变形雅可比（p=4，q=3）—傅立叶矩的抗噪声能力研究

使用加法性零均白噪声，从统计性重建误差、统计性信噪比和带噪声图像描述等方面分析了变形雅可比(p=4，g=3)—傅立叶矩(PJFM′S)的抗噪声能力。理论和实验都表明，该矩除了具有平移、灰度、尺度和旋转等多畸变不变性，还具有很强的抗噪声能力，性能优于其它图像矩，更适合用于多畸变不变图像的描述和识别。     

基于递推算法的Krawthcouk矩的局部图像重建

图像局部特征提取在图像处理领域中有着重要的应用,Zernike等其他正交矩,一般用来表达图像的整体特征;而Krawtchouk矩则可以用来表达图像的局部特征。本文在Krawtchouk不变矩定义的基础上,讨论了基于递推算法的Krawtchouk不变矩的图像局部重建问题。最后给出了基于上述算法的实验结果。实验结果表明,该算法不仅可以有效地提取图像的局部特征,而且提取特征和图像重建时间与原始算法相比都有显著减少。     

基于兴趣点不变矩的图像拼接

提出了一种基于兴趣点不变矩(IPIM)的图像拼接技术.利用Harris角检测器获取图像中的兴趣点,计算兴趣点邻域的平移、旋转及尺度不变矩,通过比较各兴趣点邻域不变矩的欧式距离提取出初始特征点对,根据几何变换模型剔除伪特征对,最后利用正确映射模型实现图像的拼接.实验表明,该方法对平移以及任意角度的旋转具有良好的鲁棒性,对于具有小尺度变换的图像仍然具有很好的拼接效果.     

SAR图像矩特征分析

图像的矩特征对图像所表示物体的特征有很独特的表示意义,在目标识别、图像分析等很多领域都有着广泛的应用。本文用MATLAB软件编程仿真分析了图像矩的特征。对经过预处理的一幅SAR图像,用编写的目标提取函数对图像进行目标提取,编程计算出目标的不高于三阶的七个几何矩特征的值,然后分析了图像旋转时几何矩特征的特性。在图像以每次5度进行旋转直至转360的过程中,使用双线性插值法和维纳滤波对旋转后的图像处理,然后分别计算出每个角度时目标的矩值,并作出矩值的变化曲线。     

基于兴趣点伪泽尼克矩的图像拼接

针对基于特征匹配的传统图像拼接方法对旋转和噪声敏感的问题,提出了一种基于兴趣点伪泽尼克(Zernike)矩的图像自动拼接技术.利用哈里斯(Harris)角检测器获取图像中的兴趣点,计算以兴趣点为中心邻域窗口的伪泽尼克矩,通过比较各个兴趣点邻域伪泽尼克矩的欧氏距离提取出初始特征点对,根据几何变换模型剔除伪特征点对,最后利用得到的几何变换模型,对输入图像进行几何变换后将两幅图像间的重叠区域进行图像融合,完成图像的拼接.实验表明,该方法对平移、任意角度的旋转以及噪声均具有鲁棒性,对于具有小尺度变换(小于1.5)的图像仍然具有很好的拼接效果.     

一种基于图像特征的图像分类方法

图像分类是色域匹配的关键环节,不同类型的图像采用不同的匹配方法.针对如何有效分类图像,设计了一种基于图像特征的图像分类算法.首先建立图像颜色的三个通道特征统计模型和基于空间灰度级的纹理统计、边缘特征的统计模型,然后根据模型计算出图像的三类特征值,利用特征统计评判和神经网络技术分析计算数据,最后得出图像类型.实验结果表明,算法有较高的分类精度.     

