基于高斯尺度空间的末制导目标跟踪方法

针对光电成像末制导阶段目标尺寸和姿态均迅速变化的问题,提出了一种基于高斯尺度空间的目标跟踪方法。该方法根据目标模板信息获得其在待匹配图像中的搜索区域,由高斯尺度空间理论求得相应的空间图像;利用尺度参数估计出不同尺寸目标的待匹配图像,并将目标模板与这些待匹配图像进行相关匹配,由匹配程度最高的图像求得目标模板的当前位置和尺寸;当模板尺寸发生变化时,对目标模板进行刷新。仿真结果表明,该算法能适应末制导阶段目标尺寸的急剧变化,在强杂波地面背景下实现对目标的稳定跟踪。     

基于形态学尺度空间和梯度修正的分水岭分割

分水岭是一种有效的图像分割方法,但存在过分割现象,为此提出了一种基于形态学尺度空间和梯度修正的分水岭图像分割方法,该方法利用形态学混合开闭重建尺度空间和梯度修正技术,在平滑原始图像的同时保留了重要的区域轮廓而去除了易造成过分割的区域细节和噪声,克服了传统的形态学开闭尺度空间在平滑细节和噪声时,部分重要区域轮廓也被平滑及不满足尺度因果性的问题。对平滑后的图像采用梯度修正分水岭变换,保持了尺度和分割区域数目间的因果性,进一步消除了标准分水岭的过分割现象。仿真实验表明,该方法能有效地消除过分割现象,分割的区域数目满足尺度因果性,且具有较高的区域轮廓定位能力。     

基于双边滤波的Harris角点检测

角点是图像重要的特征之一。角点检测在图像处理和计算机视觉中起着重要的作用。原始的Harris角点检测不具有尺度不变性且常常将噪声作为角点,为了解决此问题,结合尺度空间理论和双边滤波的思想,对原始的Harris算法进行了改进。改进的算法加入高斯核,让算法具有尺度性,加入双边滤波,对检测的对象去噪保边,提高了检测精度。利用该算法,也可以快速准确地自动实现了对一定区域的棋盘格角点检测与提取。实验证明,该改进算法有较高的检测精度、准确率和稳定性,达到了预期的效果。     

一种基于Gabor小波的局部特征尺度提取方法

图像的局部特征尺度在进行特征提取和构造尺度不变量时非常重要。提出了一种基于Gabor小波的局部特征尺度提取方法,该方法利用视皮层简单细胞的2维Gabor函数模型, 构造了一个Gabor尺度空间核函数,利用该核函数计算图像的Gabor尺度空间分解,并在尺度空间中搜索局部极大值作为特征点的固有尺度。实验结果表明,该方法可在不同对比度 条件下有效地提取各类特征的局部尺度,并且相比高斯拉普拉斯(LoG)方法有更好的适应性和可靠性。     

一种通用的仿射不变特征区域提取方法

本文利用尺度-空间理论和自相关矩阵的局部形状提出了一种通用的提取仿射不变特征区域的方法.首先,在尺度-空间中对图像的归一化高斯微分求三维局部极大值获得特征点和特征尺度位置,然后在特征点的特征尺度上用自相关矩阵刻画局部的灰度变化,提取的椭圆区域即为仿射不变特征区域.在此通用方法框架下构造了Harris3D、Laplace3D、Hessian3D和Localjet43D四种仿射不变特征区域算法.实验结果表明这四种算法都具有照度、旋转和尺度不变性.用本文设计的一种仿射不变性仿真实验方法验证了算法的仿射不变性.比较四种算法发现除了Harris3D性能稍差外其他三种算法性能接近.     

