Geometric invariant features in the Radon transform domain for near-duplicate image detection

Radon transform has been widely used in content-based image representation due to its excellent geometric properties. In this paper, we propose a family of geometric invariant features based on Radon transform for near-duplicate image detection. According to the theoretical analysis between geometric operations (translation, scaling, and rotation) and Radon transform, we present a geometric invariant feature model. Based on the feature model, we developed four kinds of geometric invariant features. In addition, a uniform sampling technique is introduced to combine different features. The comprehensive performance of the combined feature is better than that of a single one. Extensive experiments show that the proposed features are robust, not only to rotation and scaling, but also to other operations, such as compression, noise contamination, blurring, illumination modification, cropping, etc., and achieve strong competitive performance compared with the state-of-the-art image features.     

基于时间保持能量图和Radon变换的步态识别

Radon变换把图像从坐标空间映射到Radon空间,因其可以保存频率信息而被应用在步态识别算法中。主要从频率角度入手,着力提高基于Radon变换的步态识别算法的识别正确率,提出了基于时间保持能量图的Radon变换步态识别算法。传统的步态能量图是对步态周期中经过归一化的人体轮廓图求算术平均而得到的步态特征表示,最近提出的时间保持能量图在保持步态能量图的优点的基础上,保留了步态序列的时间信息,在改进的步态周期检测算法的基础上,提出将时间保持能量图和Radon变换结合到一起的步态识别算法。也对结合不同数据空间的特征如频率、形状等做了初步探讨。     

基于Radon变换的视频测速算法

为了从视频监测图像中自动提取车辆行驶速度,提出了一种基于Radon变换的视频测速算法。根据车辆行驶轨迹的时空特点,利用道路交通标线为图像与道路建立距离映射关系,简化了现场标定的条件和过程;利用时间堆栈成像方法,建立了车辆行驶轨迹的时空关系;基于Radon变换的图像处理,实现了行驶车辆的视频测速技术。算法具有较好的鲁棒性,与传统方法相比,计算复杂性也要低很多。     

Generalized Fuzzy Hough Transform for Detecting Arbitrary Shapes in a Vague and Noisy Image

A generalized Hough transform is an effective method for an arbitrary shape detection in a contour image. However, the conventional generalized Hough transform is not suitable for a noisy and blurred image. This paper describes a generalized fuzzy Hough transform which is derived by fuzzifying the vote process in the Hough transform. The present generalized fuzzy Hough transform enables a detection of an arbitrary shape in a very noisy, blurred, and even distorted image. The effectiveness of the present method has been confirmed by some preliminary experiments for artificially produced images and for actual digital images taken by an ordinary digital camera     

基于形状和纹理的图像检索

构造了Radon变换的不变量，提出一种新的基于形状和纹理的图像检索方法。对小波图像边缘提取Radon不变量作为形状特征，同时提取小波各频道能量作为纹理特征。然后将形状特征和纹理特征分别进行高斯归一化，计算图像形状和纹理的相似度。最后，利用形状和纹理相似度的加权和进行图像检索。试验结果表明该方法对噪声具有较强的鲁棒性，具有尺度、平移和旋转不变性。     

非线性Radon变换及其在人脸识别中的应用

探讨抛物线、双曲线、椭圆3种非线性Radon变换及其性质,分析它们之间的关系,并将所述3种非线性Radon变换应用于人脸识别．通过对这3种非线性Radon变换及其性质研究得出,当抛物线、双曲线及椭圆的形状参数趋于无穷大时,图像抛物线Radon变换与线性Radon变换相等,双曲线Radon变换与椭圆Radon变换相等;同时,非线性Radon变换具有降噪功能和表达图像纹理特征的特点．文中将受噪声污染的人脸图像分别表示为3种非线性Radon变换下的特征矩阵,并结合PCA算法应用于人脸识别．实验结果表明非线性Radon变换在人脸识别中的有效性．     