海量小差异图像高精度挖掘算法设计

针对传统的图像挖掘算法对小差异性图像特征挖掘精度不高的难题,提出一种基于不变矩特征提取的海量小差异图像高精度挖掘算法,通过构建图像的边缘检测和种子点分割模型,再采用小波降噪进行抗干扰处理,通过曲面约束进行相似图像的解释散点特征提取,根据海量小差异图像的旋转平移和尺度的不变性,实现对小差异图像的特征分辨和高精度挖掘。实验测试结果表明该算法能提高海量小差异图像挖掘的精度。     

基于小波变换和Zernike不变矩的CT/MRI医学图像处理技术

仅采用小波变换技术融合CT/MRI医学图像时,只单次剔除CT/MRI医学图像不重要信息,残留大量冗余信息。为此,结合小波变换和Zernike不变矩方法处理CT/MRI医学图像,基于Zernike不变矩边缘检测算法构建较为理想的阶跃边缘模型,融合修正的CT/MRI医学图像放大效应后,通过Zernike不变矩检测图像亚像素边缘,首次剔除部分不重要信息;在此基础上采用小波变换方法将CT/MRI医学图像分割成3N个高频子带和1个低频子带,再次剔除CT/MRI医学图像中的不重要信息,最后依据图像区域方差值确定融合值,实现多个源CT/MRI医学图像信息融合。经过实验分析发现,融合后的CT/MRI图像能精准体现融合前图像信息,清晰度显著高于融合前图像。     

基于正交多项式不变矩的图像描述

通过对大图像、小图像、噪声图像的重建,比较了泽尼克矩、正交的傅里叶-梅林矩.畸变的雅可比-傅里叶矩的图像描述能力,最后得出:畸变的雅可比-傅里叶矩有着最强的图像描述能力.在实验中还发现:在噪声图像的重建中,随着重建阶数的提高,图像的重建误差并不是一直减少,而是和有噪声图像一样,是一个先降后升的过程,并对此现象作了解释:在离散空间中连续正交多项式矩并不是完全意义上的正交,是这种正交误差造成了此现象.     

用离散傅立叶-切比雪夫正交矩描述图像

提出了一种基于离散Fourier正交函数系与离散Chebyshev正交多项式的图像正交矩。分析了连续正交矩与离散正交矩的原理与重构方法,提出Fourier-Chebyshev正交矩的构造原理与图像重构方法。对Legendre矩与Zernike矩两种经典的图像矩与离散Fourier-Chebyshev正交矩做了对比实验。实验结果表明,在复杂图像的处理中,该矩具有比传统连续函数矩更好的特征表达能力。     

Image watermarking using DNST-PHFMs magnitude domain vector AGGM-HMT

Invisibility, robustness and payload are three indispensable and contradictory properties for any image watermarking systems. Therefore, in this paper a novel statistical image watermark decoder based on robust discrete nonseparable Shearlet transform (DNST)-polar harmonic Fourier moments (PHFMs) magnitude and effective vector anisotropic generalized Gaussian mixtures (AGGM)-hidden Markov tree (HMT). We begin with a detailed study on the robustness and statistical characteristics of local DNST- PHFMs magnitudes of natural images. This study reveals the excellent robustness, highly non-Gaussian marginal statistics and strong dependencies of local DNST-PHFMs magnitudes. We also find that conditioned on their generalized neighborhoods, the local DNST-PHFMs magnitudes can be approximately modeled as anisotropic generalized Gaussian variables. Based on these findings, we model local DNST-PHFMs magnitudes using a vector AGGM-HMT that can capture all interscale, interdirection, and interlocation dependencies. Meanwhile, model parameters can be estimated effectively by using localization clues guided expectation–maximization (LCGEM) approach. Finally, we develop a new statistical image watermark decoder using the vector AGGM-HMT and maximum likelihood (ML) decision rule. Extensive experimental results show the superiority of the proposed watermark decoder over several state-of-the-art statistical watermarking methods and some approaches based on convolutional neural networks.     

