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.

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.

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.

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.

Information Moment for Chinese Character Recognition

Moment invariants firstly introduced by M. K Hu in 1962, has some shortcomings. After counting a large number of statistical distribution information of Chinese characters,the authors put forward the concept of information moments and demonstrate its invariance to translation,rotation and scaling.Also they perform the experiment in which information moments compared with moment invaiants for the effects of similar Chinese characters and font recognition.At last they show the recognition rate of 88% by information moments,with 70% by moment inariants.

An Invariance Principle for ρ^--Mixing Random Sequences

In this paper,we establish an invariance principle for ρ^--mixing random sequences under some moment condition.The result improve and extend the relevant result of Wu（2003）.

An Improved Algorithm for Pseduo-Jacobi-Fourier Moments

Image moments have been used in many research fields of the engineering. However, the related computation of invariant moments mostly adopted the polar coordinate system, which not only increase the computational load, but also cause large quantized error. To solve this problem, an improved algorithm to compute Pseudo-Jacobi-Fourier moments in the Cartesian coordinate system is proposed in this paper. The experimental results show that the reconstructed image with improved PJFM’s has more advantages than polar coordinate system, such as more information, fewer moments, less time consuming. And the recognition rate of the microscopic images of 8 helminth eggs was also higher than in polar coordinate system.

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。

Mosaic of Printed Circuit Board Image Based on Gaussian-Hermite Moment Invariants

Quality inspection of a PCB （Printed Circuit Board） always requires us to stitch some separated images into an integral one. However, during image acquisition, some environmental influences such as vibration, noise and illumination will cause image degradation. An efficient image mosaic method has been urgently required to obtain a high-quality PCB panorama. Hence, an image mosaic method based on Gaussian-Hermite moments is presented in this paper. The characteristic points in the neighborhood of a PCB are represented by Gaussian-Hermite moment in- variants. They are characterized by independence to translation or rotation transformations. Meanwhile, such feature representation shows better noise robustness. Experimental results show that the proposed method produces a qualified mosaic of PCB image.

Radial Hahn Moment Invariants for 2D and 3D Image Recognition

Recently, orthogonal moments have become efficient tools for two-dimensional and three-dimensional（2D and 3D） image not only in pattern recognition, image vision, but also in image processing and applications engineering. Yet, there is still a major difficulty in 3D rotation invariants. In this paper, we propose new sets of invariants for 2D and 3D rotation, scaling and translation based on orthogonal radial Hahn moments. We also present theoretical mathematics to derive them. Thus, this paper introduces in the first case new 2D radial Hahn moments based on polar representation of an object by one-dimensional orthogonal discrete Hahn polynomials, and a circular function. In the second case, we present new 3D radial Hahn moments using a spherical representation of volumetric image by one-dimensional orthogonal discrete Hahn polynomials and a spherical function. Further 2D and 3D invariants are derived from the proposed 2D and 3D radial Hahn moments respectively, which appear as the third case. In order to test the proposed approach, we have resolved three issues： the image reconstruction, the invariance of rotation, scaling and translation, and the pattern recognition. The result of experiments show that the Hahn moments have done better than the Krawtchouk moments, with and without noise. Simultaneously, the mentioned reconstruction converges quickly to the original image using 2D and 3D radial Hahn moments, and the test images are clearly recognized from a set of images that are available in COIL-20 database for 2D image, and Princeton shape benchmark（PSB） database for 3D image.

Rotation Scaling and Translation Invariants of 3D Radial Shifted Legendre Moments

This paper proposes a new set of 3D rotation scaling and translation invariants of 3D radially shifted Legendre moments. We aim to develop two kinds of transformed shifted Legendre moments： a 3D substituted radial shifted Legendre moments （3DSRSLMs） and a 3D weighted radial one （3DWRSLMs）. Both are centered on two types of polynomials. In the first case, a new 3D ra- dial complex moment is proposed. In the second case, new 3D substituted/weighted radial shifted Legendremoments （3DSRSLMs/3DWRSLMs） are introduced using a spherical representation of volumetric image. 3D invariants as derived from the sug- gested 3D radial shifted Legendre moments will appear in the third case. To confirm the proposed approach, we have resolved three is- sues. To confirm the proposed approach, we have resolved three issues： rotation, scaling and translation invariants. The result of experi- ments shows that the 3DSRSLMs and 3DWRSLMs have done better than the 3D radial complex moments with and without noise. Sim- ultaneously, the reconstruction converges rapidly to the original image using 3D radial 3DSRSLMs and 3DWRSLMs, and the test of 3D images are clearly recognized from a set of images that are available in Princeton shape benchmark （PSB） database for 3D image.

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。