基于特征向量的SAR图像目标识别方法研究

用于描述区域特征的Hu矩不变量在模式识别中得到广泛使用。然而在噪声影响下,尤其是SAR图像中严重的相干斑噪声,Hu 矩不变量不再保持其完美的性能。以Hu七个矩不变量为基础,结合SAR图像的特点,引入四个仿射矩不变量和SAR图像中目标区域的峰值、均值和方差系数,构成SAR图像中目标识别的特征向量。该特征向量体现了SAR图像区域目标的形状特征和区域的灰度信息。通过对两种不同分辨率下的T72坦克SAR图像的目标识别仿真实验,均获得了较好的目标识别效果,说明所选取的SAR图像目标识别的特征向量是有效的,具有较强的目标识别性能。     

基于不变矩的高分辨率遥感图像建筑物提取方法

为了有效地对图像进行特征提取, 利用不变矩算法对IKONOS和WorldView两种高分辨率遥感图像的城市建筑物地区进行提取。首先将图像数据经过Canny边缘检测和标记分水岭分割, 然后在此基础上分别利用胡氏不变矩和仿射不变矩对图像进行特征提取; 最后通过实验结果的评价可以证明在建筑物的特征提取上, 仿射不变矩比胡氏不变矩的提取效果更加显著, 进而也证明了利用不变矩算法对高分辨率遥感图像建筑物特征提取这一方法是可行且有效的。     

3D Motion Estimation and Motion Fusion by Affine Region Matching

In this paper,a new method is presented for 3D motion estimation by image region correspondences using stereo cameras.Under the weak perspectivity assumption.we first employ the moment tensor theory (Cyganski and Orr^[11]) to compute the monocular affine transformations relating images taken by the same camera at different time instants and the binocular affine transformations relating images taken by different cameras at the same time instant.We then show that 3D motion can be recovered from these 2D transformations.A space-time fusion strategy is proposed to aim at robust results.No knowledge of point correspondences if requred in the above processes and the computations involved are linear.To find corresponding image regions,new affine invariants,which show stronger invariance,are derived in term of tensor contraction theory.Experiments on real motion images are conducted to verify the proposed method.     

An automatic method for generating affine moment invariants

Affine moment invariants are important if one wants to recognize the surface of a plane in three dimensions when the orientation of the plane is not known beforehand and only two-dimensional information is available. The notion of generating function is introduced as a simple and straightforward way to derive various affine invariants. By this notion, we can get the explicit construction of much more affine moment invariants. Based on this conclusion, a large set of invariant polynomials can be generated automatically and immediately by the algorithm we have designed. These new affine moment invariants can be applied to recognize the image. Approaches in this paper will improve the practicability of affine invariants in object recognition applications.     

基于分数阶矩的仿射不变特征提取

仿射不变的特征提取在目标识别和配准中起关键作用, 图像矩是提取仿射不变特征的重要方法, 高阶矩对噪声较敏感, 实际中仅有几个由整数阶矩构造的仿射不变量可用. 本文引入分数阶矩, 它由变形累次积分定义, 不仅充分利用仿射变换映直线为直线这一特性,而且能方便地消除仿射变换前后极角因子的影响. 利用分数阶矩给出了仿射不变量的构造, 传统矩构造的不变量仅是这种构造的特例. 实验结果表明低次矩构造的不变量一般有较好的抗噪性能.     

Cross-weighted moments and affine invariants for image registrationand matching

A framework for deriving a class of new global affine invariants for both object matching and positioning based on a novel concept of cross-weighted moments with fractional weights is presented. The fractional weight factor allows for a more flexible range to balance between the capability to discriminate between objects that differ only in small shape details and the sensitivity of small shape details to the presence of the noise. Moreover, it makes it possible to arrive at low order (zero order) affine invariants that are more robust than those derived from higher order regular moments. The affine transformation parameters are recovered from the zero and the first order cross-weighted moments without requiring any feature point correspondence information. The equations used to find the affine transformation parameters are linear algebraic. The sensitivity of the cross-weighted moment invariants to noise, missing data, and perspective effects is shown on real images     

基于组合不变矩和神经网络的三维物体识别

在三维物体识别系统中,提出将三维物体的Hu不变矩和仿射不变矩两者的低阶矩组合作为三维物体的特征,结合改进的BP神经网络应用于三维物体的分类识别。理论分析和仿真实验表明组合这两种矩特征进行物体识别,性能优于单独使用Hu不变矩,如果进一步对这两种组合的矩特征进行主成分分析处理,可显著提高系统识别性能,并减少网络的训练时间。     

Implicit Moment Invariants

The use of traditional moment invariants in object recognition is limited to simple geometric transforms, such as rotation, scaling and affine transformation of the image. This paper introduces so-called implicit moment invariants. Implicit invariants measure the similarity between two images factorized by admissible image deformations. For many types of image deformations traditional invariants do not exist but implicit invariants can be used as features for object recognition. In the paper we present implicit moment invariants with respect to polynomial transform of spatial coordinates, describe their stable and efficient implementation by means of orthogonal moments, and demonstrate their performance in artificial as well as real experiments.     