Inverse and approximation problem for two-dimensional fractal sets

The geometry of fractals is rich enough that they have extensively been used to model natural phenomena and images. Iterated function systems (IFS) theory provides a convenient way to describe and classify deterministic fractals in the form of a recursive definition. As a result, it is conceivable to develop image representation schemes based on the IFS parameters that correspond to a given fractal image. In this paper, we consider two distinct problems: an inverse problem and an approximation problem. The inverse problem involves finding the IFS parameters of a signal that is exactly generated via an IFS. We make use of the wavelet transform and of the image moments to solve the inverse problem. The approximation problem involves finding a fractal IFS-generated image whose moments match, either exactly or in a mean squared error sense, a range of moments of the original image. The approximating measures are generated by an IFS model of a special form and provide a general basis for the approximation of arbitrary images. Experimental results verifying our approach will be presented.     

Multiresolution MAP despeckling of SAR images based on locally adaptive generalized Gaussian pdf modeling.

In this paper, a new despeckling method based on undecimated wavelet decomposition and maximum a posteriori MIAP) estimation is proposed. Such a method relies on the assumption that the probability density function (pdf) of each wavelet coefficient is generalized Gaussian (GG). The major novelty of the proposed approach is that the parameters of the GG pdf are taken to be space-varying within each wavelet frame. Thus, they may be adjusted to spatial image context, not only to scale and orientation. Since the MAP equation to be solved is a function of the parameters of the assumed pdf model, the variance and shape factor of the GG function are derived from the theoretical moments, which depend on the moments and joint moments of the observed noisy signal and on the statistics of speckle. The solution of the MAP equation yields the MAP estimate of the wavelet coefficients of the noise-free image. The restored SAR image is synthesized from such coefficients. Experimental results, carried out on both synthetic speckled images and true SAR images, demonstrate that MAP filtering can be successfully applied to SAR images represented in the shift-invariant wavelet domain, without resorting to a logarithmic transformation.     

Bayesian Wavelet-Based Image Denoising Using the Gauss–Hermite Expansion

The probability density functions (PDFs) of the wavelet coefficients play a key role in many wavelet-based image processing algorithms, such as denoising. The conventional PDFs usually have a limited number of parameters that are calculated from the first few moments only. Consequently, such PDFs cannot be made to fit very well with the empirical PDF of the wavelet coefficients of an image. As a result, the shrinkage function utilizing any of these density functions provides a substandard denoising performance. In order for the probabilistic model of the image wavelet coefficients to be able to incorporate an appropriate number of parameters that are dependent on the higher order moments, a PDF using a series expansion in terms of the Hermite polynomials that are orthogonal with respect to the standard Gaussian weight function, is introduced. A modification in the series function is introduced so that only a finite number of terms can be used to model the image wavelet coefficients, ensuring at the same time the resulting PDF to be non-negative. It is shown that the proposed PDF matches the empirical one better than some of the standard ones, such as the generalized Gaussian or Bessel K-form PDF. A Bayesian image denoising technique is then proposed, wherein the new PDF is exploited to statistically model the subband as well as the local neighboring image wavelet coefficients. Experimental results on several test images demonstrate that the proposed denoising method, both in the subband-adaptive and locally adaptive conditions, provides a performance better than that of most of the methods that use PDFs with limited number of parameters.      

Bayesian wavelet-based image denoising using the gauss-hermite expansion

The probability density functions (PDFs) of the wavelet coefficients play a key role in many wavelet-based image processing algorithms, such as denoising. The conventional PDFs usually have a limited number of parameters that are calculated from the first few moments only. Consequently, such PDFs cannot be made to fit very well with the empirical PDF of the wavelet coefficients of an image. As a result, the shrinkage function utilizing any of these density functions provides a substandard denoising performance. In order for the probabilistic model of the image wavelet coefficients to be able to incorporate an appropriate number of parameters that are dependent on the higher order moments, a PDF using a series expansion in terms of the Hermite polynomials that are orthogonal with respect to the standard Gaussian weight function, is introduced. A modification in the series function is introduced so that only a finite number of terms can be used to model the image wavelet coefficients, ensuring at the same time the resulting PDF to be non-negative. It is shown that the proposed PDF matches the empirical one better than some of the standard ones, such as the generalized Gaussian or Bessel K-form PDF. A Bayesian image denoising technique is then proposed, wherein the new PDF is exploited to statistically model the subband as well as the local neighboring image wavelet coefficients. Experimental results on several test images demonstrate that the proposed denoising method, both in the subband-adaptive and locally adaptive conditions, provides a performance better than that of most of the methods that use PDFs with limited number of parameters.     

