基于局部Zernike矩的RST不变水印

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,而使常规水印检测失败,提出了一种基于图像局部Zernike矩的RST不变水印算法.利用图像归一化后的Zernike矩幅度具有RST不变的性质,将由Zernike矩重构的水印图像在空域嵌入原始图像的局部中,并提取该区域的Zernike矩幅度矢量作为水印矢量.水印检测时,计算局部区域的Zernike矩,并与已知Zernike矩矢量计算均方误差根(RMSE)判断水印存在与否.与使用图像全局Zernike矩相比,使用局部矩使得水印具有更好的旋转不变性,同时水印对于缩放、JPEG压缩和噪声等攻击也具有较好的检测性能.     

基于Tchebichef矩与颜色矢量角度的鲁棒图像哈希算法

为了改善哈希算法对旋转等内容修改的鲁棒性,设计了径向Tchebichef矩耦合颜色矢量角度的鲁棒图像哈希算法。引入2D离散小波变换(DWT),对图像的颜色矢量角度实施分解,获取对应的4个子带,将其低频系数作为结构特征。采用径向Tchebichef矩计算预处理图像的Tchebichef矩,提取全局特征。通过组合这2种特征,以形成中间哈希序列。设计加密函数,对中间哈希完成加密,得到目标哈希序列。计算初始目标与待识别图像的哈希序列之间的l2范数距离,并将其与预设阈值作比较,完成图像内容的真伪判别。测试数据表明：相对于已有的哈希算法而言,所提算法具备更高的鲁棒性,可以对旋转、颜色与缩放等内容修改做出准确识别。     

指数矩耦合局部敏感哈希的图像伪造检测算法

为了解决图像伪造检测算法在定位篡改内容时忽略了不同颜色分量之间的关系,使其对图像特征描述不足,导致其识别结果中存在误检与漏检等情况,本文提出了基于多元指数矩与欧式局部敏感哈希的图像伪造检测算法。引入高斯低通滤波,消除可疑图像中的噪声;随后,将滤波图像分割为一系列的重叠圆形子块,改善其对旋转等内容操作的鲁棒性;基于四元指数矩(QEM),计算每个圆形子块的QEM,以提取相应的鲁棒特征,将其组合成特征矢量;采用欧式局部敏感哈希机制,生成每个子块对应的哈希序列;计算任意两个相邻哈希元素之间的空间距离,通过与预设阈值比较,完成所有子块的匹配;最后,借助随机样本一致性方法来剔除错误匹配,通过形态学处理,定位出篡改内容。实验数据显示：较已有的伪造检测方法而言,在各种几何内容修改下,所提算法具有更高的伪造检测准确性。     

基于伪Zernike矩的抗几何攻击图像水印

针对几何攻击容易使常规水印算法检测失败的问题,利用图像伪Zernike矩的幅度具有旋转不变的性质,提出了一种抗几何攻击图像水印算法(GWPZM).首先计算图像的伪Zernike矩,选择适宜于嵌入水印的矩进行幅度改变量测试.筛选出最优矩集合,根据水印比特自适应地修改集合中的矩,并对差矢量进行伪Zernike矩重构,通过将重构图像在空域迭加到原始图像中嵌入水印.水印检测时,首先计算受攻击图像的伪Zernike矩,以最优矩集合作为密钥,通过与原始图像比较矩的幅度提取水印.实验结果表明,GWPZM算法能够抵抗旋转、缩放攻击、二者组合攻击以及JPEG压缩、噪声等常规攻击.     

基于多尺度分析矩特征的人脸表情识别

Zernike不变矩具有对噪声不敏感,正交等特性,是表情的有效表征方法,高阶Zernike矩包含更多图像信息,对表情分类的作用更大.但是高阶矩的计算复杂度很大,很难达到快速表情识别的要求.本文利用小波变换对表情图像进行多尺度分析,从低频子图像中计算其Zernike矩作为判别特征进行表情识别.通过小波变换,一方面可以对图像降维,降低计算复杂度;另一方面,小波变换的去噪性能使得识别效果更好.实验表明,基于多尺度分析Zernike矩特征的方法优于单独使用小波变换或Zernike矩特征方法的识别效果.     

