Construction of a complete set of similarity invariants using pseudo-Zernike moments

To resolve the completeness and independence of an invariant set derived by the traditional method, a systematic method for deriving a complete set of pseudo-Zernike moment similarity （translation, scale and rotation） invariants is described. First, the relationship between pseudo-Zernike moments of the original image and those of the image having the same shape but distinct orientation and scale is established. Based on this relationship, a complete set of similarity invariants can be expressed as a linear combination of the original pseudo-Zernike moments of the same order and lower order. The problem of image reconstruction from a finite set of the pseudo-Zernike moment invariants （PZMIs） is also investigated. Experimental results show that the proposed PZMIs have better performance than complex moment invariants.

Classification of MAG weld pool image based on moment invariants and fisher

In order to discover characteristics of various kinds of weld pool image and identify a single image, seven image features are extracted to describe the corresponding surface formation quality by the moment iavariants method. An image feature matrix is composed by the seven characteristics. Then the matrix is projected on a line through the Fisher criterion in order to entirely distinguish various kinds of image features. And finally, transforming a seven-dimensional problem into a one-dimensional problem has been done. Compared with the three kinds of samples included in the arc welding process and quality weld pool visual image database, the images are classified into the three kinds such as superior weld formation in the condition of optimal gas flow, poor weld formation image in the condition of insuffwient gas flow, inferior weld formation in the condition of too low gas flow. Experiments show that the Fisher classification method based on moment invariants can recognize various weld pool images effectively, and it achieves a correct recognizable rate of 100%.

Automatic Identification of Axis Orbit Based on Both Wavelet Moment Invariants and Neural Network

Axis orbit is an important characteristic to be used in the condition monitoring and diagnosis system of rotating machine. The wavelet moment has the invariant to the translation, scaling and rotation. A method, which uses a neural network based on Radial Basis Function (RBF) and wavelet moment invariants to identify the orbit of shaft centerline of rotating machine is discussed in this paper. The principle and its application procedure of the method are introduced in detail. It gives simulation results of automatic identification for three typical axis orbits. It is proved that the method is effective and practicable.

New method for recognition of circular traffic sign based on radial symmetry and pseudo-zernike moments

Recognizing various traffic signs,especially the popular circular traffic signs,is an essential task for implementing advanced driver assistance system.To recognize circular traffic signs with high accuracy and robustness,a novel approach which uses the so-called improved constrained binary fast radial symmetry（ICBFRS） detector and pseudo-zernike moments based support vector machine（PZM-SVM） classifier is proposed.In the detection stage,the scene image containing the traffic signs will be converted into Lab color space for color segmentation.Then the ICBFRS detector can efficiently capture the position and scale of sign candidates within the scene by detecting the centers of circles.In the classification stage,once the candidates are cropped out of the image,pseudo-zernike moments are adopted to represent the features of extracted pictogram,which are then fed into a support vector machine to classify different traffic signs.Experimental results under different lighting conditions indicate that the proposed method has robust detection effect and high classification accuracy.

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.

Automated visual inspection of surface defects based on compound moment invariants and support vector machine

The traditional inspection methods are mostly based on manual inspection which is very likely to make erroneous judgments due to personal subjectivity or eye fatigue, and can＇t satisfy the accuracy. To overcome these difficulties, we develop a machine vision inspection system. We first compare several kinds of methods for feature extraction and classification, and then present a real-time automated visual inspection system for copper strips surface （CSS） defects based on compound moment invariants and support vector machine （SVM）. The proposed method first processes images collected by hardware system, and then extracts feature characteristics based on grayscale characteristics and morphologic characteristics （Hu and Zernike compound moment invariants）. Finally, we use SVM to classify the CSS defects. Furthermore, performance comparisons among SVM, back propagation （BP） and radial basis function （RBF） neural networks have been involved. Experimental results show that the proposed approach achieves an accuracy of 95.8% in detecting CSS defects.

A Normalization Method of Moment Invariants for 3D Objects on Different Manifolds

3D objects can be stored in computer of different describing ways, such as point set, polyline, polygonal surface and Euclidean distance map. Moment invariants of different orders may have the different magnitude. A method for normalizing moments of 3D objects is proposed, which can set the values of moments of different orders roughly in the same range and be applied to different 3D data formats universally. Then accurate computation of moments for several objects is presented and experiments show that this kind of normalization is very useful for moment invariants in 3D objects analysis and recognition.

