统计纹理特征在图像分类中应用与研究

简介纹理特征与分析的意义与方法内容,详细介绍了基于图像灰度直方图的统计特征方法,通过该方法所得出的纹理度量值,实现了对图像不同灰度值类型的像素分类。     

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.     

基于二维正交滤波的虹膜识别算法

提出一种针对灰度虹膜图像进行虹膜特征提取及匹配的方法,其利用四元数二维正交Log Gabor小波提取虹膜图像的纹理特征,以滤波后图像的解析信号作为虹膜的特征编码.该方法可以同时表征虹膜纹理多方向上的特征,更加全面地描述了虹膜纹理的特征空间.特征匹配采用类似汉明距的方式,同时以虹膜图像中眼睑、睫毛以及光斑的分布为匹配模版来减少它们的干扰.大量实验的结果表明该方法具有非常优越的识别性能.     

融合多特征与随机森林的纹理图像分类方法

针对单一纹理特征与单一分类器对失真纹理图像分类识别率差的问题,提出了一种融合多特征与随机森林的纹理图像分类方法。利用改进的方向梯度直方图(HOG)特征提取方法以及局部二值模式(LBP)图像的灰度共生矩阵进行特征提取;将提取的特征矩阵级联组成一个新的特征矩阵,利用主成分分析法进行降维融合处理;降维融合后的特征矩阵输入随机森林,通过融合投票得到最终的识别率。在KTH-TIPS失真纹理图像库上进行对比实验,结果表明:采用融合多特征与随机森林的分类方法提高了失真纹理图像的分类正确率,且具有更好的实时性。     

一种虹膜定位算法

针对虹膜二值边缘图像提取的困难，提出利用虹膜图像的灰度边缘图像以及虹膜的几何特征进行虹膜定位的快速算法。首先根据虹膜图像的整体灰度分布信息特征，利用改进的Sobel算子提取虹膜的灰度边缘图像，然后利用圆的对称性质以及投票策略提取虹膜的中心，最后利用Hough变换的半径直方图投票提取虹膜内外径。实验结果证明，该算法提高了虹膜定位的速度，并且具有较强的抗干扰能力。     

基于卷缩轮编码的虹膜识别

摘要利用图像纹理元的灰度模式及统计特征完成虹膜图像的卷缩轮分割,并在卷缩轮分割的基础上实现对虹膜的识别;实验结果表明,该方法具有很好的识别效果。     

基于图像能量谱直方图的纹理检索算法RAH

本文提出的基于纹理的图像检索算法RAH是以图像的能量谱直方图为基础,包括以下两个方面：第一,计算图像能量谱的半径和角度直方图的方法以及基于图像能量直方图的频率去提取纹理特征的算法;第二,基于纹理特征的相似度检测方程。这个实验结果表明,RAH算法优于一般的灰度共生矩阵纹理检索算法,具有较好的检索效果,比较适用于基于内容的图像检索。     

基于HOG-GLRLM特征的肠癌病理图片分类

针对利用单一特征集对肠癌病理图像的识别率难以提高这一情况,提出了一个基于HOG-GLRLM特征肠癌病理图片分类方法。考虑到图像中丰富的纹理和边缘信息,分别利用改进型的灰度行程矩阵和梯度方向直方图提取特征。并采用最小冗余最大关联的方法对各自和合并特征集进行特征选择。实验结果表明该方法的有效性。     

一种改进的图像相似度算法

图像相似度算法在图像识别、图像搜素引擎等研究领域具有重要意义。针对传统的灰度颜色直方图算法无法准确地描述各种颜色在图像中的分布情况的问题,提出了一种改进的图像相似度算法。它融合图像的纹理特征,利用灰度共生矩阵来提取图像像素在图像各个位置的特征信息。实验表明,这种融合图像纹理特征的方法不仅保留了灰度颜色直方图算法执行效率高的特点,而且弥补了颜色直方图算法的不足,进而提高了算法的准确性。在实际的应用场景中,可以通过调整两种算法的权值,来进一步提高算法的准确性。     

