基于小波矩不变量的模式识别方法

为了得到一组局产最优的特征组,提出一种结合分散度和顺序前进法（ＳＦＳ）的特征选择方法。实验结果表明小液矩不变量比Ｚｅｒｎｉｋｅ矩不变量有更好的识别效果,尤其对于形体相似的物体,效果更明显。     

用矩不变量实现印鉴自动识别

本文提出的方法主要优点是无需对印鉴图象进行旋转、定位、配准、识别速度快，内存空间小，对印鉴的形状也无限制。实验结果表明，文中提出的方法有较高的识别率，可作为印鉴识别中的方法之一。     

Sluzek矩不变量方法的改进

本文首先分析Sluzek方法的思想即:减小方差而同时尽可能不减小不同类之间的均值差;然后分析了该方法的不足之处,证明了存在改进的可能性;并且提出了改进方法,即把目标分割为n个矩等分圆环,分别计算其矩不变量,从中选取某几个量作为识别指标;最后通过例子证实了本文方法的改进效果.     

四元数域彩色图像整体式水印算法

该文提出一种结合四元数变换域和四元数分解方法的整体式彩色图像水印算法。首先对彩色载体图像进行分块四元数傅里叶变换得到其频域矩阵,然后对频域单位小块进行四元数奇异值分解,根据分解得到四元数酉矩阵前若干列中对角线元素的幅值构造水印序列,并将水印隐藏到分解得到的实系数奇异值中。仿真实验表明,该文提出的水印方法不仅可以把嵌入水印带来的误差扩散到载体图像的各个颜色分量上,而且比传统的将彩色图像划分为各个单独色彩通道,分别进行水印嵌入的方法不可见性更好,并且,通过构造与奇异值酉矩阵相关的水印序列可以有效解决现有四元数水印算法存在的误检测率以及水印图像易伪造问题。     

基于离散四元数余弦变换的彩色图像数字水印技术

将离散四元数余弦变换(DQCT)应用于彩色图像的数字水印技术中,提出了一种在彩色图像中嵌入水印的新技术。应用四元数理论把彩色图像作为一整体进行余弦变换,在彩色宿主图像的DQCT域中实现彩色水印图像的嵌入。实验表明,该方法具有较好的抗压缩能力,对高斯噪声、椒盐噪声和缩放攻击具有较好的鲁棒性。     

基于四元数的彩色图像边缘检测研究

为了实现彩色图像边缘检测,建立了彩色图像自动边缘检测算法。对彩色图像基于四元数的表示方法进行描述,针对基于snake算子实现彩色图像边缘检测算法进行了研究。首先,根据彩色图像三原色的基本特征,利用四元数的矢量表示方法以及四元数基本运算方法进行描述;其次,介绍了snake算子的基本原理,检测过程等。最后,构造了双四元数和snake算子的彩色图像边缘检测算法。实验结果表明,利用文中算法获得的彩色图像边缘检测保留了原图像的自身信息,边缘信息完整。该算法基本满足彩色图像边缘检测的稳定可靠、信息完整、抗干扰能力强等要求。     

一种新的适合于目标识别的仿射不变量

针对仿射变换所造成的某些特征点提取不稳定,提出一种新的适合于目标识别的仿射不变量,它是基于特征点和几乎不受仿射变换影响的目标轮廓线．当目标无遮挡时,新不变量仅依赖于当前特征点和整个轮廓线;当目标有遮挡时,新不变量依赖于当前特征点、与它直接相邻的两个特征点以及它们之间的轮廓线．在已有的基于特征点的仿射不变量中,新不变量所依赖的特征点是最少的,可以很好地应用于特征点提取不稳定场合的目标识别．     

基于隐含多项式曲线仿射不变量的目标识别

目标物体的识别和匹配在计算机视觉、图像视频压缩与传输中都有重要应用.由于隐含多项式曲线对物体有良好的描述能力,因而用它识别和匹配目标物体是比较有效的.文章首先证明了隐含多项式曲线封闭有界的充要条件定理,接着基于隐含多项式曲线的二次分解性质,给出了目标物体仿射几何不变量的计算方法.实验证明这种基于首二次因子积的仿射几何不变量准确的描述了物体的特征,从而能较好的设别出复杂的,甚至缺失部分信息的目标物体.     

异步四元数神经网络模型的能量函数研究

本文证明了异步离散四元数神经网络模型在其动力学演化过程中，网络的能量函数单调递减，网络最终将稳定在一个平衡态上，存贮图像在能量函数空间中对应一能量极小点，为网络的稳定吸引子。     

Generic radial orthogonal moment invariants for invariant image recognition

As the variation of parameters in Jacobi polynomial, Jacobi–Fourier moments can form various types of orthogonal moments: Legendre–Fourier moments, Orthogonal Fourier–Mellin moments, Zernike moments, pseudo-Zernike moments, and so on. In this paper, we present a generic approach based on Jacobi–Fourier moments for scale and rotation invariant analysis of radial orthogonal moments, named Jacobi–Fourier moment invariants (JFMIs). It provides a fundamental mathematical tool for invariant analysis of the radial orthogonal moments since Jacobi–Fourier moments are the generic expressions of radial orthogonal moments. Theoretical and experimental results also show the superiority of the proposed method and its robustness to noise in comparison with some exist methods.     

