基于多通道Gabor滤波器的纹理图像非监督分类

提出了一种快速、实用的Gabor滤波器设计方法.首先根据人类视觉特性选定Gabor滤波器的频率带宽参数,利用图像大小确定滤波器的中心频率,构造一组多通道的Gabor滤波器在时域和频域中提取图像多方向和多分辨率的Gabor能量特征,对特征空间进行非线性变换和Gauss滤波处理,最后通过K means方法实现纹理图像的分割.实验结果表明,该方法能有较好的纹理分割效果.     

基于马尔科夫随机场的图像分割算法

该文整合纹理方面的信息,实现了基于马尔科夫场(MRF)的图像快速分割。通过图像纹理特征利用Gabor滤波器数据特征分量,通过马尔科夫算法将像素分类获得分割结果。     

基于GLCM与自适应Gabor滤波器组的纹理图像分割

基于Gabor滤波器的纹理图像分割算法存在参数难以选择的问题。为此,提出一种预测图像纹理类型数与Gabor滤波器组参数的分割算法。将图像分割成大小相等的区域块,根据各类纹理特性预测Gabor滤波器组参数,利用各区域块的纹理特征向量预测纹理类型数,并使用预测的滤波器组提取图像纹理特征,通过预测的纹理类型数对图像进行聚类分割。实验结果表明,该算法能以较高的精度与较快的速度分割纹理图像,且受纹理类型数量影响较小。     

基于局部纹理特征的钢卷侧面缺陷检测方法研究

针对钢卷侧面图像的纹理结构,提出了一套有效的钢卷侧面图像检测方法和一种新的Gabor滤波器参数设计方法,并将Gabor滤波器与LOG滤波器相结合对图像做预处理,有效的滤除了噪声并增强了图像纹理结构.另外,改进了基于"散度"的Zernike矩特征选取方法并结合SVM对预处理后的钢卷侧面图像子块分类,从而检测目标图像中是否含有缺陷.     

基于颜色和纹理特征的遥感图像检索

针对单一特征遥感图像检索的局限性和传统综合特征检索方法计算复杂度高的问题,提出了一种基于纹理特征和颜色特征的遥感图像的检索。在分析了现存遥感图像的检索方法后,得出了用颜色特征及Gabor小波提取出的纹理特征进行图像检索的总体思路。设计了一组多尺度和多方向的滤波器组,优化并选择滤波器组的各参数。首先对图像进行滤波,然后再进行纹理特征的提取,最后用颜色直方图对遥感图像进行颜色特征提取,最终将融合这两种图像特征,设计并实现一个基于颜色特征和Gabor小波纹理特征的图像检索原型系统。在进行图像检索时将纹理特征和颜色特征融合对图像库进行图像检索。由实验结果表明,所提出的方法是有效的。     

基于人类视觉特性的纹理分割方法

纹理分割是将一幅图像依据纹理不同分成若干个不同的区域,目前广泛采用的是利用滤波器族(如Gabor)对图像进行分解。但由于图像纹理表现的各异性,通常在滤波器参数的选择上不能做到自适应,导致提取的特征不明显,分类效果不好,使用范围受限。文中提出了一种基于人类视觉系统(Human Visual System,HVS)机理的纹理分割方法,不但可以模拟人类观察纹理直觉处理阶段确定纹理区的个数并且粗略地划分区域,而且可以模拟专注处理阶段自动选择Gabor滤波器的个数及参数。该算法符合HVS区分纹理机理,计算过程简单、方便。针对各纹理选取的特征明显,分类效果好。     

基于支持向量机的纹理图像分类算法

研究纹理图像的分类问题,纹理特征提取和分类器设计是决定分类正确率高低的关键。由于库存图像较多,且质量受到噪声影响,使图像特征提取比较困难。针对传统特征提取和分类算法分类正确率不高的难题,提出一种基于支持向量机的纹理图像分类算法。首先采用Gabor滤波器对纹理特征进行提取,采用主成分分析对提取后的特征进行选择,最后采用支持向量机进行纹理图像的分类。采用Brodatz纹理库进行测试实验,实验结果表明,支持向量机分类算法提高了纹理图像的分类正确率,降低了误分率和拒分率,且分类速度加快,适用于更为复杂的纹理分类,为图像提取提供了参考。     

基于Gabor小波变换的ICA火灾图像纹理识别算法

针对火灾图像纹理识别问题,提出了基于Gabor小波变换的ICA火灾图像纹理识别算法,并根据火灾图像纹理识别特点进行了优化;首先用不同尺度和方向的Gabor滤波器对待识别图像滤波,得到其特征图像,然后将特征图像转化成特征向量作为ICA的输入,得到基矢量子空间,再将测试图像经过Gabor滤波器的特征向量投影到ICA子空间中得到系数向量作为目标识别特征,最后用支持向量机进行识别;通过与Gabor滤波器法和ICA方法的对比实验,表明该算法可以在火灾纹理图像的识别率上比传统方法提高5％以上,为火灾图像识别提供了一种新思路.     

