基于四元数Zernike矩的RST不变彩色图像水印

提出一种基于四元数Zernike矩( QZM)的RST( Rotation, Scale, Translation)不变彩色图像水印方案,利用缩放平移归一化后的QZM模值具有旋转、缩放和平移不变性,采用抖动量化调制的方法在QZM模值中嵌入水印信息。该水印算法可以把水印嵌入所带来的误差扩散到红、绿、蓝3幅分量图像中,实验表明该算法在具有良好的不可感知性的同时对旋转、缩放和平移等几何攻击具有较好的鲁棒性。     

基于最小外接圆补偿机制的形状图像检索

提出了一种新的基于最小外接圆补偿机制的二值形状图像检索算法,其不仅提取了目标区域的特征,而且补偿了目标最小外接圆下的背景区域特征。提取常用于描述区域特征的Hu不变矩和Zernike不变矩作为图像特征,不变矩特征具有平移、缩放、旋转不变性。图像相似度用归一化特征向量的欧氏距离表示。该方法不仅计算简单,而且可有效补偿人眼的视觉感受。通过大量实验表明,该算法较仅基于目标区域的检索算法取得了更好的检索精度和回召率。     

用于视觉传感器的有理系数小波滤波器的设计

有理系数小波滤波器的设计可以通过降低小波函数消失矩阶数来实现,但设计方程中出现的自由变量的取值需要在一定的范围之内,因为需要满足Daubechies紧支集条件。归纳总结了有理系数小波滤波器的设计过程,并以长度为4的有理系数小波滤波器设计为例,对如何确定设计方程中自由变量的取值范围进行了讨论,以得到适用于视觉传感器图像处理算法硬件实现的有理系数小波滤波器。     

一维统计量的椭圆检测法

数字影像中目标的识别是十分关键的,该文以椭圆作为研究对象,在对于基于二维随机变量的以HU不变矩为代表的不变矩算法上,提出了基于动差方法建立一维的随机统计量,并且证明该随机变量的三级动差和四级动差均为一常量。由于本算法只使用一维随机变量且该统计量又很容易获得,成为了其突现的优越性。     

基于小波不变矩的医学图像配准技术研究

图像配准是对取自不同时间、不同传感器或不同视角的同一场景的两幅图像或者多幅图像匹配的过程,是图像融合、目标探测识别和计算机视觉等技术的重要基础,主要用于消除来自不同传感器的图像中目标的位置差异.提出了一种新图像匹配算法,新算法以提升格式小波变换为基础,利用图像多级小波分解后近似分量的轮廓相似性进行图像的快速配准.实验结果表明,新方法高效精确.     

Different Estimation Methods for Constant Stress Accelerated Life Test under the Family of the Exponentiated Distributions

The Accelerated Life Testing (ALT) has been used for a long time in several fields to obtain information on the reliability of product components and materials under operating conditions in a much shorter time. One of the main purposes of applying ALT is to estimate the failure time functions and reliability performance under normal conditions. This paper concentrates on the estimation procedures under ALT and how to select the best estimation method that gives accurate estimates for the reliability function. For this purpose, different estimation methods are used, such as maximum likelihood, least squares (LS), weighted LS, and probability weighted moment. Moreover, the reliability function under usual conditions is predicted. The estimation procedures are applied under the family of the exponentiated distributions in general, and for the exponentiated inverted Weibull (EIW) as a special case. Numerical analysis including simulated data and a real life data set is conducted to compare the performances between these four methods. It is found that the ML method gives the best results among other estimation methods. Finally, a comparison between the EIW and the Inverted Weibull (IW) distributions based on a real life data set is made using a likelihood ratio test. It is observed that the EIW distribution can provide better fitting than the IW in case of ALT. Copyright © 2015 John Wiley & Sons, Ltd.     

An efficient approach for statistical moments estimation of structural response based on a novel adaptive hybrid dimension-reduction method

Statistical moments estimation is one of the main topics for the analysis of a stochastic system, but the balance among the accuracy, efficiency, and versatility for different methods of statistical moments estimation still remains a challenge. In this paper, a novel point estimate method (PEM) based on a new adaptive hybrid dimension-reduction method (AH-DRM) is proposed. Firstly, the adaptive cut-high-dimensional model representation (cut-HDMR) is briefly reviewed, and a novel AH-DRM is developed, where the high-order component functions of the adaptive cut-HDMR are further approximated by multiplicative forms of the low-order component functions. Secondly, a new point estimation method (PEM) based on the AH-DRM is proposed for statistical moments estimation. Finally, several examples are investigated to demonstrate the performance of the proposed PEM. The results show the proposed PEM has fairly high accuracy and good versatility for statistical moments estimation.     

基于Radon变换不变矩和小波提升的水印算法

提出了一种基于Radon变换不变矩和提升小波的抗几何攻击水印算法。该方法首先对图像进行一次提升小波分解,然后计算其低频成分的Radon变换不变矩来构建水印系统。水印提取过程简单,只需计算所得图像的几个Radon变换不变矩不变量。文章给出了实验结果,并与基于几何矩不变量的算法进行了比较。经过仿真实验证明,该方法对于旋转,缩放,平移等攻击具有很好鲁棒性的同时,对于普通的加噪,滤波,JPEG压缩攻击也具有很好的鲁棒性,且具有极低误检率。     

Orthogonal variant moments features in image analysis

Moments are statistical measures used to obtain relevant information about a certain object under study (e.g., signals, images or waveforms), e.g., to describe the shape of an object to be recognized by a pattern recognition system. Invariant moments (e.g., the Hu invariant set) are a special kind of these statistical measures designed to remain constant after some transformations, such as object rotation, scaling, translation, or image illumination changes, in order to, e.g., improve the reliability of a pattern recognition system. The classical moment invariants methodology is based on the determination of a set of transformations (or perturbations) for which the system must remain unaltered. Although very well established, the classical moment invariants theory has been mainly used for processing single static images (i.e. snapshots) and the use of image moments to analyze images sequences or video, from a dynamic point of view, has not been sufficiently explored and is a subject of much interest nowadays. In this paper, we propose the use of variant moments as an alternative to the classical approach. This approach presents clear differences compared to the classical moment invariants approach, that in specific domains have important advantages. The difference between the classical invariant and the proposed variant approach is mainly (but not solely) conceptual: invariants are sensitive to any image change or perturbation for which they are not invariant, so any unexpected perturbation will affect the measurements (i.e. is subject to uncertainty); on the contrary, a variant moment is designed to be sensitive to a specific perturbation, i.e., to measure a transformation, not to be invariant to it, and thus if the specific perturbation occurs it will be measured; hence any unexpected disturbance will not affect the objective of the measurement confronting thus uncertainty. Furthermore, given the fact that the proposed variant moments are orthogonal (i.e. uncorrelated) it is possible to considerably reduce the total inherent uncertainty. The presented approach has been applied to interesting open problems in computer vision such as shape analysis, image segmentation, tracking object deformations and object motion tracking, obtaining encouraging results and proving the effectiveness of the proposed approach.