基于三维纹理分割的高光谱图像异常检测

一幅复杂背景的高光谱图像可以看成是由不同纹理组合而成,纹理的统计特性可以近似用高斯分布来描述。采用纹理分割实现复杂背景的分解,从而突破异常大小和形状的限制。采用三维高斯马尔科夫场来描述高光谱图像背景的分布特性,利用最大似然估计得出模型参数,以此参数为特征进行纹理分割,在各纹理上计算像素的统计特性,进行异常检测。     

基于Zernike矩特征的电力设备红外图像目标识别

基于红外图像处理的电力设备及其关键构件识别是红外诊断技术的关键步骤,其难点之一在于设备图像的倾斜、缩放以及外形相似性导致的设备特征参量难以提取。本文以电流互感器、电压互感器、避雷器、隔离开关以及断路器五种外形相对接近的设备状态红外图像为研究对象,采用具有旋转与缩放不变性的Zernike矩作为待识别设备的特征,并基于相关向量机(Relevance Vector Machine,RVM)进行设备分类与识别。实验结果表明,该方法不受目标在图像中所处位置与倾斜角度影响,能够自定义生成大量高质量样本且有效分辨不同设备,设备识别准确率达到94.7%,验证了该方案的有效性与实用性。     

一种图像信息隐藏盲检测算法

论文提出了一种通用的图像信息隐藏盲检测算法。该算法从空间域和变换域提取图片的特征值,并将它们结合起来作为特征向量以判断图片是否含有隐藏信息,其中,空间域特征值是图片的梯度能量,小波域特征值是图片小波子带系数及其线性预测误差的特征函数的高阶统计量。实验显示该算法可以可靠地检测出含隐藏信息的图片。     

Generalized Gaussian mixture models as a nonparametric Bayesian approach for clustering using class-specific visual features

Recently, there has been a growing interest in the problem of learning mixture models from data. The reasons and motivations behind this interest are clear, since finite mixture models offer a formal approach to the important problems of clustering and data modeling. In this paper, we address the problem of modeling non-Gaussian data which are largely present, and occur naturally, in several computer vision and image processing applications via the learning of a generative infinite generalized Gaussian mixture model. The proposed model, which can be viewed as a Dirichlet process mixture of generalized Gaussian distributions, takes into account the feature selection problem, also, by determining a set of relevant features for each data cluster which provides better interpretability and generalization capabilities. We propose then an efficient algorithm to learn this infinite model parameters by estimating its posterior distributions using Markov Chain Monte Carlo (MCMC) simulations. We show how the model can be used, while comparing it with other models popular in the literature, in several challenging applications involving photographic and painting images categorization, image and video segmentation, and infrared facial expression recognition.     

应用支持向量机分类的多角度目标识别技术

综合应用图像的不变矩特征和支持向量机分类方法,提出了一种对于红外图像中多角度目标的识别方法。首先通过目标分割算法求得红外图像中目标的轮廓图像,然后从轮廓图像的Hu矩、Zernike矩和Fourier-Mellin矩中选取适当阶次的矩特征组成目标在特定视角范围内的不变性特征向量;对目标的视角范围进行适当划分以解决多角度引起的目标样本多样性,并在每个划分的视角范围内分别应用支持向量机的方法进行多目标分类。测试结果表明,本文提出的方法较好地实现了红外图像中多角度目标的识别问题,是一种有效的自动目标识别算法。