基于Krawtchouk矩和小波变换的数字水印算法

提出了一种基于Krawtchouk矩和小波变换的抵抗几何攻击的内容认证水印算法。该方法首先对图像进行一次小波分解，然后计算其低频成分的Krawtchouk低阶矩不变量来构建水印。水印提取过程简单，只需计算所得图像的几个低阶Krawtchouk矩不变量。文中给出了实验结果，并与Alghoniemy提出的基于几何矩不变量的数字水印算法进行了比较。结果表明，该方法简单、有效，对旋转、缩放、剪切、组合攻击等几何攻击以及JPEG压缩攻击具有更高的稳健性。     

可有效抵抗一般性几何攻击的数字水印检测方法

以回归型支持向量机理论为基础, 结合性能稳定的伪Zernike矩和Krawtchouk矩, 提出了一种可有效抵抗一般性几何攻击的强鲁棒数字图像水印检测算法. 该算法首先选取图像的低阶Krawtchouk矩作为特征向量, 然后利用SVR对几何变换参数进行训练学习并对待检测图像进行数据预测, 最后对其进行几何校正并提取水印信息. 仿真实验结果表明, 该数字图像水印检测算法不仅具有较好的不可感知性, 而且对常规信号处理和一般性几何攻击均具有较好的鲁棒性.     

鲁棒性数字图像水印技术

数字水印技术作为版权保护的重要手段已经成为了研究的热点,但是实用的鲁棒性数字水印技术不多,特别是抗几何攻击的水印算法不多.文中利用Krawtchouk不变矩对平移、旋转和缩放的不变性以及Krawtchouk矩良好的局部特性和重构性能,根据水印的容量自适应地改变某些相对稳定的低阶矩来嵌入水印,并用Rijndael加密和Arnold置乱等技术以及构造的图像特征不变矩阵生成相关的二值逻辑表来进行版权认证.实验证明,该算法不仅对JPEG压缩、裁剪、噪声等常见攻击有较强的免疫力,而且对平移、旋转、缩放等几何攻击也具有较强的免疫力.     

基于Krawtchouk不变矩和SVD-LWT的双重水印研究

为提高数字图像水印的鲁棒性和透明性,提出了一种基于Krawtchouk不变矩、奇异值分解(SVD)和提升小波变换(LWT)的彩色图像双水印算法。该算法首先从载体RGB图像中提取绿色分量进行两级提升小波变换,利用Krawtchouk不变矩对平移、旋转和缩放的不变性以及Krawtchouk矩良好的局部特性和重构性,计算其低频分量的Krawtchouk低阶矩不变量构造水印系统;然后对其变换后的中频分量奇异值进行分解,嵌入混沌置乱后的水印图像的奇异值作为另一水印系统。仿真实验结果表明,该算法不仅有良好的不可见性,而且对常见攻击和几何攻击都具有很好的鲁棒性,并具有一定的应用价值。     

Krawtchouk矩旋转不变性的分析

矩是基于区域的形状描述子,相对于基于轮廓的描述子例如傅立叶、链码描述子等,对于不连通的图像形状描述和对噪声的鲁棒性等方面有着更良好的性能.正交矩又可分为连续正交矩和离散正交矩,Krawtchouk矩是离散正交矩中的一种,和连续正交矩不同,基于离散正交矩本身的离散特性,更适合于对数字图像的处理.但同其他离散矩一样,Krawtchou矩并不具备天然的几何不变性(旋转、缩放和平移),这也从一定程度上限制了Krawtchouk矩的应用.为使Krawtchouk矩得到更广泛的应用,对Krawtchouk旋转不变矩的构造进行详细分析和实验,比较出更适合用于浮游植物的Krawtchouk旋转不变矩.     

基于Krawtchouk矩的运动目标分类

针对视频监控系统,为了实现对运动目标的有效分类,提高其分类准确率,提出一种基于Krawtchouk矩不变量特征的运动目标分类技术.提取目标图像的低阶Krawtchouk矩不变量来描述目标,确保目标在平移、缩放、旋转条件下的不变性,选用K-means均值聚类算法进行数据聚类,达到准确分类的效果.通过实验验证了该方法的有效性,与应用Hu矩不变量进行分类的结果进行比较,表明了该方法具有较好的分类效果.     

