基于空间结构统计建模的图像分类方法

提出一种基于图像空间结构统计建模的复杂纹理图像模式识别方法。从理论上分析了复杂纹理图像空间结构的韦伯分布过程,通过构造多尺度全向高斯导数滤波器,获得复杂纹理图像在不同观测尺度上的全方向空间结构统计建模表征结果。基于偏最小二乘-判决分析原理构建分类器,实现了复杂纹理图像的分类识别。实验结果表明,所提出的图像空间结构统计建模方法能获得复杂纹理图像关键性的视觉感知特性,基于该方法的图像分类准确率高且性能稳定。     

硅太阳能电池纹理缺陷检测

为实现硅太阳能电池纹理缺陷检测,提出一种采用方向可变滤波器组并结合Hough变换的检测方法。通过方向可变滤波器提取图像边缘并采用Hough变换确定纹理方向,采用角度与纹理方向一致的方向可变滤波器滤波,实现消除规则直线纹理,保留纹理缺陷特征。对滤波后的纹理缺陷结果图像采用双阈值法,以确定纹理缺陷所在的位置。和Gabor滤波器及小波滤波器的比较实验结果表明：该方法比前两种方法能更有效地进行硅太阳能电池纹理缺陷检测。     

一种方向Gabor滤波纹理分割算法

结合人眼视觉特性，设计了一种方向Gabor滤波器，该滤波器顾及了纹理图像的方向特性；利用Gabor滤波器的带通技术，抑制次要纹理图像的主频率分量，增强目标纹理图像主频率分量，使滤波输出图像具有较大的类间离散度和较小的类内离散度，将纹理图像的分割转化为传统的图像分割，使图像的分割质量和算法效率都得到了提高。     

基于匹配Gabor滤波器的规则纹理缺陷检测方法

许多工业产品表面纹理都可以被认为是由基本纹理单元在空间按照一定的规则进行排列组合的结果,但由于各种原因,这些有规则纹理图象经常出现的一些缺陷,因而检测这些有规则纹理图象的缺陷是机器视觉检测的重要内容,为了对这种缺陷进行有效地检测,在对这类纹理图象进行功率谱分析的基础上,根据人眼的视觉原理,设计了两类匹配Gabor滤波器,即正常纹理匹配Gabor滤波器和缺陷纹理匹配Gabor滤波器,前者能够突出正常纹理,抑制缺陷纹理,而后者恰恰相反,在将这两类滤波器用于规则纹理图象缺陷的自动检测时,均获得了良好的检测精度和速度。     

Evaluation of the effects of Gabor filter parameters on texture classification

Gabor filtering is a widely adopted technique for texture analysis. The design of a Gabor filter bank is a complex task. In texture classification, in particular, Gabor filters show a strong dependence on a certain number of parameters, the values of which may significantly affect the outcome of the classification procedures. Many different approaches to Gabor filter design, based on mathematical and physiological consideration, are documented in literature. However, the effect of each parameter, as well as the effects of their interaction, remain unclear. The overall aim of this work is to investigate the effects of Gabor filter parameters on texture classification. An extensive experimental campaign has been conducted. The outcomes of the experimental activity show a significant dependence of the percentage of correct classification on the smoothing parameter of the Gabor filters. On the contrary, the correlation between the number of frequencies and orientations used to define a filter bank and the percentage of correct classification appeared to be poor.     

综合纹理特征的高光谱遥感图像分类方法

提出了一种基于Gabor滤波的高光谱遥感图像支持向量机(SVM)分类方法,通过将Gabor滤波器组产生的纹理特征引入SVM分类,不仅充分利用了SVM适于解决高维数据分类问题的优势,而且在分类过程中实现了空间结构信息和光谱信息的综合使用,有效利用了高光谱图像“图谱合一”的特性.采用中科院上海技术物理研究所研制的模块化成像光谱仪OMIS (operative modular imaging spectrometry)真实数据进行的实验,实验结果表明,该方法提高了分类效果,分类结果更具有空间连贯性,并且能有效地克服噪声的影响.     

