一类新的正交矩-Franklin矩及其图像表达

该文定义了一类以Franklin函数为核的正交矩,称之为Franklin矩.Franklin函数是一类完备正交一次样条函数系.传统的Legendre矩、Zernike矩等多项式矩,由于涉及高次多项式的计算,往往会导致计算不稳定,特征空间维数扩展受到制约.Franklin函数是正交的,相应的矩函数可以使得图像分解后的信息具有独立性,没有信息的冗余.而且,Franklin函数仅由一次分段多项式组成,在计算过程中,避免了高次多项式的计算,兼具复杂度低、数值稳定的优点.通过对图像的重构实验表明,Franklin矩比传统正交多项式矩具有更好的特征表达能力.     

正交旋转不变 V 矩及其在图像重建中的应用

定义在单位圆盘上的正交旋转不变矩函数(如Zernike矩) 具有非常广泛的应用. 本文基于一类正交分段多项式函数系--V系统, 构造了一种新型的矩函数, 称之为正交旋转不变V矩(简称为V矩). 除了正交性、旋转不变性之外, 由于V系统具有次数低、表达式简单的优点, V矩能够避免传统矩函数中高阶多项式的计算, 从而能够保证数值稳定性, 降低计算复杂度. 实验结果表明, V矩比传统的正交旋转不变矩具有更好的图像重建与图像检索结果.     

4阶均匀代数三角样条空间的拟Legendre基

为解决代数三角样条空间上正交基的理论问题,提出了4阶均匀代数三角样条空间上构造正交基的方法.该方法利用6阶C-B样条基函数构造一组辅助函数,并以这组辅助函数的二阶导数形式定义样条空间上的一组正交基,称为拟Legendre基.实例结果表明,使用这组正交基可以简化内积计算,便于最佳平方逼近问题求解.     

周期B样条基函数系数的并行算法

在现有周期B样条插值方法中,需要用迭代算法确定B样条基函数系数。针对现有方法的不足,建立B样条基函数系数的并行算法。首先构造周期区域的正交B样条基,得出正交B样条基函数系数的并行算法;进一步利用正交B样条基函数系数与B样条基函数系数的关系,得出B样条基函数系数的并行算法;最后推导二阶、三阶、四阶周期插值B样条基函数系数及插值点函数值的显式算式。实验证明了该方法在实现B样条基函数系数快速并行算法的同时保持了B样条基函数简单的函数关系。     

三维U系统矩与三维模型检索

提出3DU系统矩的定义,并将其应用于3D模型检索．作为一种可选定次数的完备正交多项式系统,U系统所定义的矩具有标准正交性、收敛性等优良性质．根据U系统理论实现了U1系统矩的高阶的快速计算,可以定义任意维数的特征空间．实验结果表明,3DU1系统矩优于几何矩和Zernike矩,具有更高的检索效率．     

任意阶正交B样条插值新方法

插值样条的一般解法是利用内点条件和边界条件得出一个样条系数的Ⅳ方程组,然后采用解线性代数方程组的方法求解.由于各插值基互不正交,插值系数相互耦合,计算步骤烦琐.新方法利用三角函数的正交性,构造了插值区域新的正交B样条插值基函数,并给出任意阶正交B样条插值基函数插值系数的通用计算公式.通过构造正交基,新方法解除了插值基以及插值系数之间的耦合,将样条插值问题从求解N×N矩阵简化为使用N个公式直接同时求解,实现了插值系数的并行计算.     

参数可调的通用半正交图像矩模型

目的 为了提高以正交多项式为核函数构造的高阶矩数值的稳定性,增强低阶矩抗噪和滤波的能力,将仅具有全局描述能力的常规正交矩推广到可以局部化提取图像特征的矩模型,从频率特性分析的角度定义一种参数可调的通用半正交矩模型。方法 首先,对传统正交矩的核函数进行合理的修正,以修正后的核函数（也称基函数）替代传统正交矩中的原核函数,使其成为修改后的特例之一。经过修正后的基函数可以有效消除图像矩数值不稳定现象。其次,采用时域的分析方法能够对图像的低阶矩作定量的分析,但无法对图像的高频部分（对应的高阶矩）作更合理的表述。因此提出一种时—频对应的方法来分析和增强不同阶矩的稳定性,通过对修正后核函数的频带宽度微调可以建立性能更优的不同阶矩。最后,利用构建的半正交—三角函数矩研究和分析了通用半正交矩模型的特点及性质。结果 将三角函数为核函数的图像矩与现有的Zernike、伪Zernike、正交傅里叶—梅林矩及贝塞尔—傅里叶矩相比,由于核函数组成简单,且其值域恒定在[-1,1]区间,因此在图像识别领域具有更快的计算速度和更高的稳定性。结论 理论分析和一系列相关图像的仿真实验表明,与传统的正交矩相比,在数值稳定性、图像重构、图像感兴趣区域（ROI）特征检测、噪声鲁棒性测试及不变性识别方面,通用的半正交矩性能及效果更优。     

用封闭周期域对称B样条基实现均匀样条逼近

针对现有求解均匀样条曲线控制顶点方法巾使用较为复杂的迭代算法的不足,提出均匀样条曲线控制顶点的快速并行算法.首先将基本B样条基平移建立对称B样条基(参数定义域为单位区间);然后利用复函数组{εk(v)=e1kv}的正交性构造封闭周期区域的正交B样条基,得出正交B样条基系数的显式并行计算公式;进一步,利用正交基系数与对称B样条基系数(样条曲线控制顶点)的关系,得出控制顶点的显式并行计算公式.最后以四阶与三阶样条逼近为例分析并行公式的快速算法,用从封闭及任意给定点列构造B样条曲线的2个例子证明了该算法的有效性.实验结果表明,文中算法为简单的B样条基增加了对称性,能够容易地实现快速并行计算,可提高构造大规模样条曲面的效率.     

