基于NSST和MCM纹理特征提取算法

纹理特征提取作为图像处理的重要环节,对图像的后续处理有着至关重要的影响.文中在多分辨共生矩阵算法的基础上,针对标准Brodatz纹理图像检索,通过非下采样剪切波变换的多分辨共生矩阵和混合高斯模型相结合,提出了一种纹理特征提取算法.文中首先对Brodatz纹理图像进行非下采样剪切波变换得到子带系数,通过对细节子带直方图分析,引入了拟合效果较好的混合高斯模型.然后利用优化的非均匀量化策略,提取多分辨共生矩阵纹理特征F2和F10.最后将提取的纹理特征与统计特征级联融合并结合具有权重系数的相似性度量公式,用于最终纹理图像检索.仿真实验表明:与传统多分辨共生矩阵的方法相比,文中所提算法的平均检索率分别提高了2.01%和8.87%.     

基于非下采样Contourlet变换与TV模型混合图像去噪算法

在非下采样Contourlet变换的基础上,综合考虑全变差扩散和正态逆高斯模型,提出一种新的图像去噪算法.首先,对图像进行非下采样Contourlet变换,得到高频子带和低频子带系数.然后,对低频子带进行全变差扩散处理,对于方向带通子带,先通过分类准则对其进行分类,将其分为重要系数和不重要系数,对重要系数采样正态逆高斯建模,不重要系数采用高斯分布模型建模.实验结果证明,本文方法在视觉效果、峰值信噪比以及平均结构性上均优于许多算法.     

一种结合小波模极大值法和Canny算子的目标提取方法

针对基于小波变换的目标提取中忽略低频子图像的一些重要信息的问题.提出了一种基于小波变换的模极大值法和Canny算子的目标提取方法.在小波域中,通过求解局部小波系数模型的极大值点提取(检测)高频边缘,利用Canny算子提取(检测)低频边缘.然后根据融合规则对两个子图像边缘进行融合.实验结果表明,该方法不仅能有效地增强图像边缘,而且能准确地定位图像边缘.     

遥感图像小波变换系数的统计分布

本文研究了遥感图像经张量积小波与非张量积多元小波变换后得到的小波系数的统计分布及其特性,得出了遥感图像经双正交9-7整数小波变换后的系数的每个高频子带在能量分布上近似关于原点对称,每个高上频子带都具有“非高斯”性,每层的三个高频子带分布相似,第一层的各子带的值在零点附近更为集中,所以在零点形成更陡更窄的“尖峰”的结论.     

基于小波变换的Laws纹理测度在植被分割中的应用

针对从全色航空影像中进行植被区域提取的随机性和复杂性,阐述了一种基于小波的Laws纹理测度进行植被提取的新算法,它的特点是先用小波变换将图像变换到不同的尺度层上,然后再在多尺度层上提取Laws纹理测度,形成植被区域的特征.与传统的植被提取方法比,它用到了不同频率上纹理的Laws信息,从而更准确的刻画了植被区域的纹理特征,试验结果表明:基于小波变换的Laws纹理测度对植被有较好的分割效果.     

基于Curvelet域高斯尺度混合模型的地震信号降噪方法

结合曲波变换和高斯尺度混合模型提出地震信号随机噪声压制方法.该方法首先运用曲波变换对含有随机噪声的地震信号进行分解,然后对各小波子带系数分别建立高斯尺度混合模型估计出原始地震信号所对应的小波系数,最后经曲波逆变换重构获得降噪处理后的地震信号.仿真地震信号和实际地震信号的实验结果均表明本文方法能够有效压制地震信号中的随机噪声干扰,较多地保留了有效信号.     

多小波图像插值算法研究

与单小波变换一样,多小波变换同样具有多分辨分析的特性,1次多小波变换可以将图像分解成4个低频子带和12个高频子带,而且原图像的大小是每个子带的4倍.根据多小波变换的这一特点,利用原图像与经过1次多小波变换后的各高频子带的信息,并考虑各子带的分形维数,提出了一种新颖的灰度图像插值算法.实验结果表明,与传统的插值算法相比,例如双线性插值与双三次多项式插值,该算法的插值效果较好,且克服了单小波插值中出现的斑点干扰.     