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。     

结合Gabor滤波器和扩展LTP算子的无监督纹理图像分割

目的 现实中的纹理往往具有类型多样、形态多变、结构复杂等特点,直接影响到纹理图像分割的准确性。传统的无监督纹理图像分割算法具有一定的局限性,不能很好地提取稳定的纹理特征。本文提出了基于Gabor滤波器和改进的LTP（local ternary pattern）算子的针对复杂纹理图像的纹理特征提取算法。方法 利用Gabor滤波器和扩展LTP算子分别提取相同或相似纹理模式的纹理特征和纹理的差异性特征,并将这些特征融入到水平集框架中对纹理图像进行分割。结果 通过实验表明,对纹理方向及尺度变化较大的图像、复杂背景下的纹理图像以及弱纹理模式的图像,本文方法整体分割结果明显优于传统的Gabor滤波器、结构张量、拓展结构张量、局部相似度因子等纹理分割方法得到的结果。同时,将本文方法与基于LTP的方法进行对比,分割结果依然更优。在量化指标方面,将本文方法与各种无监督的纹理分割方法就分割准确度进行对比,结果表明,在典型的纹理图像上,本文方法准确度达到97%以上,高于其他方法的分割准确度。结论 提出了一种结合Gabor滤波器和扩展LTP算子的无监督多特征的纹理图像分割方法,能够较好地提取相似纹理模式的特征和纹理的差异性特征,且这些纹理特征可以很好地融合到水平集框架中,对真实世界复杂纹理图像能够得到良好的分割效果。     

基于LabVIEW的自适应滤波器设计与研究

针对自适应滤波器编程复杂,难以按照虚拟仪器系统的形式来测试工程应用中的实际性能等问题。文中利用LabVIEW8.6提供的自适应滤波器工具包,设计了基于最小均方误差算法、递推最小二乘算法的自适应滤波器,并对影响两种算法的参数对滤波器的敏感性进行了分析;进而,利用音频信号验证了滤波器性能。仿真结果表明,所设计的自适应滤波器功能全面,人机交互界面良好,便于工程技术人员快速开发,具有较好的工程实用价值。     

Comparison of stochastic filtering methods for 3D tracking

In the recent years, the 3D visual research has gained momentum with publications appearing for all aspects of 3D including visual tracking. This paper presents a review of the literature published for 3D visual tracking over the past five years. The work particularly focuses on stochastic filtering techniques such as particle filter and Kalman filter. These two filters are extensively used for tracking due to their ability to consider uncertainties in the estimation. The improvement in computational power of computers and increasing interest in robust tracking algorithms lead to increase in the use of stochastic filters in visual tracking in general and 3D visual tracking in particular. Stochastic filters are used for numerous applications in the literature such as robot navigation, computer games and behavior analysis. Kalman filter is a linear estimator which approximates system's dynamics with Gaussian model while particle filter approximates system's dynamics using weighted samples. In this paper, we investigate the implementation of Kalman and particle filters in the published work and we provide comparison between these techniques qualitatively as well as quantitatively. The quantitative analysis is in terms of computational time and accuracy. The quantitative analysis has been implemented using four parameters of the tracked object which are object position, velocity, size of bounding ellipse and orientation angle.     

Evaluation and design of filters using a Taylor series expansion

We describe a new method for analyzing, classifying, and evaluating filters that can be applied to interpolation filters as well as to arbitrary derivative filters of any order. Our analysis is based on the Taylor series expansion of the convolution sum. Our analysis shows the need and derives the method for the normalization of derivative filter weights. Under certain minimal restrictions of the underlying function, we are able to compute tight absolute error bounds of the reconstruction process. We demonstrate the utilization of our methods to the analysis of the class of cubic BC-spline filters. As our technique is not restricted to interpolation filters, we are able to show that the Catmull-Rom spline filter and its derivative are the most accurate reconstruction and derivative filters, respectively, among the class of BC-spline filters. We also present a new derivative filter which features better spatial accuracy than any derivative BC-spline filter, and is optimal within our framework. We conclude by demonstrating the use of these optimal filters for accurate interpolation and gradient estimation in volume rendering     

Exact finite-dimensional filters for doubly stochasticauto-regressive processes

In this paper, we derive exact finite-dimensional recursive filters for a class of doubly stochastic auto-regressive (AR) models. We assume that the parameters of the doubly stochastic AR process vary according to a nonlinear polynomial function of a Gaussian state-space process. Apart from being of mathematical interest, these finite-dimensional filters have potential applications in time-series analysis and image-enhanced tracking of maneuvering targets     