基于Pseudo-Zernike不变矩的PNN车牌汉字识别

基于不变矩理论,提出一种应用概率神经网络作为识别器的车牌汉字识别技术。利用Pseudo-Zernike矩特征的旋转不变性和良好的抗噪性能,将其作为车牌汉字识别的特征矢量,结合Pseudo-Zernike矩的快速算法和概率神经网络识别器快速学习和识别的性能,可适应实时环境下所获取的车牌汉字灰度图像的识别,具有较高的准确率,实验结果表明了该方法的有效性。

基于Pseudo-zernike不变矩的肺部图像特征提取及分类研究

为准确、快速地分析肺部图像的特征,提出了一种基于Pseudo-zernike不变矩的肺部特征分析方法。通过对肺部图像进行预处理和基于Pseudo-zernike不变矩的特征提取,利用具有良好识别性能的SVM分类器对提取的肺部图像特征值做分类研究。实验结果表明,该方法能够很好地表征肺部图像的特征,具有良好的分类准确率。

A NEW FAST ALGORITHM OF 2-D MOMENT INVARIANT BASED ON PROJECTION

In this paper,we advanced a new fast algorithm of 2-D moment in-variant based on image projection,by means of projection transformation it can com-press the information of a 2-D image into 1-D information.Thus,the amount ofcomputation and data size are decreased greatly and,moreover,the projection trans-formation,which is merely an operation of additions,is easier to be achieved onhardwares.The results of computer simulation proved the correctness and quicknessof our method.

Efficient Image Recognition Technique Using Invariant Moments and Principle Component Analysis

Image recognition is widely used in different application areas such as shape recognition, gesture recognition and eye recognition. In this research, we introduced image recognition using efficient invariant moments and Principle Component Analysis (PCA) for gray and color images using different number of invariant moments. We used twelve moments for each image of gray images and Hu’s seven moments for color images to decrease dimensionality of the problem to 6 PCA’s for gray and 5 PCA’s for color images and hence the recognition time. PCA is then employed to decrease dimensionality of the problem and hence the recognition time and this is our main objective. The PCA is derived from Karhunen-Loeve’s transformation. Given an N-dimensional vector representation of each image, PCA tends to find a K-dimensional subspace whose basis vectors correspond to the maximum variance direction in the original image space. This new subspace is normally lower dimensional (K N). Three known datasets are used. The first set is the known Flower dataset. The second is the Africans dataset, and the third is the Shapes dataset. All these datasets were used by many researchers.

水电机组故障诊断专家系统运行状态智能预测及故障识别

对水电设备进行智能预测可以将故障诊断提前至故障发生前,由此可预防不稳定运行对机组设备的损害,延长机组寿命。本文基于ARMA模型、匹配矩阵模型(MM)和Hu不变矩原理,针对水电机组需要重点监控的三类数据:自相关性较强参数、自相关性弱参数、轴心轨迹参数,开发了一套水电机组智能预测和故障识别系统。本系统自2019年实际装备以来预测结果和实际数据拟合良好,将各类潜在故障数据输入系统,系统均可预测并准确判断故障类型。综上,本系统可实现对故障的提前预测和诊断,有力保障水电机组的正常稳定发电。

基于不变矩卡尔曼预测的核相关跟踪算法改进

针对KCF(核相关滤波器)算法在目标发生严重遮挡时易出现目标丢失的问题,提出了一种结合不变距特征及卡尔曼预测的核相关滤波跟踪算法。利用模板图像和当前帧图像的不变矩特征计算相关量,然后根据不同遮挡情况下相关量值的变化情况设定目标遮挡判断机制。当目标未遮挡时KCF算法对目标继续跟踪;当目标被遮挡时利用卡尔曼滤波预测目标位置,目标复现后KCF算法利用预测的位置信息继续跟踪。最后采用OTB-2015数据集测试算法的有效性,实验结果表明,论文算法相对于KCF算法跟踪精度和跟踪成功率分别提高了12.95%和13.28%,有效改善了遮挡情况下的目标跟踪性能。

基于机器视觉的轴承识别与定位算法研究

针对运用视觉技术对轴承进行质量检测与尺寸测量时,能够准确地识别定位到目标轴承,提出一种基于机器视觉的轴承识别与定位算法。通过对采集到的轴承图像进行预处理,分割出目标图像并提取图像的外轮廓边缘特征;设置轴承的模板图像,结合图像的Hu不变矩特征对轴承进行识别匹配;通过最小二乘法对图像边缘点进行圆拟合并采用迭代法进行修正,通过计算圆心的位置坐标,实现对轴承的定位。实验结果为:轴承的识别匹配度在0~0.03之间,定位误差在0.5像素以内,满足系统对轴承的识别定位精度要求。