An effective image retrieval method based on Kernel Density Estimation of collage error and moment invariants

In this paper, we propose a new method that combines collage error in fractal domain and Hu moment invariants for image retrieval with a statistical method - variable bandwidth Kernel Density Estimation (KDE). The proposed method is called CHK (KDE of Collage error and Hu moment) and it is tested on the Vistex texture database with 640 natural images. Experimental results show that the Average Retrieval Rate (ARR) can reach into 78.18%, which demonstrates that the proposed method performs better than the one with parameters respectively as well as the commonly used histogram method both on retrieval rate and retrieval time.     

基于相对点矩的SAR图像匹配算法

为了解决景象匹配导航系统中图像存在旋转误差以及遮档问题,提出了一种基于相对点矩的SAR图像匹配算法.Harris角点提取算于结合亚像素精确定位算法可以获得高精度的特征点坐标,而Hu不变矩具有平移、旋转、缩放(RTS)不变性,结合两者优点,本文首次提出了相对点矩的概念.相对点矩同样具有RTS不变性,可以实现任意旋转角度下...     

基于小波变换与图像不变矩的遥感图象匹配研究

小波变换具有数据压缩和检测局部突变的能力,而图像空间不变矩对于平移、旋转、比例缩放、灰度对比度变化具有不变性.两者优势的结合不但可以有效减少遥感图像匹配的计算量,而且还可以减弱对于图像采集平台拍摄方位、拍摄距离、角度气象条件等的依赖性,提高算法的实用性.     

Radial Tchebichef moment invariants for image recognition

Radial Tchebichef moments as discrete orthogonal moments in the polar coordinate have been successfully used in the field of image recognition. However, the scale invariant property of these moments has not been studied due to its complexity of the problem. In this paper, we present a method to construct a set of scale and rotation invariants extracted from radial Tchebichef moments, named radial Tchebichef moment invariants (RTMI). Experimental results show the efficiency and the robustness to noise of the proposed method for recognition tasks.     

改进的综合BTC颜色矩和DT-CWT的图像检索算法

特征提取是基于内容的图像检索中的关键技术.针对基于单一特征检索效果不理想的问题,提出一种改进的综合颜色和纹理特征的图像检索算法.该算法在YIQ颜色空间中进行特征提取,首先结合方块编码(BTC)的思想,提取颜色矩作为颜色特征;采用双树复小波变换(DT-CWT)提取纹理特征,融合两种特征并利用相似性度量方式进行图像检索.实验结果表明算法所提取的颜色、纹理特征更利于检索,使用综合特征检索的平均查准率比同类算法更高.     

Fast and effective Keypoint-based image copy-move forgery detection using complex-valued moment invariants

Copy-move forgery is one of the most common image tampering schemes, with the potential use for misleading the opinion of the general public. Keypoint-based detection methods exhibit remarkable performance in terms of computational cost and robustness. However, these methods are difficult to effectively deal with the cases when 1) forgery only involves small or smooth regions, 2) multiple clones are conducted or 3) duplicated regions undergo geometric transformations or signal corruptions. To overcome such limitations, we propose a fast and accurate copy-move forgery detection algorithm, based on complex-valued invariant features. First, dense and uniform keypoints are extracted from the whole image, even in small and smooth regions. Then, these keypoints are represented by robust and discriminative moment invariants, where a novel fast algorithm is designed especially for the computation of dense keypoint features. Next, an effective magnitude-phase hierarchical matching strategy is proposed for fast matching a massive number of keypoints while maintaining the accuracy. Finally, a reliable post-processing algorithm is developed, which can simultaneously reduce false negative rate and false positive rate. Extensive experimental results demonstrate the superior performance of our proposed scheme compared with existing state-of-the-art algorithms, with average pixel-level F-measure of 94.54% and average CPU-time of 36.25 s on four publicly available datasets.     

一种基于组合不变矩的新的舰船图像目标识别方法

利用图像的不变矩特征进行目标识别是一种有效的方法.通过比较Hu矩、仿射不变矩和小波不变矩的特点和适用条件,提出了一种运用部分Hu矩、仿射矩和小波矩组合来识别舰船图像目标的方法.仿真实验结果表明,该组合矩对于提高舰船图像目标的识别率是有效的.     

Quaternion pseudo-Zernike moments combining both of RGB information and depth information for color image splicing detection

The quaternion representation (QR) used in current quaternion-based color image processing creates redundancy when representing a color image of three components by a quaternion matrix having four components. In this paper, both RGB and depth (RGB-D) information are considered to improve QR for efficiently representing RGB-D images. The improved QR fully utilizes the four-dimensional quaternion domain. Using this improved QR, firstly we define the new quaternion pseudo-Zernike moments (NQPZMs) and then propose an efficient computational algorithm for NQPZMs through the conventional pseudo-Zernike moments (PZMs). Finally, we propose an algorithm for color image splicing detection based on the NQPZMs and the quaternion back-propagation neural network (QBPNN). Experimental results on four public datasets (DVMM, CASIA v1.0 and v2.0, Wild Web) demonstrate that the proposed splicing detection algorithm can achieve almost 100% accuracy with the appropriate feature dimensionality and outperforms 14 existing algorithms. Moreover, the comparison of six color spaces (RGB, HSI, HSV, YCbCr, YUV, and YIQ) shows that the proposed algorithm using YCbCr color space has the overall best performance in splicing detection.