Generalizing the Hough transform to detect arbitrary shapes

The Hough transform is a method for detecting curves by exploiting the duality between points on a curve and parameters of that curve. The initial work showed how to detect both analytic curves^(1,2) and non-analytic curves,⁽³⁾ but these methods were restricted to binary edge images. This work was generalized to the detection of some analytic curves in grey level images, specifically lines,⁽⁴⁾ circles⁽⁵⁾ and parabolas.⁽⁶⁾ The line detection case is the best known of these and has been ingeniously exploited in several applications.^(7,8,9)We show how the boundaries of an arbitrary non-analytic shape can be used to construct a mapping between image space and Hough transform space. Such a mapping can be exploited to detect instances of that particular shape in an image. Furthermore, variations in the shape such as rotations, scale changes or figure ground reversals correspond to straightforward transformations of this mapping. However, the most remarkable property is that such mappings can be composed to build mappings for complex shapes from the mappings of simpler component shapes. This makes the generalized Hough transform a kind of universal transform which can be used to find arbitrarily complex shapes.     

A fast digital Radon transform—An efficient means for evaluating the Hough transform

A fast digital Radon transform based on recursively defined digital straight lines is described, which has the sequential complexity of N² log N additions for an N × N image. This transform can be used to evaluate the Hough transform to detect straight lines in a digital image. Whilst a parallel implementation of the Hough transform algorithm is difficult because of global memory access requirements, the fast digital Radon transform is vectorizable and therefore well suited for parallel computation. The structure of the fast algorithm is shown to be quite similar to the FFT algorithm for decimation in frequency. It is demonstrated that even for sequential computation the fast Radon transform is an attractive alternative to the classical Hough transform algorithm.     

基于双谱不变量的图像识别研究

提出了一种基于双谱变换的图像识别方法。为降低计算的复杂性,利用Radon变换将图像数据变换到一维空间,通过计算投影数据的双谱构造出具有比例和平移不变性的特征。提出了一种基于循环相关的旋转不变性算法以使得该算法具有真正的不变量特性,该文还提出了一种改进的整体平均算法,使得该不变量计算速度大为提高,通过仿真分析与讨论,指出了该算法的有效性。     

形变冗余的改进GHT目标双层定位方法研究

经典广义Hough变换可以较好地解决非形变目标定位问题,但对于存在形变的目标定位问题存在不少困难。为解决该问题,同时考虑如何提高检测定位速度与减少存储消耗, 在粗定位与精确定位两级框架下提出基于改进GHT形变目标两层定位快速算法。粗定位过程首先利用图像的局域二进制模式的直方图特征对图像进行全局搜索,检测出目标大致范围 ;在精确定位过程中,通过建立模板图像边缘像素的R表,使待检测图像边缘像素在约束的参数范围内依据该R表进行局部搜索,并通过一个投票结果散布窗对得到的累积矩阵进行 集中化处理,达到把每一点邻域内投票结果集中在某点的目的,从而给出最后的检测结果。实验表明,本文算法能够较好的解决一定程度形变目标的定位问题,同时减少了运算时 间以及存储消耗,检测稳定性高,具有一定应用意义。     

Generalized Discrete Radon Transforms and Their Use in the Ridgelet Transform

We introduce and study a new class of Radon transforms in a discrete setting for the purpose of applying them to the ridgelet and curvelet transforms. We give a detailed analysis of the p-adic case and provide a closed-form formula for an inverse of the p-adic Radon transform. We give conditions for a scaled version of the generalized discrete Radon transform to yield a tight frame, and discuss a direct Radon matrix method for the implementation of a local ridgelet transform. We then study the effectiveness of some types of the generalized Radon transforms in reducing a type of noise known as speckle that is present in synthetic aperture radar (SAR) imagery. Flavia Colonna received the M.A. degree and the Ph.D degree in mathematics from the University of Maryland (College Park) in 1980 and 1985, respectively. She was an assistant professor at the University of Bari (Italy) until she joined the faculty of George Mason University in 1986, where she is currently professor of mathematics. Her research interests include discrete harmonic analysis, integral geometry, potential theory, and classical complex function theory. Glenn R. Easley received the B.S. degree (with honors) and the M.A. degree in mathematics from the University of Maryland, College Park, in 1993 and 1996, respectively, and the Ph.D. degree in computational science and informatics from George Mason University in 2000. Since 2000, he has been working for System Planning Corporation in signal and image processing. His research interests include computational harmonic analysis, wavelet analysis, synthetic aperture radar, deconvolution and computer vision.This revised version was published online in June 2005 with correction to CoverDate     