基于Blob-Harris特征区域和NSCT-Zernike的鲁棒水印算法

为了有效抵抗水印图像的几何攻击,该文提出了一种基于Blob-Harris特征区域和非下采样轮廓波变换(NSCT)和伪Zernike矩的鲁棒水印算法。首先原始图像进行两层非下采样Contourlet变换后提取其低频图像,然后利用Blob-Harris检测算子对低频图像进行特征点提取,根据各个特征点的特征尺度确定其特征区域,优化筛选出稳定且互不重叠的特征区域并将其四周补零,得到稳定的互不重叠的方形特征区域作为水印嵌入区域,最后计算每一个方形特征区域的Zernike矩,将水印信息嵌入在量化调制正则化Zernike矩的幅值当中。实验结果表明,Lena图峰值信噪比达到40 dB以上时,本文算法对常规图像处理以及缩放、旋转、剪切等几何攻击和组合攻击都有相对较强的鲁棒性。     

基于聚类分析和SIFT的图像感知哈希算法

本文提出一种基于尺度不变特征变换(SIFT)和聚类分析(K-means)相结合的感知哈希算法。其中,基于不变特征变换用于提取图像的局部稳定特征点,聚类分析用来对特征数据经行压缩并得到图像感知哈希。图像的相似性通过感知哈希值之间的汉明距离来测评。实验数据分析表明,该算法在图像尺度变换、各种几何攻击、仿射变换以及JPEG等攻击中具有较好的稳健率。     

基于Blob-Harris特征区域和NSCT-Zernike的鲁棒水印算法

为了有效抵抗水印图像的几何攻击,该文提出了一种基于Blob-Harris特征区域和非下采样轮廓波变换(NSCT)和伪Zernike矩的鲁棒水印算法.首先原始图像进行两层非下采样Contourlet变换后提取其低频图像,然后利用Blob-Harris检测算子对低频图像进行特征点提取,根据各个特征点的特征尺度确定其特征区域,优化筛选出稳定且互不重叠的特征区域并将其四周补零,得到稳定的互不重叠的方形特征区域作为水印嵌入区域,最后计算每一个方形特征区域的Zernike矩,将水印信息嵌入在量化调制正则化Zernike矩的幅值当中.实验结果表明,Lena图峰值信噪比达到40 dB以上时,本文算法对常规图像处理以及缩放、旋转、剪切等几何攻击和组合攻击都有相对较强的鲁棒性.     

基于Harris特征点和伪Zernike矩的鲁棒水印算法

抵抗几何攻击的鲁棒性是目前数字水印技术中的热点亦是难点。文中以Harris检测算子和伪Zernike矩为理论基础,提出一种有效抵抗几何攻击的鲁棒水印算法。通过提取归一化图像的Harris特征点,选取部分稳定特征点确定圆形区域并计算伪Zernike矩,并量化调制伪Zernike矩的幅值来嵌入水印信息。实验结果表明,本算法对于抗几何攻击具有较好的鲁棒性,同时也能抵抗常规的信号处理攻击。     

一种改进的亚像素边缘检测方法

为满足测量系统的快速、高精度的图像测量要求,提出一种基于传统Zernike矩结合小波变换实现亚像素边缘检测的方法。算法先用小波模极大值原理对图像粗定位,再用Zernike矩算法对边缘进行亚像素定位,并用最大类熵阈值法计算阈值,实现图像的亚像素边缘检测。实验表明,该方法抗噪性能好,且检测精度更高,能达0.1~0.2个像素。     

Image alignment based perceptual image hash for content authentication

Perceptual image hash is an emerging technology that is closely related to many applications such as image content authentication, image forging detection, image similarity detection, and image retrieval. In this work, we propose an image alignment based perceptual image hash method, and a hash-based image forging detection and tampering localization method. In the proposed method, we introduce an image alignment process to provide a framework for image hash method to tolerate a wide range of geometric distortions. The image hash is generated by utilizing hybrid perceptual features that are extracted from global and local Zernike moments combining with DCT-based statistical features of the image. The proposed method can detect various image forging and compromised image regions. Furthermore, it has broad-spectrum robustness, including tolerating content-preserving manipulations and geometric distortion-resilient. Compared with state-of-the-art schemes, the proposed method provides satisfactory comprehensive performances in content-based image forging detection and tampering localization.     

基于Zernike矩的低信噪比红外目标检测方法

针对低信噪比红外目标图像,分析了Zernike矩的基本原理、计算方法和旋转不变性,提出了基于Zernike正交矩的低信噪比红外目标检测方法,并比较了模板匹配、Hu矩、Zernike矩方法的目标识别效果.理论分析与实验验证了所提方法的有效性.     

