考虑圆形特征边缘模糊和偏心误差修正的高精度相机标定方法

针对立体视觉系统采用圆形特征点标定时存在的空间圆形投影边缘模糊和偏心现象问题,利用改进Zernike矩和偏心误差修正进行圆心的高精度定位,以此提高相机参数的标定精度。首先考虑了由于立体视觉成像系统的标定场景光照强度不均匀引起的圆形特征投影图像边缘模糊的问题,引入高斯误差函数对边缘过渡段的灰度分布进行描述,建立了高斯边缘模型,并基于该模型计算投影图像的Zernike矩,然后利用改进Zernike矩实现高精度的圆形特征投影边缘像素坐标定位。此外,分析了影响圆形特征中心投影点和拟合圆心间偏差大小的因素,基于该分析对迭代拟合圆心进行偏差补偿使之逼近真实的圆心投影,最后通过所提算法对99圆形标志点进行圆心坐标提取并用于相机参数的标定。仿真实验表明,文中算法对投影图像边缘定位的精度以及圆心拟合的精度均高于传统的算法;实测实验中,基于圆心高精度坐标得到的相机标定参数对标准杆进行三维重建,长度测量精度比传统算法提高了30%。     

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。     

Target recognition in SAR images using radial Chebyshev moments

In this study, a new algorithm for classification of ground vehicles from standard synthetic aperture radar (SAR) images is proposed. Radial Chebyshev moment (RCM) is a discrete orthogonal moment that has distinctive advantages over other moments for feature extraction. Unlike invariant moments, its orthogonal basis leads to having minimum information redundancy, and its discrete characteristics explore some benefits over Zernike moments (ZM) due to having no numerical errors and no computational complexity owing to normalization. In this context, we propose to use RCM as the feature extraction mechanism on the segmented image and to compare results of the fused images with both Zernike and radial Chebyshev moments. Firstly, by applying different threshold target and shadow parts of each SAR images are extracted separately. Then, segmented images are fused based on the combination of the extracted segmented region, segmented boundary and segmented texture. Experimental results will verify that accuracy of RCM, which improves significantly over the ZM. Ten percent improvement in the accuracy is obtained by using RCM and fusion of segmented target and shadow parts. Furthermore, feature fusion improves the total accuracy of the classification as high as 6%.     

利用Zernike矩对心脏超声图进行光流场计算的邻域优化算法

本文针对心脏超声图的特性 ,介绍了一种计算二维光流场的邻域优化算法 ,通过对Zernike矩的正交性、旋转不变性等特性的分析 ,论证了Zernike矩在心脏超声图中作为运动不变特征的优越性 ,从而将传统光流场计算中的亮度不变假设推广到了亮度各阶Zernike矩不变 .另外 ,由于Zernike矩计算了像素点附近其余像素的贡献 ,使得单个像素点引起的误差对特征的影响大大减小 ,从而提高了算法的鲁棒性     

Robust image watermarking using local Zernike moments

In this work, we propose a robust image watermarking algorithm using local Zernike moments, which are computed over circular patches around feature points. The proposed algorithm locally computes Zernike moments and modifies them to embed watermarks, achieving robustness against cropping and local geometric attacks. Moreover, to deal with scaling attacks, the proposed algorithm extracts salient region parameters, which consist of an invariant centroid and a salient scale, and transmits them to the decoder. The parameters are used at the decoder to normalize a suspect image and detect watermarks. Extensive simulation results show that the proposed algorithm detects watermarks with low error rates, even if watermarked images are distorted by various geometric attacks as well as signal processing attacks.     

基于小波和Zernike矩的标尺靶亚像素检测算法

为解决特殊环境下基于光电成像的高精度测量系统中的低照度含噪靶面高精度测量的难题,提出了一种基于小波变换和Zernike矩相结合的亚像素边缘检测方法.该算法首先对获得的低照度含噪靶面图像采用基于小波变换局部模极大值的方法进行抗噪声的粗级边缘提取和去噪处理,获得靶面图像的像素级边缘和具有边缘保持特性的无噪声靶面图像,然后通过在边缘区域内求取Zernike矩的方法进一步提高边缘检测的精度,使得边缘检测的精度达到亚像素级,以便进一步提高测量精度.试验和仿真结果表明:该算法能够在特殊环境下实现对低照度含噪靶面的高精度边缘检测和测量,检测精度达到0.2pixel,具有较强的工程应用价值.     

改进Zernike矩亚像素边缘检测算法研究*

针对Ghosal算法检测出的边缘较粗以及手动反复调节阶跃强度阈值引起的效率低的问题,提出了一种改进算法.首先,推导出模板系数,并计算得出Zernike矩.其次,利用推导出的公式计算出距离阈值和边缘阈值,利用Otsu法计算得到最佳阶跃强度.最后,通过设计三组实验,来验证改进算法的有效性.实验证明,改进算法能够更加有效地检测出图像的边缘,减少伪边缘的存在并且细化了边缘,提高了定位精度,同时降低了算法的执行时间.     