实时图像质心检测算法设计及其FPGA实现

为了克服通用处理器实现图像质心检测算法时处理速度慢,难以应用于高性能实时计算机视觉和图像处理系统的缺点,采用并行处理、流水线等设计优化技术,并通过现场可编程门阵列(field-programmable gate array,FPGA)设计了一种实时图像质心检测算法。首先,分析了实时图像质心检测算法,并在MATLAB上对其进行仿真验证;然后,使用处理器系统(processing system,PS)和可编程逻辑(programmable logic,PL)相结合的方式,在ZYNQ SOC FPGA芯片上设计、实现了实时图像质心检测算法;最后,进行了实时图像质心检测的硬件验证。试验结果表明,当采集图像分辨率为1024×768、帧率为60时,该设计能够正确实时地计算并标定图像的质心。

基于轮廓匹配算法的非标准件在线检测系统研究

随着机器视觉在标准件检测中的应用,基于机器视觉的非标准件检测研究受到越来越多的关注。对符合非标准件在线检测要求的视觉检测方法进行了研究。利用视觉系统,采集非标准件的图像。通过轮廓提取算法,提取工件的轮廓和相应的计算机辅助设计(CAD)模型的轮廓。计算工件轮廓和相应CAD模型轮廓的图形不变矩。比较图像不变矩的近似程度并判断该工件是否符合造型要求。在验证试验中,随机选取19件冲压模具镶块进行了检测方法的检验并在各工件间进行交叉验证。试验结果表明,检测正确率为100%、平均检测用时为1.4 s/件。以CAD图形为基准进行检测,无需事先收集工件的图像。基于图形不变矩进行相似度匹配,对检测环境要求低,硬件的可配置性强。由于只匹配轮廓信息,检测效率高,能满足非标准件在线检测的多样化应用需求。

基于因果不变表示的领域泛化算法

目的:针对当前领域泛化方法没有考虑到数据和标签之间潜在的因果机制,忽视特征间潜在的语义依赖关系的问题,提出了一种通过解耦因果不变表示(decoupling causal invariant representation,DCIR)实现的领域泛化方法。方法:首先利用矩对原数据进行因果干预,生成具有相同因果特征不同非因果特征的增强数据,模拟非因果特征的可变性。并且提出了解耦邻近因子分解算法(decoupling proximity factorization algorithm,DPFA),基于经验协方差矩阵,通过设置滑动窗口与削减值,获取干预前后的特征不变表示及特征之间潜在的语义依赖关系。结果:在Digits、PACS和CIFAR10-C 3种基准数据集上进行了广泛实验,提出的DCIR方法相较于现有的的基线模型,平均精度分别最高可达到13.6%、26.29%、18.9%的提升。结论:通过解耦因果不变表示实现的领域泛化方法能够有效提升模型的泛化性能。

通用径向复指数变换的图像鲁棒水印算法

针对图像水印对噪声和几何攻击的鲁棒性差的问题,文中提出了一种通用径向复指数变换的鲁棒水印算法。该算法通过调整选取的通用径向复指数变换的变换系数幅值大小并嵌入图像,在提取水印过程,通过水印图像计算变换系数,从而获得水印。实验结果显示了通用径向复指数变换的参数取不同值时,该水印算法对噪音、滤波和几何攻击的鲁棒性,验证了通用径向复指数变换的参数适当选取时,该水印算法对中心修剪攻击的鲁棒性更强。

基于纹理特征的钢轨表面缺陷检测

为提高钢轨表面缺陷识别的准确率,采用双边滤波的方法去除噪声,较好地保留缺陷边界;优化阙值计算方法,结合灰度直方图峰值的个数,采用相应的阙值计算方法,确保二值化结果可靠、有效;应用频域滤波优化,消除低频、细微的干扰区域;通过不变矩特征提取,有效地区分掉块和压溃缺陷,从而提高了钢轨表面缺陷识别的智能化程度和准确率。

Image Analysis by Two Types of Franklin-Fourier Moments

In this paper,we first derive two types of transformed Franklin polynomial:substituted and weighted radial Franklin polynomials.Two radial orthogonal moments are proposed based on these two types of polynomials,namely substituted Franklin-Fourier moments and weighted Franklin-Fourier moments(SFFMs and WFFMs),which are orthogonal in polar coordinates.The radial kernel functions of SFFMs and WFFMs are transformed Franklin functions and Franklin functions are composed of a class of complete orthogonal splines function system of degree one.Therefore,it provides the possibility of avoiding calculating high order polynomials,and thus the accurate values of SFFMs and WFFMs can be obtained directly with little computational cost.Theoretical and experimental results show that Franklin functions are not well suited for constructing higher-order moments of SFFMs and WFFMs,but compared with traditional orthogonal moments(e.g.,BFMs,OFMs and ZMs)in polar coordinates,the proposed two types of Franklin-Fourier Moments have better performance respectively in lower-order moments.