一种新的Pseudo-Zernike矩的快速算法

Zernike矩因具有正交性和旋转不变性而广泛应用于模式识别、图像分析等领域.Pseudo-Zernike 矩具有与Zernike 矩相似的性质,但它较Zernike 矩具有更好的抗噪声性.由于pseudo-Zernike矩的复杂性,相关的快速算法的研究尚未得到很好的解决.本文根据pseudo-Zernike矩自身的特点,推导了一种快速有效的计算方法.     

基于综合特征和SVM相关反馈的图像检索

提出综合纹理、颜色和形状特征的图像检索方法。首先采用Gabor小波计算ROIs(Regions of Interest)的位置和数目;然后在ROIs中,使用Gabor小波提取纹理特征,采用YUV空间直方图和颜色矩表示颜色特征,使用Zernike矩提取形状特征。为了提高图像检索的准确度,最后采用基于支持向量机(SVM)的相关反馈算法。实验结果表明,提出的方法具有较好的检索性能。     

Image analysis by Krawtchouk moments

A new set of orthogonal moments based on the discrete classical Krawtchouk polynomials is introduced. The Krawtchouk polynomials are scaled to ensure numerical stability, thus creating a set of weighted Krawtchouk polynomials. The set of proposed Krawtchouk moments is then derived from the weighted Krawtchouk polynomials. The orthogonality of the proposed moments ensures minimal information redundancy. No numerical approximation is involved in deriving the moments, since the weighted Krawtchouk polynomials are discrete. These properties make the Krawtchouk moments well suited as pattern features in the analysis of two-dimensional images. It is shown that the Krawtchouk moments can be employed to extract local features of an image, unlike other orthogonal moments, which generally capture the global features. The computational aspects of the moments using the recursive and symmetry properties are discussed. The theoretical framework is validated by an experiment on image reconstruction using Krawtchouk moments and the results are compared to that of Zernike, pseudo-Zernike, Legendre, and Tchebyscheff moments. Krawtchouk moment invariants are constructed using a linear combination of geometric moment invariants; an object recognition experiment shows Krawtchouk moment invariants perform significantly better than Hu's moment invariants in both noise-free and noisy conditions.     

A new Zernike moments based technique for camera identification and forgery detection

In multimedia forensics, it is important to identify those images that were captured by a specific camera from a given set of N data images as well as detecting the tampered region in these images if forged. This paper presents a new technique based on Zernike moments feature extraction for blindly classifying correlated PRNU images as well as locating the tampered regions in image under investigation. The proposed clustering algorithm is based on estimating the Zernike moments and applying a hierarchical clustering for classification. The forgery detection algorithm is based on picking up the peak Euclidean distance between the Zernike moments vector of blocks of the scaled-down forged image and its corresponding ones in the capturing camera PRNU. As Zernike moments are scale and rotational invariant, its feature when computed using scaled-down PRNU images lead to considerable computation time saving. Simulation examples are given to verify the effectiveness of the proposed techniques when compared to other state-of-the-art techniques even in case of very weakly correlated PRNU.     

Image analysis by Tchebichef moments

This paper introduces a new set of orthogonal moment functions based on the discrete Tchebichef polynomials. The Tchebichef moments can be effectively used as pattern features in the analysis of two-dimensional images. The implementation of the moments proposed in this paper does not involve any numerical approximation, since the basis set is orthogonal in the discrete domain of the image coordinate space. This property makes Tchebichef moments superior to the conventional orthogonal moments such as Legendre moments and Zernike moments, in terms of preserving the analytical properties needed to ensure information redundancy in a moment set. The paper also details the various computational aspects of Tchebichef moments and demonstrates their feature representation capability using the method of image reconstruction.     

一种基于Zemike矩和稳态遗传算法的遥感图像匹配方法

该文提出了一种基于稳态遗传算法和Zernike矩的遥感图像匹配方法。将稳态遗传算法快速而有效的全局寻优特点应用于遥感图像的匹配定位。考虑到旋转畸变,采用遥感图像的Zernike矩作为相似性量度,实现遥感图像的匹配。实验结果表明,该算法可以克服图像的旋转畸变,在保证一定的匹配精度下,具有较高的匹配效率。