基于Gabor滤波器和HOG特征的织物疵点检测

针对织物疵点检测问题,提出了一种基于Gabor滤波器和方向梯度直方图（HOG）特征的织物疵点检测算法。首先使用3个尺度、4个方向的Gabor滤波器组对织物图像进行滤波,并做融合处理,增强织物图像疵点区域和背景纹理之间的对比度;然后使用双边滤波减弱图像背景纹理和噪声的影响;最后将图像划分成均匀子块,提取每个子图像块的HOG特征,利用图像疵点区域和背景纹理的HOG特征差异进行阈值分割实现织物疵点的检测。实验选取5种常见织物疵点进行验证,并与传统的Gabor滤波算法进行了实验对比,结果表明该算法可以较好的抑制织物背景纹理的干扰,更加准确的检测出织物疵点。     

Unsupervised texture segmentation using Gabor filters

This paper presents a texture segmentation algorithm inspired by the multi-channel filtering theory for visual information processing in the early stages of human visual system. The channels are characterized by a bank of Gabor filters that nearly uniformly covers the spatial-frequency domain, and a systematic filter selection scheme is proposed, which is based on reconstruction of the input image from the filtered images. Texture features are obtained by subjecting each (selected) filtered image to a nonlinear transformation and computing a measure of “energy” in a window around each pixel. A square-error clustering algorithm is then used to integrate the feature images and produce a segmentation. A simple procedure to incorporate spatial information in the clustering process is proposed. A relative index is used to estimate the “true” number of texture categories.     

基于Gabor滤波器组的织物疵点检测方法

给出了基于Gabor滤波器组的织物疵点检测方法。在分析Gabor滤波器时频特性的基础上,针对素色坯布织物疵点图像,设计了椭圆形多尺度多方向的Gabor滤波器组,并应用该滤波器组在频域对织物疵点图像进行滤波处理,对滤波后的多幅图像进行融合与分割处理,将疵点从织物背景中分割出来,从而实现了疵点的检测。实验结果证明了该方法的有效性。     

基于可分离Gabor滤波的纹理分析方法

提出一种基于可分离Gabor滤波的纹理分析新方法,该方法把传统的Gabor滤波器分解成为两个相互正交的低通和带通滤波器,以降低滤波运算量;并指出在正交分解条件不满足的情况下,通过旋转样本图像,解决Gabor滤波器在任意角度的正交分解问题.实验表明,新方法与传统方法相比,效率提高30%以上,并且准确率更高.     

Adaptive Matrices and Filters for Color Texture Classification

In this paper we introduce an integrative approach towards color texture classification and recognition using a supervised learning framework. Our approach is based on Generalized Learning Vector Quantization (GLVQ), extended by an adaptive distance measure, which is defined in the Fourier domain, and adaptive filter kernels based on Gabor filters. We evaluate the proposed technique on two sets of color texture images and compare results with those other methods achieve. The features and filter kernels learned by GLVQ improve classification accuracy and they are able to generalize much better for data previously unknown to the system.     

Textural and local spatial statistics for the object‐oriented classification of urban areas using high resolution imagery

Textural and local spatial statistical information is important in the classification of urban areas using very high resolution imagery. This paper describes the utility of textural and local spatial statistics for the improvement of object‐oriented classification for QuickBird imagery. All textural/spatial bands were used as additional bands in the supervised object‐oriented classification. The texture analysis is based on two levels: segmented image objects and moving windows across the whole image. In the texture analysis over image objects, the angular second moment textural feature at a 45° angle showed an improved classification performance with regard to buildings, depicting the patterns of buildings better than any other directions. The texture analysis based on moving windows across the whole image was conducted with various window sizes (from 3×3 to 13×13), and four grey‐level co‐occurrence matrix (GLCM) textural features (homogeneity, contrast, angular second moment, and entropy) were calculated. The contrast feature with the 7×7 window size improved classification up to 6%. One type of local spatial statistics, Moran's I feature with the vertical neighbourhood rule, improved the classification accuracy even further, up to 7%. Comparison of results between spectral and spectral+textural/spatial information indicated that textural and spatial information can be used to improve the object‐oriented classification of urban areas using very high resolution imagery.     