Texture segmentation using 2-D Gabor elementary functions

Many texture-segmentation schemes use an elaborate bank of filters to decompose a textured image into a joint space/spatial-frequency representation. Although these schemes show promise, and although some analytical work has been done, the relationship between texture differences and the filter configurations required to distinguish them remain largely unknown. This paper examines the issue of designing individual filters. Using a 2-D texture model, we show analytically that applying a properly configured bandpass filter to a textured image produces distinct output discontinuities at texture boundaries; the analysis is based on Gabor elementary functions, but it is the bandpass nature of the filter that is essential. Depending on the type of texture difference, these discontinuities form one of four characteristic signatures: a step, ridge, valley, or a step change in average local output variation. Accompanying experimental evidence indicates that these signatures are useful for segmenting an image. The analysis indicates those texture characteristics that are responsible for each signature type. Detailed criteria are provided for designing filters that can produce quality output signatures. We also illustrate occasions when asymmetric filters are beneficial, an issue not previously addressed     

电动汽车同步变速箱传动振荡抑制

针对电动汽车动力系统的简化模型,提出使用时滞滤波器减少动力系统中的传动振荡的方法。先将复杂的电机控制系统和变速传动系统简化成低阶(二阶)线性系统,通过实验估计系统参数,然后根据简化系统模型,依据时滞滤波器理论设计几种时滞滤波器。最后通过仿真和实验,验证时滞滤波器对传动振荡具有良好的抑制效果,间接说明时滞滤波器在汽车动力系统这类时变高阶系统中仍然能够滤除其中的二阶振荡环节。     

Finite-dimensional recurrent algorithms for optimal nonlinear logical–dynamical filtering

The problem of the most accurate estimation of the current state of a multimode nonlinear dynamic observation system with discrete time based on indirect measurements of this state is considered. The general case when a mode indicator is available and the measurement errors depend on the plant disturbances is investigated. A comparative analysis of two known approaches is performed—the conventional absolutely optimal one based on the use of the posterior probability distribution, which requires the use of an unimplementable infinite-dimensional estimation algorithm, and a finitedimensional optimal approach, which produces the best structure of the difference equation of a low-order filter. More practical equations for the Gaussian approximations of these two optimal filters are obtained and compared. In the case of the absolutely optimal case, such an approximation is finitedimensional, but it differs from the approximation of the finite-dimensional optimal version in terms of its considerably larger dimension and the absence of parameters. The presence of parameters, which can be preliminarily calculated using the Monte-Carlo method, allows the Gaussian finite-dimensional optimal filter to produce more accurate estimates.     

子带自适应滤波器组参数的选取及其影响

为了研究自适应滤波在噪声消除,干扰抑制以及雷达信号处理中的广泛应用,对滤波器各影响因素进行了分析,提出了各参数的设计规则.以自适应滤波理论为基础,将信号划分成不同的子带通过原型分析综合滤波器组,将处理后的各路信号叠加可恢复原信号.选择合理的参数设置滤波器可以使系统的综合性能最优,同时可以将信号在传输过程中造成的损失减小到最低.通过大量仿真验证了该参数选取规则的准确性和有效性.     

Optimization of temporal filters for constructing robust features in speech recognition

Linear discriminant analysis (LDA) has long been used to derive data-driven temporal filters in order to improve the robustness of speech features used in speech recognition. In this paper, we proposed the use of new optimization criteria of principal component analysis (PCA) and the minimum classification error (MCE) for constructing the temporal filters. Detailed comparative performance analysis for the features obtained using the three optimization criteria, LDA, PCA, and MCE, with various types of noise and a wide range of SNR values is presented. It was found that the new criteria lead to superior performance over the original MFCC features, just as LDA-derived filters can. In addition, the newly proposed MCE-derived filters can often do better than the LDA-derived filters. Also, it is shown that further performance improvements are achievable if any of these LDA/PCA/MCE-derived filters are integrated with the conventional approach of cepstral mean and variance normalization (CMVN). The performance improvements obtained in recognition experiments are further supported by analyses conducted using two different distance measures.     

Real-time implementation of order-statistics-based directional filters

Vector filters based on order-statistics have proved successful in removing impulsive noise from colour images while preserving edges and fine image details. Among these filters, the ones that involve the cosine distance function (directional filters) have particularly high computational requirements, which limits their use in time-critical applications. In this paper, we introduce two methods to speed up these filters. Experiments on a diverse set of colour images show that the proposed methods provide substantial computational gains without significant loss of accuracy.