基于不变矩的人形"头肩像"识别技术

针对实时监控识别人形的要求,提出了一种基于矩不变量的分级识别人形“头肩像”技术。首先从视频帧序列中,采用差分的方法分割出活动目标,对活动目标进行预处理后,根据活动目标轮廓的最小外接矩形宽高比,所定义的轮廓描述符和仿射不变矩,依据所选择的分级差别策略,由实验得出的经验数值,判断运动动目标是人形“头肩像”“正面”还是“侧面”。实验结果表明本方法具有快速,适应性强的特点。     

Legendre moment invariants to blur and affine transformation and their use in image recognition

The processing of the images simultaneously degraded by blur and affine transformation has become a key task in many applications and many novel methods are designed specifically for it in which the moment-based methods play an important role. However, the existing moment-based methods all resort to non-orthogonal moments invariants which have problem of information redundancy and are sensitive to noise. In this paper, we construct a new set of combined invariants of orthogonal Legendre moments which hold for blur and affine transformation together. The experimental results show that the proposed invariants have better discriminative power and robustness to noise with the comparison to other invariants.     

Determination of blur and affine combined invariants by normalization

The determination of invariant characteristics is an important problem in pattern recognition. In many situations, images to be processed are usually subjected to geometric distortion and/or blur degradation. In this paper, we introduce an approach to derive blur and affine combined invariants (BAI). Firstly, we normalize the image to a standard form by using blur invariant moments as normalization constraints. Then, we construct the blur and affine combined invariants at the standard form. Using the method proposed in this paper, a set of blur and affine combined invariant features can be obtained easily and effectively. Several experimental results are presented to illustrate the performance of the invariants for simultaneously affine deformed and blur degraded images.     

Combined blur and affine moment invariants and their use in pattern recognition

The paper is devoted to the recognition of objects and patterns deformed by imaging geometry as well as by unknown blurring. We introduce a new class of features invariant simultaneously to blurring with a centrosymmetric PSF and to affine transformation. As we prove in the paper, they can be constructed by combining affine moment invariants and blur invariants derived earlier. Combined invariants allow to recognize objects in the degraded scene without any restoration.     

Vector Ordering and Morphological Operations for Colour Image Processing: Fundamentals and Applications

: The extension of concepts of greyscale morphology to colour image processing requires the use of a proper ordering of vectors (colours) and the definitions of infimum and supremum operators in an appropriate colour space. In this paper, a new approach to colour image morphology is proposed. It is based on a new ordering of vectors in the HSV colour space that is partial ordering. The proposed approach is hue preserving, and it is not a component-wise technique. Its basic characteristic is that it is compatible to the standard greyscale morphology: its fundamental and secondary operations possess the same basic properties as their greyscale counterparts, and furthermore, it is identical to greyscale morphology when it is applied to greyscale images. Examples that illustrate the application of the defined operations to colour images are provided. Moreover, the usefulness of the new method in various colour image processing applications, such as colour image edge detection, object recognition, vector top-hat filtering and skeleton extraction, is demonstrated. Received: 14 July 2000, Received in revised form: 24 April 2001, Accepted: 19 June 2001     

A Frequency Domain Technique Based on Energy Radial Projections for Robust Estimation of Global 2D Affine Transformations

The contribution of this paper is twofold: (1) it provides a thorough analysis of the frequency domain relationships relating two affine-warped images and (2) based on a fundamental equation between energy radial projections, it presents an original algorithm for estimating the global 2D affine transformation between the two images. It is well known that operating in the frequency domain allows one to separate the estimate of the affine matrix, related to the magnitudes of the Fourier transforms of the two images, from the estimate of the translation vector, related to their phases. Exploiting this property, our algorithm consists of two main steps: (1) the affine matrix is first estimated by solving, with a coarse-to-fine strategy, a suitable minimization problem formulated upon the radial projections of the image energies, and (2) after compensation for the contribution of the affine matrix, the translation vector is then recovered by means of phase correlation. The proposed method is very robust against perspective distortion and, with moderate translational displacements, it may also work when the two images differ along their peripheral areas. Experimental evidence of these characteristics is reported and discussed. The algorithm can be efficiently implemented via FFT and well suits applications requiring unsupervised and/or quasi-real-time estimation of global motion that can be described with 2D affine transformations.     

基于几何不变量的图像特征识别

图像的特征识别是图像处理和识别中的一个重要问题，几何不变量作为特征的特征值在很多领域已经得到了广泛的应用。实际中，普遍采用在仿射变换及射影变换下保持不变的仿射、射影不变量作为特征值。本文根据具体图像的特点，利用4类仿射和射影不变量构成特征的特征值空间，依据4步识别策略来识别图像中的特征点，从而完成识别任务。实验表明，这4类不变量能够较好地识别出实际图像中的特征。