TDFA:一种生成空间影像金字塔的方法

目的 降采样滤波是生成空间金字塔影像数据的主要手段,但目前没有一种客观指标来鉴别滤波器的降采样效果,因为至少需要空间金字塔的两层原始信号才能计算滤波器的降采样峰值信噪比(PSNR)。为解决此难题,本文建立一种研究路线:先基于视频影像数据评选确定一个性能优秀的降采样滤波器,然后验证该滤波器降采样生成遥感金字塔的主观目视效果,提出一种沿图像纹理方向滤波的降采样方法TDFA(texture direction filtering approach),可生成高质量的空间影像金字塔。方法 本文把降采样与升采样结合提出一种重采样滤波对偶RSFP(re-sampling filter pair),作为当前层金字塔数据的一个逼近,用来评价降采样滤波器效果。基于RSFP评价手段,筛选出一种基于纹理滤波的金字塔生成方法TDFA:对每个8×8块,TDFA在直流、水平、135°、垂直和45°等5个方向中搜索确定图像的一个纹理方向,用一个3阶滤波器沿纹理方向实施降采样,效果优于目前最好的最邻近插值方法,无任何伪彩、锯齿、块效应或马赛克。结果 利用大量影像数据实验,同几个典型滤波器的降采样效果对比,TDFA提升平均PSNR的范围,对拉格朗日滤波器是7.29~8.44 dB;对双线性滤波器是6.26~7.40 dB;对AVS的1/4插值滤波器是5.80~6.84 dB;对最邻近插值是4.51~5.70 dB。结论 本文提出的纹理滤波降采样算法可以生成质量优于现有最好水平的遥感金字塔影像,也可以生成高质量的多层视频流媒体数据。所提出的重采样滤波对偶RSFP可以输出当前层的高精度预测,用于可伸缩视频编码处理。     

高斯颜色模型在瓷片图像分类中的应用

由于RGB颜色空间不能很好贴近人的视觉感知,同时也缺少对空间结构的描述,因此采用兼顾颜色信息和空间信息的高斯颜色模型以获取更全面的特征,提出了一种基于高斯颜色模型和多尺度滤波器组的彩色纹理图像分类法,用于瓷器碎片图像的分类。首先将原始图像的RGB颜色空间转换到高斯颜色模型;再用正规化多尺度LM滤波器组对高斯颜色模型的3个通道构造滤波图像,并借助主成分分析寻找主特征图,接着选取各通道的最大高斯拉普拉斯和最大高斯响应图像,与特征图联合构成特征图像组用以进行参数提取;最后以支持向量机作为分类器进行学习和分类。实验结果表明,与基于灰度的、基于RGB模型的和基于RGB_bior 4.4小波的方法相比,本文方法具有更好的分类结果,其中在Outex纹理图像库上获得的分类准确率为96.7%,在瓷片图像集上获得的分类准确率为94.2%。此方法可推广应用到其他彩色纹理分类任务。     

多分辨率二项分布滤波器及其在纹理分类中的应用

从空域与频域两方面对二项分布滤波器及Ｇａｂｏｒ滤波器进行了分析比较。当两种滤波器的尺度空间常数很大时,它们的时域与频域性能基本相近,但在尺度空间常数较小时,二项分布滤波器的性能稍优于Ｇａｂｏｒ滤波器。在对纹理图象的分类中其性能得到验证。     