基于纹理和BP神经网络的SAR图像分类

研究基于纹理和BP神经网络的SAR图像分类。首先用增强FROST滤波算法对SAR图像进行去噪处理。然后基于灰度共生矩阵理论提取去噪后的SAR图像多种纹理特征,并通过大量实验筛选出有效的纹理特征。最后,结合纹理特征,分别采用经典的最大似然分类法和BP神经网络分类法对SAR图像进行分类。实验结果表明：纹理信息辅助SAR图像的灰度进行分类,大大地提高了SAR图像的分类精度;基于BP神经网络的SAR图像分类精度高于最大似然分类法的分类精度。     

基于虹膜纹理深度特征和Fisher向量的人种分类

目的 虹膜是位于人眼表面黑色瞳孔和白色巩膜之间的圆环形区域,有着丰富的纹理信息。虹膜纹理具有高度的区分性和稳定性。人种分类是解决虹膜识别在大规模数据库上应用难题的主要方法之一。现有的虹膜图像人种分类方法主要采用手工设计的特征,而且针对亚洲人和非亚洲人的基本人种分类,无法很好地解决亚种族分类问题。为此提出一种基于虹膜纹理深度特征和Fisher向量的人种分类方法。方法 首先用CNN（convolutional neural network）对归一化后的虹膜纹理图像提取深度特征向量,作为底层特征;然后使用高斯混合模型提取Fisher向量作为最终的虹膜特征表达;最后用支持向量机分类得到最终结果。结果 本文方法在亚洲人和非亚洲人的数据集上采用non-person-disjoint的方式取得99.93%的准确率,采用person-disjoint的方式取得91.94%的准确率;在汉族人和藏族人的数据集上采用non-person-disjoint的方式取得99.69%的准确率,采用person-disjoint的方式取得82.25%的准确率。结论 本文通过数据驱动的方式从训练数据中学习到更适合人种分类的特征,可以很好地实现对基本人种以及亚种族人种的分类,提高了人种分类的精度。同时也首次证明了用虹膜图像进行亚种族分类的可行性,对人种分类理论进行了进一步地丰富和完善。     

基于Gabor变换的纹理分类与识别

自然纹理图像复杂多样,目前国际上没有明确的分类标准,利用中文自然语言中的纹理概念词对常见的自然纹理进行基于概念的分类,并建立了自然纹理图像库。提出了Gabor频谱滤波提取纹理特征的方法,大大提高了计算速度。以支持向量机为分类器,并与传统的基于BP神经网络的识别方法进行对比,实验验证了该分类方法的有效性。     

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.     

一种基于核主成分分析和组合分类器的虹膜识别方法

提出了一种新的虹膜特征提取与识别方法,该方法利用核主成分分析 （KPCA）在高维空间具有较强的特征选择能力来提取虹膜图像的纹理特征。采用了一种距 离度量和支持向量机相结合的两级分类方法,前级采用欧式距离来度量图像间的相似性,若 符合条件,给出分类结果,否则拒绝,并转入后一级分类器——支持向量机分类,以减少进 入支持向量机的样本数目,该组合分类方法充分利用了支持向量机识别率高和距离度量速度 快的优点。实验结果表明,该方法提高了虹膜识别率,是一种有效的虹膜识别方法。     

Texture-based Dither Matrices

Continuous tone images must be halftoned to be displayed on binary output devices such as printers. Halftoning algorithms at low resolutions of the output hardware introduce textures into the resulting display. In this work we control halftoning texture by generating a threshold matrix from an image-based texture. We demonstrate that processing textures by the adaptive histogram equalization algorithm approximates pixel distribution properties of traditional dither screens. Ordered dithering with the resulting threshold matrix enables us to define texture in the halftoned image. We control the appearance of this texture by a combination of the ordered dither algorithm with an error diffusion process. We present applications of texture-based dither screens to both photorealistic and artistic rendering. In the case of photorealistic tone reproduction our technique preserves textures and edges of the original image. The ability to define an arbitrary texture enables us to introduce a variety of artistic effects, including embossing of images with textures and text, and approximation of the appearance of of conventional illustration media. We evaluate the resulting halftoning using multi-scale edge distortion measures. Our quantitative evaluation closely corresponds to the visual observations.     