基于Finite Ridgelet变换的图像线性特征提取

线性特征是图像的一种重要局部特征,它常常决定图像中目标的形状。线性特征的提取在图像匹配、目标描述与识别以及运动估计、目标跟踪等领域具有十分重要的意义。常用的线性特征检测方法有Radon变换和Hough变换,但检测曲线复杂度会很高。本文提出一种多尺度几何分析的线性特征检测方法,该方法以finite ridgelet理论为基础,结合正交小波变换对线性特征进行提取。Finite ridgelet变换对于含有直线奇异的多变量函数具有良好的逼近特性,能够获得连续空间函数的稀疏表达,同时具有区域平滑性、很好的可逆性和去冗余性。实验结果表明,本方法即使在背景复杂的环境下也具有良好的检测效果。     

基于颜色空间分布特征的图像检索

目前,基于颜色特征的图像检索大多是以图像的颜色直方图作为颜色特征,这种图像检索方法有简单高效的优点,但丢失了颜色的空间分布信息,该文从CT图像重建的理论中得到启发,将对一幅图像从几个方向的投影图作为这幅图像的颜色特征分布。为进一步减少检索时运算的数据量,对图像做小波分解,然后对分解后图像的低频子带做Radon变换得到颜色空间分布的特征向量,并根据这个特征进行检索,实验表明,当检索图像中有明显的颜色目标时,该方法比传统的颜色直方图法更精确,颜色空间性更强,而且检索用时更短。     

The Radon transform and its application to shape parametrization in machine vision

A general method is presented that uses the Radon transform as a means of defining a two-dimensional transform space in which information about different, analytically defined shape primitives in an edge image space may be encoded simultaneously. Examples are given illustrating how the shape-indicative distributions within the transform space may be deduced. The results show that each set of coded information is transparent to any other and that each shape-indicative distribution may be located using a convolution mask peculiar to that distribution.     

一种基于近似有限Ridgelet变换的SAR图像分割方法

由Donoho等提出的有限正交Ridgelet变换成功应用于高噪声图像的边缘检测和分割,但由于有限Radon的“缠绕”现象使得在该方法图像的重构时产生边缘的“混叠”和“洞”,影响了边缘检测和图像分割的质量。论文结合Wedgelet变换提出了基于“自然直线”的近似有限Ridgelet变换从根本上克服了这些缺陷和解决了离散Radon变换的图像重构问题。最后将这一方法用于SAR图像分割,并取得了满意的结果。     

超复数空间彩色边缘检测器的实现

提出超复数空间彩色图像边缘检测器的一种新实现方法--基于超复数空间旋转变换的彩色边缘检测。分析结果表明，Sangwine提出的超复数空间彩色图像边缘检测算法实质是一种色差图像边缘检测算法。通过把彩色图像映射到超复数空间进行处理，产生色差边缘图像。为了解决超复数空间的卷积运算问题，文中对超复数空间旋转运算进行推广，提出了旋转变换算子，把超复数空间中的矢量卷积运算转化为标量运算，极大地降低了计算复杂度。实验结果表明了本方案的正确性。     

基于Radon变换的纹理图像多尺度不变量分析算法

为了更好地进行图像纹理分析,提出了一种基于Radon变换的不变量纹理识别算法。该算法首先利用Radon变换将图像投影到1维空间,然后通过对投影数据进行一种平移和比例不变的自适应小波变换来构造出具有比例和平移不变性的图像的特征矩阵。这种通过对特征矩阵进行多尺度分析得到的多尺度能量特征不但具有平移、比例和旋转不变性,而且反映出了纹理图像在不同尺度上的能量分布特征。在特征提取完成以后,即可利用支撑向量机进行分类。同其他方法的比较说明,该算法可较好地描述纹理特征,并可完成纹理识别。     

基于小波变换的织物纹理方向检测方法

织物组织结构及参数的正确识别与分析是一项费时而又重要的工作。该文提出了一种运用图像处理技术检测织物纹理方向和识别织物组织的新方法。把小波变换对织物图像信号良好的分解性能与Radon变换对纹理直线的检测特性结合,运用于织物纹理方向的检测分析及织物组织识别中。实验表明该方法能较准确地测定纹理方向等结构参数,检测结果为正确推断与识别织物所属的基本组织类型提供有效依据。