基于Krawtchouk不变矩的复制-粘贴篡改盲检测算法

针对数字图像检测中一类常见的复制-粘贴图像篡改,提出一种基于小波变换和径向Krawtchouk不变矩的盲检测算法。算法利用小波变换提取图像的低频分量,对低频分量分块提取径向Krawtchouk不变矩特征,这种特征描述方式对图像旋转后处理具有鲁棒性,然后将特征向量进行按字典排序,并结合数学形态学进行图像复制篡改区域的检测和定位。实验表明该算法能有效地定位出复制和粘贴的图像篡改区域,并对粘贴区域旋转操作具有很强的鲁棒性。     

抗几何攻击图像水印研究

研究图像数字水印安全问题.抵抗几何攻击一直是水印算法研究的难点和重点,在图像版权保护过程的研究,针对旋转、缩放和平移等几何攻击能够破坏水印检测的同步性,容易使常规水印算法检测不稳定的问题,利用图像伪Zernike矩的幅度具有旋转不变的性质,提出了一种可有效抵抗几何攻击的数字图像水印新方法.利用图像归一化技术将原始载体映射到几何不变空间内,以消除缩放、平移之影响,然后结合伪Zernike矩的旋转不变特性,计算出归一化图像的伪Zernike矩.最后选取部分低阶伪Zernike矩,并采纳量化调制策略将水印信息嵌入到伪Zernike矩幅值中.仿真结果表明,数字图像水印算法不仅具有良好的透明性,而且具有较强的抗攻击能力,为设计提供了可靠依据.     

基于Fourier-Mellion变换抗仿射变换水印算法

现有的数字水印技术大多难以抵抗几何变换类攻击,如旋转、平移和尺度变换等,特别是仿射变换（旋转、平移和尺度变换混合的多种攻击）,其中一个最主要的原因是：几何变换虽然并未去除图像中的水印信息,但却使水印的检测与嵌入之间失去同步,从而导致水印检测的失效。针对以上原因提出了一种基于图像矩和傅里叶梅林变换抗仿射不变性水印算法。对于平移造成的几何攻击,在水印检测之前利用原始图像的几何矩估计水印图像所经过的几何变换,根据估计的参数对水印图像进行相应的校正和水印检测。对于旋转和尺度变换造成几何攻击,通过对数极坐标系能将笛卡尔坐标系中的旋转和缩放变换为平移的性质,结合Fourier变换来抵抗旋转和缩放攻击。实验结果证明,该方法可以获得良好的图像视觉效果,同时对于几何攻击具有很好的鲁棒性。     

基于局部Zernike矩的RST不变零水印算法

旋转、缩放和平移(RST)等几何攻击能够破坏水印检测的同步性,使得水印检测失败。针对此问题,提出了一种基于图像局部Zernike矩的RST不变零水印算法。Zernike矩的幅度具有旋转不变性,再结合图像归一化,使其具有缩放和平移不变性。由于Zernike矩的图像重构效果不理想且重构过程中复杂度高,因此水印嵌入选择零水印方案。实验结果表明,该算法对旋转、缩放和平移(RST)的攻击具有很好的鲁棒性,同时对JPEG压缩、加噪、滤波等常见的图像处理操作也具有很好的鲁棒性。     

结合SIFT和Krawtchouk矩不变量的图像配准方法

提出了一种基于SIFT和Krawtchouk矩不变量的图像配准方法。通过SIFT关键点检测方法检测关键点;对每个关键点计算其邻域的Krawtchouk矩不变量,并将其构成描述关键点的特征向量;计算关键点特征向量之间的欧氏距离找出相匹配的关键点对。实验结果表明,该算法的配准性能与标准SIFT算法相当,而运算速度比标准SIFT算法有较大程度提高。     