基于计算光场成像的虹膜活体检测方法

光场成像相对传统光学成像是一次重大技术革新,高维光场信息为生物特征识别的发展与创新带来了新机遇.虹膜身份识别技术以其唯一性、稳定性、高精度等优势广泛应用于国防、教育、金融等各个领域,但是现有的虹膜识别系统容易被人造假体虹膜样本欺骗导致误识别.因此,虹膜活体检测是当前虹膜识别研究亟待解决的关键问题.本文提出一种基于计算光场成像的虹膜活体检测方法,通过软硬件结合的方式,充分挖掘四维光场数据的信息.本方法使用实验室自主研发的光场相机采集光场虹膜图像,利用光场数字重对焦技术提取眼周区域的立体结构特征和虹膜图像的纹理特征,进行特征融合与虹膜分类.在自主采集的近红外光场虹膜活体检测数据库上进行实验,本方法的平均分类错误率（Average classification error rate,ACER）为3.69%,在现有最佳方法的基础上降低5.94%.实验结果表明本方法可以准确有效地检测并阻止打印虹膜和屏显虹膜对系统的攻击.     

结合LBP和Brushlet的自适应图像检索

针对时域和频域纹理特征的优点和互补性,提出一种结合局部二值模式(LBP)和Brushlet域系数统计特征的自适应纹理图像检索方法。利用Brushlet变换得到各个子带的能量作为频域特征,提取图像的LBP直方图作为空域特征,并采用改进的Canberra距离进行度量,使用闭环反馈实现图像的自适应检索。实验结果表明,与LBP方法和Brushlet方法相比,该方法的平均检索率分别提高8.93%和18.66%。     

A new approach for rotation-invariant and noise-resistant texture analysis and classification

The analysis and classification of images, such as texture images, is one of the substantial and important fields in image processing. Due to destructive effects of image rotation and noise, the stability and efficiency of texture analysis and classification methods are an important research area. In this paper, a new method for texture analysis and classification has been proposed which is based on a particular combination of wavelet, ridgelet and Fourier transforms as well as support vector machine. The proposed method has been evaluated for 13 texture datasets produced by three original datasets containing 25 and 111 original textures from Brodatz database and 24 original textures from OUTEX database. These datasets comprise 415584 and 93600 rotated noise-free and noisy texture images for Brodatz database and also 49920 noisy and 4320 noise-free texture images for OUTEX database, respectively. Simulation results demonstrate the capability, efficiency and also stability of the proposed method especially for real-time rotation-invariant and noise-resistant texture analysis and classification.     

Improvement of classification in urban areas by the use of textural features: The case study of Lucknow city,Uttar Pradesh

Investigations have been carried out for digital spectral and textural classification of an Indian urban environment using SPOT images with grey level co-occurrence matrix (GLCM), grey level difference histogram (GLDH), and sum and difference histogram (SADH) approaches. The results indicate that a combination of texture and spectral features significantly improves the classification accuracy compared with classification with pure spectral features only. This improvement is about 9% and 17% for an addition of one and two texture features, respectively. GLDH and SADH give statistically similar results to GLCM, and take less computing time than GLCM. Conventional separability measures like transformed divergence, Bhattacharya distance, etc. are not effective in feature selection when classification is carried out with spectral and texture features. An alternative approach using simple statistics such as average coefficient of variation, skewness, and kurtosis and correlation amongst feature sets has shown greater feature selection potential when a combination of spectral and texture features is used.