A multiscale texture analysis procedure for improved forest stand classification

Image texture is a complex visual perception. With the ever-increasing spatial resolution of remotely sensed data, the role of image texture in image classification has increased. Current approaches to image texture analysis rely on a single band of spatial information to characterize texture. This paper presents a multiscale approach to image texture where first and second-order statistical measures were derived from different sizes of processing windows and were used as additional information in a supervised classification. By using several bands of textural information processed with different window sizes (from 5×5 to 15×15) the main forest stands in the image were improved up to a maximum of 40%. A geostatistical analysis indicated that there was no single window size that would adequately characterize the range of textural conditions present in this image. A number of different statistical texture measures were compared for this image. While all of the different texture measures provided a degree of improvement (from 4 to 13% overall), the multiscale approach achieved a higher degree of classification accuracy regardless of which statistical procedure was used. When compared with single band texture measures, the level of overall improvement varied between 4 and 8%. The results indicate that this multiscale approach is an improvement over the current single band approach to analysing image texture.     

A multi-scale supervised orientational invariant neural architecture for natural texture classification

A multi-scale supervised neural architecture, called Multi-Scale SOON, is proposed for natural texture classification. This architecture recognizes the input textured image through a hierarchical categorization structure in multiple scales. This process consists of three sequential phases: a multi-scale feature extraction, a scale prototype pattern generation, and a multi-scale prototype fusion pattern classification. First phase extracts scale textural features using the Gabor filtering. Then, a hierarchical categorization shapes the classification. First categorization level generates the scale prototypes and an upper level categorizes the prototypes fusion. Three increasing complexity tests over the well-known Brodatz database are performed in order to quantify the Multi-Scale SOON behavior. The comparison to other standout methods proves Multi-Scale SOON behavior to be satisfactory. The tests, including the entire texture album, show the stability and robustness of the Multi-Scale SOON response.     

Image mining by spectral features: A case study of scenery image classification

Spectral features of images, such as Gabor filters and wavelet transform can be used for texture image classification. That is, a classifier is trained based on some labeled texture features as the training set to classify unlabeled texture features of images into some pre-defined classes. The aim of this paper is twofold. First, it investigates the classification performance of using Gabor filters, wavelet transform, and their combination respectively, as the texture feature representation of scenery images (such as mountain, castle, etc.). A k-nearest neighbor (k-NN) classifier and support vector machine (SVM) are also compared. Second, three k-NN classifiers and three SVMs are combined respectively, in which each of the combined three classifiers uses one of the above three texture feature representations respectively, to see whether combining multiple classifiers can outperform the single classifier in terms of scenery image classification. The result shows that a single SVM using Gabor filters provides the highest classification accuracy than the other two spectral features and the combined three k-NN classifiers and three SVMs.     

Optimal Gabor filters for textile flaw detection

The task of detecting flaws in woven textiles can be formulated as the problem of segmenting a “known” non-defective texture from an “unknown” defective texture. In order to discriminate defective texture pixels from non-defective texture pixels, optimal 2-D Gabor filters are designed such that, when applied to non-defective texture, the filter response maximises a Fisher cost function. A pixel of potentially flawed texture is classified as defective or non-defective based on the Gabor filter response at that pixel. The results of this optimised Gabor filter classification scheme are presented for 35 different flawed homogeneous textures. These results exhibit accurate flaw detection with low false alarm rate. Potentially, our novel optimised Gabor filter method could be applied to the more complicated problem of detecting flaws in jacquard textiles. This second and more difficult problem is also discussed, along with some preliminary results.     

基于改进Gabor优化选择的布匹瑕疵检测方法

针对传统Gabor优化选择方法用于布匹瑕疵检测时准确率低、鲁棒性差的缺点,提出了改进的优化选择方法,通过瑕疵图像与标准图像Gabor滤波后分块子图均值差平方和的代价函数实现优化选择。设置一组不同方向和尺度的Gabor滤波器并提取标准图像滤波后相关参数,通过改进的优化选择方法实现滤波后瑕疵图像的最优选择,利用迭代式阈值分割对最优滤波后图像进行二值分割,根据分割后图像的像素信息检测是否含有瑕疵并输出瑕疵信息。实验验证该方法,并与传统优化选择方法对比分析,结果表明该方法运算量较少,且检查性能高,可满足在线检测要求。