基于等积环投影与Zernike矩的快速模板匹配

为增强工业级机器视觉系统的实时性和稳定性,本文提出一种基于等积环投影与Zernike矩的具有旋转不变性的快速模板匹配方法,该方法采用由粗到精匹配策略,在粗匹配阶段以等积环投影向量作为特征进行匹配得到候选点集,再基于Zenrike矩进行精确匹配.主要创新点为提出复杂度低、抗噪性强和具有旋转不变性的等积环投影向量特征,并利用圆对称性在八分圆法基础上进一步降低Zernike矩计算量.经理论分析和实验对比验证,此法在保障匹配精度前提下,其匹配速度胜于当前同类最优算法,且对高斯噪声及线性亮度变化有强鲁棒性.     

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.     

基于InSAR的三维地形匹配导航技术的研究与实现

研究了一种以干涉合成孔径雷达(InSAR)信息为基础的三维地形匹配导航系统,该系统采用基于3-D Zernike矩的三维地形匹配算法,同时针对3-D Zernike矩在地形匹配中计算实时性差的问题进行了改进。为验证系统的有效性和算法性能,搭建了基于VC++和OpenSceneGraph的三维可视化软件仿真平台。仿真结果表明,基于3-D Zernike矩的三维地形匹配算法定位精确度高,对地形的适应能力强,算法的实时性问题得到了良好解决,系统具有较高的工程实用价值。     

基于改进的Canny算子和Zernike矩的亚像素边缘检测方法

为满足电荷藕合器件(CCD)图像测量系统的快速、高精度测量要求,提出了一种基于改进Canny算子和Zemike矩的图像亚像素边缘检测新方法.该算法先利用改进的Canny算子进行边缘点的粗定位,在像素级上确定边缘点的坐标和梯度方向,然后再根据构造的边缘点向量和参考阈值,用Zernike矩算法对边缘点进行亚像素的重新定位,...     

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.     

基于视觉图像的石英摆片参数精密测量方法

为了解决检测石英摆片易划伤和提高检测的速度、精度,提出了利用视觉图像进行石英摆片平面几何参数测量的非接触测量方法.该方法通过视觉系统对石英摆片图像进行摄取, 由Sobel算子对图像进行单像素精度的边缘初始位置定位,进而利用改进的Zernike矩亚像素定位算法实现对目标边缘的精确定位,最后通过最小二乘法拟合边缘点得出石英摆片的平面几何参数.实验结果表明:该方法稳定性好,测量精度高,定位精度优于0.1 pixel,可实现石英摆片平面几何参数的精密测量.     

基于改进爬山算法的微小零件亚像素级定位

为提高微小零件亚像素级定位效果,采用改进爬山算法。将目标区域向外扩展获得调焦窗口跟踪漂移图像,边界由像距和视角决定;然后优化爬山搜索算法得到最优收敛解,二维图像信息熵构成调焦图像评价函数;改进Zernike矩算法,通过高阶矩的模代替边缘参数,减少了计算量,卷积窗口矩阵构成Zernike矩的差值,提高定位精确度;最后给出了算法流程。实验结果显示,改进Zernike矩偶模板比奇模板边缘亚像素定位误差小,相比空间矩算法、多项式拟合算法、Zernike矩算法、区域生长算法和模板匹配算法,对规则形状定位误差均值分别减少了43.24%,21.62%, 32.43%,27.03%和56.76%;对不规则形状定位误差均值分别减少了39.02%,20.15%,26.83%,24.39%和51.22%。本文算法定位精确度较高。     

基于Zernike矩和SVM的不变性目标识别

在图像目标识别中,目标图像平移、尺度和旋转不变性是一个重要前提.文中采用稳定性好的低阶几何矩特征实现目标的平移、尺度不变性变换;然后利用Zemike矩提取目标的旋转不变性特征;最后在目标不变特征空间通过支持向量机(SVM)分类器实现目标识别.实验证实了算法的有效性.     

Exact Zernike and pseudo-Zernike moments image reconstruction based on circular overlapping blocks and Chamfer distance

This study aims to explore a novel approach to reconstruct multi-gray-level images based on circular blocks reconstruction method using two exact and fast moments: Zernike (CBR-EZM) and pseudo-Zernike (CBR-EPZM): An image is first divided into a set of sub-images which are then reconstructed independently. We also introduced Chamfer distance (CD) to capitalize on the use of discrete distance instead of Euclidean one. The combination of our methods and CD leads to CBR-EZM-CD and CBR-EPZM-CD methods. Obviously, image partitioning offers significant advantages, but an undesirable circular blocking effect can occur. To mitigate this effect, we have implemented overlapping feature to our new methods leading to OCBR-EZM-CD and OCBR-EPZM-CD, by exploiting neighborhood information of the circular blocks. The main motivation of this novel approach is to explore new applications of Zernike and pseudo-Zernike moments. One of the fields is feature extraction for pattern recognition: Zernike and pseudo-Zernike moments are well known to capture only the global features, but thanks to the circular block reconstruction, we can now use those moments to extract also local features.     

Analysis of principle deviation of subpixel Zernike moments operator

In original Zernike moments subpixel edge operator, ideal step edge model is chosen for calculating four parameters of a pixel point, and therefore principle deviation is caused by edge model. In order to discuss the effect of principle deviation on edge location, sampled edge model is chosen for calculating edge parameters, principle deviation of edge translation l is derived based on Zernike moments edge detection theory, and modified formula of l is given for Zernike moments operator with masks of 5×5 size. Both theory analysis and testing result demonstrate that principle deviation is zero when edge translation l is limited in central pixel of a sampled window. In another case, the modification of l should be considered, which results in edge location accuracy of non-subpixel level for this subpixel edge operator.     

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.