基于小波的曲线任意放大

文章利用小波多分辨理论,提出了任意比例放大曲线的方法.该算法简单,放大后的曲线失真小且比较光滑.同时,在理论上讨论了适于放大缩小曲线的小波基应具有的特征.理论和实验结果均说明双正交B样条小波和半正交B样条小波具有较好的几何特征,适于本文算法.     

基于显著特征谱嵌入的三维模型相似性分析*

面向快速、高效的三维模型检索技术的迫切需求,提出基于显著特征谱嵌入的三维模型相似性分析方法.首先通过局部曲率及凸凹性检测,有效提取模型的显著特征点,构建模型的显著特征描述算子.然后基于拉普拉斯映射及谱分析原理进一步提取模型的内蕴形状特征.最后,结合薄板样条函数实现模型间的配准与相似性分析.通过实验验证文中方法不仅有效提高模型匹配的效率,而且能有效识别同一类模型的结构特征,同时对于残缺模型间的匹配具有较强的鲁棒性.     

Radial invariant of 2D and 3D Racah moments

In this paper, we introduce new sets of 2D and 3D rotation, scaling and translation invariants based on orthogonal radial Racah moments. We also provide theoretical mathematics to derive them. Thus, this work proposes in the first case a new 2D radial Racah moments based on polar representation of an object by one-dimensional orthogonal discrete Racah polynomials on non-uniform lattice, and a circular function. In the second case, we present new 3D radial Racah moments using a spherical representation of volumetric image by one-dimensional orthogonal discrete Racah polynomials and a spherical function. Further 2D and 3D invariants are extracted from the proposed 2D and 3D radial Racah moments respectively will appear in the third case. To validate the proposed approach, we have resolved three problems. The 2D/ 3D image reconstruction, the invariance of 2D/3D rotation, scaling and translation, and the pattern recognition. The result of experiments show that the Racah moments have done better than the Krawtchouk moments, with and without noise. Simultaneously, the mentioned reconstruction converges rapidly to the original image using 2D and 3D radial Racah moments, and the test 2D/3D images are clearly recognized from a set of images that are available in COIL-20 database for 2D image, and PSB database for 3D image.     

3D radial invariant of dual Hahn moments

In this work, we propose new sets of 2D and 3D rotation invariants based on orthogonal radial dual Hahn moments, which are orthogonal on a non-uniform lattice. We also present theoretical mathematics to derive them. Thus, this paper presents in the first case new 2D radial dual Hahn moments based on polar representation of an image by one-dimensional orthogonal discrete dual Hahn polynomials and a circular function. The dual Hahn polynomials are general case of Tchebichef and Krawtchouk polynomials. In the second case, we introduce new 3D radial dual Hahn moments employing a spherical representation of volumetric image by one-dimensional orthogonal discrete dual Hahn polynomials and a spherical function, which are orthogonal on a non-uniform lattice. The 2D and 3D rotational invariants are extracts from the proposed 2D and 3D radial dual Hahn moments respectively. In order to test the proposed approach, three problems namely image reconstruction, rotational invariance and pattern recognition are attempted using the proposed moments. The result of experiments shows that the radial dual Hahn moments have performed better than the radial Tchebichef and Krawtchouk moments, with and without noise. Simultaneously, the mentioned reconstruction converges quickly to the original image using 2D and 3D radial dual 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 PSB database for 3D image.

     

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

In this paper, we propose a new set of 2D and 3D rotation invariants based on orthogonal radial Meixner moments. We also present a theoretical mathematics to derive them. Hence, this paper introduces in the first case a new 2D radial Meixner moments based on polar representation of an object by a one-dimensional orthogonal discrete Meixner polynomials and a circular function. In the second case, we present a new 3D radial Meixner moments using a spherical representation of volumetric image by a one-dimensional orthogonal discrete Meixner polynomials and a spherical function. Further 2D and 3D rotational invariants are derived from the proposed 2D and 3D radial Meixner moments respectively. In order to prove the proposed approach, three issues are resolved mainly image reconstruction, rotational invariance and pattern recognition. The result of experiments prove that the Meixner moments have done better than the Krawtchouk moments with and without nose. Simultaneously, the reconstructed volumetric image converges quickly to the original image using 2D and 3D radial Meixner moments and the test images are clearly recognized from a set of images that are available in a PSB database.     

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.     

正交样条小波在图像压缩中的应用

给出了正交样条小波滤波器系数的计算方法和数据，将正交样条小波应用于图像压缩中，并与其他常用的小波如Daubechies小波和双正交小波进行了比较。结果表明，用正交样条小波进行图像压缩可以取得很好的效果。     

基于正交投影的三维模型相似性比较算法研究*

提出了一种基于二维正交投影图像的三维模型相似性比较算法。首先计算三维模型的二维正交投影图像,然后提取二维正交投影图像的边缘轮廓夹角特征,并比较这些二维正交投影图像的相似性,最后通过二维正交投影图像的轮廓相似性来获取三维模型的相似性。实验验证了该算法的可行性,并且其对于三维模型检索所要求的平移、旋转和缩放不变性具有较好的鲁棒性。     

正交样条小波在信号奇异性检测中的应用

本文给出了正交样条小波在信号奇异性检测中的应用,并且与其它常用的小波如Daubechies小波和双正交小波作了比较。实验表明,用正交样条小波进行信号奇异性检测不仅计算量小,而且效果好。