Differential and Integral Geometry of Linear Scale-Spaces

Linear scale-space theory provides a useful framework to quantify the differential and integral geometry of spatio-temporal input images. In this paper that geometry comes about by constructing connections on the basis of the similarity jets of the linear scale-spaces and by deriving related systems of Cartan structure equations. A linear scale-space is generated by convolving an input image with Green's functions that are consistent with an appropriate Cauchy problem. The similarity jet consists of those geometric objects of the linear scale-space that are invariant under the similarity group. The constructed connection is assumed to be invariant under the group of Euclidean movements as well as under the similarity group. This connection subsequently determines a system of Cartan structure equations specifying a torsion two-form, a curvature two-form and Bianchi identities. The connection and the covariant derivatives of the curvature and torsion tensor then completely describe a particular local differential geometry of a similarity jet. The integral geometry obtained on the basis of the chosen connection is quantified by the affine translation vector and the affine rotation vectors, which are intimately related to the torsion two-form and the curvature two-form, respectively. Furthermore, conservation laws for these vectors form integral versions of the Bianchi identities. Close relations between these differential geometric identities and integral geometric conservation laws encountered in defect theory and gauge field theories are pointed out. Examples of differential and integral geometries of similarity jets of spatio-temporal input images are treated extensively.     

Feature-Based Image Analysis

According to Marr's paradigm of computational vision the first process is an extraction of relevant features. The goal of this paper is to quantify and characterize the information carried by features using image-structure measured at feature-points to reconstruct images. In this way, we indirectly evaluate the concept of feature-based image analysis. The main conclusions are that (i) a reasonably low number of features characterize the image to such a high degree, that visually appealing reconstructions are possible, (ii) different feature-types complement each other and all carry important information. The strategy is to define metamery classes of images and examine the information content of a canonical least informative representative of this class. Algorithms for identifying these are given. Finally, feature detectors localizing the most informative points relative to different complexity measures derived from models of natural image statistics, are given.     

Linear scale-space

The formulation of afront-end orearly vision system is addressed, and its connection with scale-space is shown. A front-end vision system is designed to establish a convenient format of some sampled scalar field, which is suited for postprocessing by various dedicated routines. The emphasis is on the motivations and implications of symmetries of the environment; they pose natural, a priori constraints on the design of a front-end.The focus is on static images, defined on a multidimensional spatial domain, for which it is assumed that there are no a priori preferred points, directions, or scales. In addition, the front-end is required to be linear. These requirements are independent of any particular image geometry and express the front-end's pure syntactical, bottom up nature.It is shown that these symmetries suffice to establish the functionality properties of a front-end. For each location in the visual field and each inner scale it comprises a hierarchical family of tensorial apertures, known as the Gaussian family, the lowest order of which is the normalised Gaussian. The family can be truncated at any given order in a consistent way. The resulting set constitutes a basis for alocal jet bundle. Note that scale-space theory shows up here without any call upon the prohibition of spurious detail, which, in some way or another, usually forms the basic starting point for diffusion-like scale-space theories.     

Linear Scale-Space Theory from Physical Principles

In the past decades linear scale-space theory was derived on the basis of various axiomatics. In this paper we revisit these axioms and show that they merely coincide with the following physical principles, namely that the image domain is a Galilean space, that the total energy exchange between a region and its surrounding is preserved under linear filtering and that the physical observables should be invariant under the group of similarity transformations. These observables are elements of the similarity jet spanned by natural coordinates and differential energies read out by a vision system.Furthermore, linear scale-space theory is extended to spatio-temporal images on bounded and curved domains. Our theory permits a delay-operation at the present moment which is in agreement with the motion detection model of Reichardt. In this respect our theory deviates from that of Koenderink which requires additional syntactical operators to realise such a delay-operation.Finally, the semi-discrete and discrete linear scale-space theories are derived by discretising the continuous theories following the theory of stochastic processes. The relation and difference between our stochastic approach and that of Lindeberg is pointed out. The connection between continuous and (semi-)discrete sale-space theory for infinitely high scales observed by Lindeberg is refined by applying appropriate scaling limits. It is shown that Lindeberg's requirement of normalisation for one-dimensional discrete Green's functions can be incorporated into our theory for arbitrary dimensional discrete Green's functions, parameter determination can be avoided, and the requirement of operation at even and odd coordinates sum can be guaranteed simultaneously by taking a normalised linear combination of the identity operator and the first step discrete Green's functions. The new discrete Green's functions are still intimately related to the continuous Green's functions and appear to coincide with pyramidal discrete Green's functions.