数据的多流形结构分析

建立了基于ALM和权值的LRR聚类改进模型,对高维数据进行分析,将其分为两个独立的子空间,并与传统k-means聚类模型进行对比,采用评价指标模型对聚类结果进行评价分析.提出的LRR聚类改进模型在正则项引入了权重系数w,可以更好地将扰动分开,求解结果及评价指标均有效地验证了其稳定性、精确度等性能均有所提升.建立了SMMC改进模型,对机器工件外部边缘轮廓进行分类.从求解结果可看出该模型非常适合用于处理混合多流形聚类问题,对于比较复杂的曲线有着很好的分类性能.按照数据预处理、数据建模分析、模型结果评价步骤,通过使用谱聚类分析和多流形学习方法,对所给出的高维数据进行分析和处理,并通过评价模型得出相应的评价指标,对数据的多流形结构进行了深入的研究和探讨.     

基于自适应小波变换和矢量量化的图像压缩编码

运用小波变换进行图像压缩的算法其核心都是小波变换的多分辨率分析以及对不同尺度的小波系数的量化和编码 .本文提出了一种基于能量的自适应小波变换和矢量量化相结合的压缩算法 .即在一定的能量准则下 ,根据子图像的能量大小决定是否进行小波分解 ,然后给出恰当的小波系数量化 .在量化过程中 ,采用一种改进的LBG算法进行码书的训练 .实验表明 ,本算法广泛适用于不同特征的数字图像 ,在取得较高峰值信噪比的同时可以获得较高的重建图像质量 .     

基于多尺度几何分解的光照不均图像增强方法

针对光照不均匀的图像,结合W系统和NSCT变换,提出了一种新的图像增强方法.方法首先利用W变换对图像进行多尺度分解,然后利用NSCT中的非下采样方向滤波器组对尺度分解中的高频部分进行方向分解,得到不同尺度不同方向上的变换系数.在多尺度几何分解的基础上,对低频子带图像采用动态直方图均衡化、高频子带图像采用同态滤波的方法进行增强处理,最后利用非线性函数减小图像明、暗部分灰度值的差异,得到最后的增强结果.仿真实验结果表明,算法无论在视觉效果上还是客观评价指标上都优于其他被比较的四种增强算法,对于过亮、过暗以及局部光照不均匀的图像均取得了更好的增强效果,在增强图像细节的同时能有效抑制图像的伪吉布斯失真和过增强失真.在评价指标上,算法对三组经典图像处理后的增强图像的信息熵分别达到了10.0755、9.7879、10.5338,明显优于其他方法.     

基于提升小波的基因芯片数据的分类预测

针对肿瘤的早期诊断,提出了一种基于提升小波变换的特征提取的方法,对肿瘤数据样本进行分析鉴别.该方法利用提升小波变换对190例肝癌（包括对照）和107例肺癌（包括对照）基因表达谱芯片数据进行处理后,提取信号的低频信息,经支持向量机训练学习,构造分类器模型,用于癌和非癌样本的区分甄别.实验结果表明,经提升小波变换提取的特征基因,送入分类器中能得到较高的分类率,且在支持向量机中选取线性核函数或径向基函数都能达到较好的分类效果.通过随机选取的20例基因表达谱芯片样本,对所建立的模型进行了测试,获得了很好的效果,因此,本文提出的方法对肿瘤的诊断有一定的应用意义.     

Clusters, outliers, and regression: fixed point clusters

Fixed point clustering is a new stochastic approach to cluster analysis. The definition of a single fixed point cluster (FPC) is based on a simple parametric model, but there is no parametric assumption for the whole dataset as opposed to mixture modeling and other approaches. An FPC is defined as a data subset that is exactly the set of non-outliers with respect to its own parameter estimators. This paper concentrates upon the theoretical foundation of FPC analysis as a method for clusterwise linear regression, i.e., the single clusters are modeled as linear regressions with normal errors. In this setup, fixed point clustering is based on an iteratively reweighted estimation with zero weight for all outliers. FPCs are non-hierarchical, but they may overlap and include each other. A specification of the number of clusters is not needed. Consistency results are given for certain mixture models of interest in cluster analysis. Convergence of a fixed point algorithm is shown. Application to a real dataset shows that fixed point clustering can highlight some other interesting features of datasets compared to maximum likelihood methods in the presence of deviations from the usual assumptions of model based cluster analysis.     