A robust image watermarking algorithm using SVR detection

Geometric distortion is known as one of the most difficult attacks to resist. Geometric distortion desynchronizes the location of the watermark and hence causes incorrect watermark detection. According to the Support Vector Regression (SVR), a new image watermarking detection algorithm against geometric attacks is proposed in this paper, in which the steady Pseudo-Zernike moments and Krawtchouk moments are utilized. The host image is firstly transformed from rectangular coordinates to polar coordinates, and the Pseudo-Zernike moments of the host image are computed. Then some low-order Pseudo-Zernike moments are selected, and the digital watermark is embedded into the cover image by quantizing the magnitudes of the selected Pseudo-Zernike moments. The main steps of watermark detecting procedure include: (i) some low-order Krawtchouk moments of the image are calculated, which are taken as the eigenvectors; (ii) the geometric transformation parameters are regarded as the training objective, the appropriate kernel function is selected for training, and a SVR training model can be obtained; (iii) the Krawtchouk moments of test image are selected as input vector, the actual output (geometric transformation parameters) is predicted by using the well trained SVR, and the geometric correction is performed on the test image by using the obtained geometric transformation parameters; (iv) the digital watermark is extracted from the corrected test image. Experimental results show that the proposed watermarking detection algorithm is not only robust against common signal processing such as filtering, sharpening, noise adding, and JPEG compression etc., but also robust against the geometric attacks such as rotation, translation, scaling, cropping and combination attacks, etc.     

利用角点坐标计算图象的矩不变量

本文提出了一种直接利用角点坐标计算形状的方法，并导出了低阶矩的闭合形式，最后给出了计算M．K．Hu提出的七个矩不变量的算法。该算法计算的是经典不变矩的精确值，但大大地筒化了计算。     

基于多特征高效索引的图像检索

为了提高图像检索的准确率和速度,提出了一种多特征组合的图像检索算法。在颜色空间非均匀量化的基础上,利用改进的颜色聚合向量方法提取图像的颜色特征;基于改进的灰度共生矩阵提取纹理特征参数;利用Krawtchouk矩不变量提取图像的形状特征;基于贡献度聚类并建立特征索引库。融合上述特征计算图像间的相似度,使用特征索引对图像进行快速检索。实验结果表明,提出算法的检索精度有较大提高,能快速检索出用户所需的图像。     

统一Hu矩及在电视图像目标识别中的应用

分析了在离散状态下比例因子对不变矩特征的影响,扩展了Hu提出的基于区域的不变矩和Chen提出的基于边界的不变矩,构造了一种新的不变矩,统一了基于区域和边界的矩不变量公式,并满足离散状态下的比例不变性,比传统的不变矩更具一般性。将其应用到电视图像目标识别中,仿真结果表明,不变矩不变性好,识别率高,实时性好,具有一定的应用前景。     

Image representation using separable two-dimensional continuous and discrete orthogonal moments

This paper addresses bivariate orthogonal polynomials, which are a tensor product of two different orthogonal polynomials in one variable. These bivariate orthogonal polynomials are used to define several new types of continuous and discrete orthogonal moments. Some elementary properties of the proposed continuous Chebyshev–Gegenbauer moments (CGM), Gegenbauer–Legendre moments (GLM), and Chebyshev–Legendre moments (CLM), as well as the discrete Tchebichef–Krawtchouk moments (TKM), Tchebichef–Hahn moments (THM), Krawtchouk–Hahn moments (KHM) are presented. We also detail the application of the corresponding moments describing the noise-free and noisy images. Specifically, the local information of an image can be flexibly emphasized by adjusting parameters in bivariate orthogonal polynomials. The global extraction capability is also demonstrated by reconstructing an image using these bivariate polynomials as the kernels for a reversible image transform. Comparisons with the known moments are performed, and the results show that the proposed moments are useful in the field of image analysis. Furthermore, the study investigates invariant pattern recognition using the proposed three moment invariants that are independent of rotation, scale and translation, and an example is given of using the proposed moment invariants as pattern features for a texture classification application.     

Novel moment invariants for improved classification performance in computer vision applications

A novel set of moment invariants based on the Krawtchouk moments are introduced in this paper. These moment invariants are computed over a finite number of image intensity slices, extracted by applying an innovative image representation scheme, the image slice representation (ISR) method. Based on this technique an image is decomposed to a several non-overlapped intensity slices, which can be considered as binary slices of certain intensity. This image representation gives the advantage to accelerate the computation of image's moments since the image can be described in a number of homogenous rectangular blocks, which permits the simplification of the computation formulas. The moments computed over the extracted slices seem to be more efficient than the corresponding moments of the same order that describe the whole image, in recognizing the pattern under processing. The proposed moment invariants are exhaustively tested in several well known computer vision datasets, regarding their rotation, scaling and translation (RST) invariant recognition performance, by resulting to remarkable outcomes.