分类的特点,构造新的单棵决策树生成算法对遥感影像进行分类。以北京市五环内区域为研究区,使用landsat7 ETM数据源,实现了基于分形纹理特征、光谱特征的改进决策树分类。实验结果表明：通过毯模型提取的纹理特征可以很好地表达表面特征,辅以该纹理信息的改进决策树分类精度相比于只用光谱信息进行分类的精度有一定的提高,改善了分类效果。     

基于多种LBP特征集成学习的人脸识别*

单一的特征与分类器只能对限定条件下的人脸进行较好的识别,当在非限定条件下(如光照、背景等发生变化时)将出现人脸识别率较低问题,针对该问题,提出了一种基于多种局部二进制特征集成学习的人脸识别算法。首先,使用监督梯度下降法 (SDM)对人脸特征点定位,应用中心对称局部二进制(CSLBP)算子提取每个特征点邻域特征,将所有人脸特征点邻域特征合成为精细的纹理特征;同时运用分区LBP直方图算法提取人脸区域的微观空间结构特征;然后,使用K最近邻算法(KNN)和支持向量机(SVM)分别训练这两种特征,得到类别排序列表和投票决策矩阵;最后,利用加权求和的规则融合决策矩阵,构成最优集成分类器,从而得到输出类别。通过在非限制性人脸库LFW上实验结果表明,所提算法采用集成的方法明显优于单一的特征和分类器。     

Perspective-aware texture analysis and synthesis

This paper presents a novel texture synthesis scheme for anisotropic 2D textures based on perspective feature analysis and energy optimization. Given an example texture, the synthesis process starts with analyzing the texel (TEXture ELement) scale variations to obtain the perspective map (scale map). Feature mask and simple user-assisted scale extraction operations including slant and tilt angles assignment and scale value editing are applied. The scale map represents the global variations of the texel scales in the sample texture. Then, we extend 2D texture optimization techniques to synthesize these kinds of perspectively featured textures. The non-parametric texture optimization approach is integrated with histogram matching, which forces the global statics of the texel scale variations of the synthesized texture to match those of the example. We also demonstrate that our method is well-suited for image completion of a perspectively featured texture region in a digital photo.     

三维表面纹理高度重建中的光线不均校正

因三维表面纹理能比二维纹理更好地表现物体的纹理信息,而且随场景光照及视角的变化而变化,所以被广泛用于虚拟现实以及计算机游戏等技术之中。Photometric Stereo作为一种有效的获取三维表面纹理信息的技术而被人们所广泛关注。均匀的光照条件是Photometric Stereo捕获和重建三维表面纹理成功的关键条件。在现实应用中,不均匀光照会导致三维表面纹理在捕获和重建过程中发生失真和畸变。针对这种失真和畸变进行了研究,并提出了一种解决此类问题的方法。实验结果表明,该方法简单可行,有效。     

自适应的纹理图像光照不变特征提取方法

为降低光照变化对纹理图像的影响,提出自适应的纹理光照不变特征提取方法。利用小波变换提取对数域纹理图像的高、低频分量,并分别采用不同方法对两者进行处理,提取其光照不变量图像,运用主分量分析法得到光照不变量数据的特征,使用K-最近特征线分类器进行图像分类。实验结果表明,该方法在光照条件复杂的Outex14数据集上能够取得较好的分类效果,分类正确率高于现有方法5．56％--22．10％。     

Multiscale Bayesian texture segmentation using neural networks and Markov random fields

This paper presents a wavelet-based texture segmentation method using multilayer perceptron (MLP) networks and Markov random fields (MRF) in a multi-scale Bayesian framework. Inputs and outputs of MLP networks are constructed to estimate a posterior probability. The multi-scale features produced by multi-level wavelet decompositions of textured images are classified at each scale by maximum a posterior (MAP) classification and the posterior probabilities from MLP networks. An MRF model is used in order to model the prior distribution of each texture class, and a factor, which fuses the classification information through scales and acts as a guide for the labeling decision, is incorporated into the MAP classification of each scale. By fusing the multi-scale MAP classifications sequentially from coarse to fine scales, our proposed method gets the final and improved segmentation result at the finest scale. In this fusion process, the MRF model serves as the smoothness constraint and the Gibbs sampler acts as the MAP classifier. Our texture segmentation method was applied to segmentation of gray-level textured images. The proposed segmentation method shows better performance than texture segmentation using the hidden Markov trees (HMT) model and the HMTseg algorithm, which is a multi-scale Bayesian image segmentation algorithm.     

Application-independent feature selection for texture classification

Recent developments in texture classification have shown that the proper integration of texture methods from different families leads to significant improvements in terms of classification rate compared to the use of a single family of texture methods. In order to reduce the computational burden of that integration process, a selection stage is necessary. In general, a large number of feature selection techniques have been proposed. However, a specific texture feature selection must be typically applied given a particular set of texture patterns to be classified. This paper describes a new texture feature selection algorithm that is independent of specific classification problems/applications and thus must only be run once given a set of available texture methods. The proposed application-independent selection scheme has been evaluated and compared to previous proposals on both Brodatz compositions and complex real images.     

Automatic texture feature selection for image pixel classification

Pixel-based texture classifiers and segmenters are typically based on the combination of texture feature extraction methods that belong to a single family (e.g., Gabor filters). However, combining texture methods from different families has proven to produce better classification results both quantitatively and qualitatively. Given a set of multiple texture feature extraction methods from different families, this paper presents a new texture feature selection scheme that automatically determines a reduced subset of methods whose integration produces classification results comparable to those obtained when all the available methods are integrated, but with a significantly lower computational cost. Experiments with both Brodatz and real outdoor images show that the proposed selection scheme is more advantageous than well-known general purpose feature selection algorithms applied to the same problem.     

基于单幅建筑物图像的太阳光方位角恢复

在建筑规划和历史遗迹重建等领域，需要在重建的场景中添加、删除虚拟物体。能否获得图像拍摄时刻的光照信息，是决定最终合成图像的照片级真实感程度的重要因素之一，本文对基于单幅建筑物图像的光源方向检测技术进行了研究。把户外的太阳光看作为平行光，在建筑物坐标系中其方向可以用方位角和仰角表示。利用图像中建筑物表面阳光强度值的变化来确定方位角。在此基础上，可以利用建筑物表面突出部分与其阴影之间的关系来确定仰角。这项技术可以应用于大多数建筑物的重建系统中。