Merging Mixture Components for Clustering Through Pairwise Overlap

Finite mixture models are well known for their flexibility in modeling heterogeneity in data. Model-based clustering is an important application of mixture models, which assumes that each mixture component distribution can adequately model a particular group of data. Unfortunately, when more than one component is needed for each group, the appealing one-to-one correspondence between mixture components and groups of data is ruined and model-based clustering loses its attractive interpretation. Several remedies have been considered in literature. We discuss the most promising recent results obtained in this area and propose a new algorithm that finds partitionings through merging mixture components relying on their pairwise overlap. The proposed technique is illustrated on a popular classification and several synthetic datasets, with excellent results.     

Fusion of multi-spectral and panchromatic images using fuzzy rule

A fusion approach is proposed to refine the resolution of a multi-spectral image using a high-resolution panchromatic image. After the two images are decomposed by wavelet transform, five texture features are extracted from the high-frequency detailed sub-images. Then a nonlinear fusion rule, i.e. fuzzy rule is used to merge wavelet coefficients from the two images according to the extracted features. Experimental results indicate that the method outperforms the traditional approaches in preserving spectral information while improving spatial information.     

基于Gabor小波的纹理图像分类(英文)

For a texture image, by recognizining the class of every pixel of the image, it can be partitioned into disjoint regions of uniform texture. This paper proposed a texture image classification algorithm based on Gabor wavelet. In this algorithm, characteristic of every image is obtained through every pixel and its neighborhood of this image. And this algorithm can achieve the information transform between different sizes of neighborhood.Experiments on standard Brodatz texture image dataset show that our proposed algorithm can achieve good classification rates.     

小波基的选取对图像去噪的影响

小波图像去噪方法是现代图像处理中的重要组成部分，小波基的不同选取直接影响到去噪的效果．本文在全局阈值的标准下，通过对噪声水平和图像纹理特征的估计，讨论了小波基的正交性和线性相位性对去噪结果的不同影响，提出了选取小波基的近似标准．     

Wavelet Filtering for Filtered Backprojection in Computed Tomography

In this paper, we present a characterization of MRA biorthogonal wavelet filters with full frequency supports. Based on this characterization, it is established that wavelet ramp filters are biorthogonal wavelets if the original wavelets are sufficiently regular. An efficient subband coding algorithm is developed for wavelet filtering in filtered backprojection, which is the most popular method in computed tomography (CT). Computer simulation suggests that this wavelet filtering process is a useful tool for improving image quality and reducing computational time in local CT reconstruction.     

sppmix: Poisson point process modeling using normal mixture models

This paper describes the package sppmix for the statistical environment R. The sppmix package implements classes and methods for modeling spatial point patterns using inhomogeneous Poisson point processes, where the intensity surface is assumed to be a multiple of a finite additive mixture of normal components and the number of components is a finite, fixed or random integer. Extensions to the marked inhomogeneous Poisson point processes case are also presented. We provide an extensive suite of R functions that can be used to simulate, visualize and model point patterns, estimate the parameters of the models, assess convergence of the algorithms and perform model selection and checking in the proposed modeling context. In addition, several approaches have been implemented in order to handle the standard label switching issue which arises in any modeling approach involving mixture models. We adapt a hierarchical Bayesian framework in order to model the intensity surfaces and have implemented two major algorithms in order to estimate the parameters of the mixture models involved: the data augmentation and the birth–death Markov chain Monte Carlo (DAMCMC and BDMCMC). We used C++ (via the Rcpp package) in order to implement the most computationally intensive algorithms.