Gauss比较函数与复杂遥感图像中线状目标的无偏自动检测

建立基于 Gauss 尺度空间的比较函数,对遥感图像中的道路结构进行特征描述、分离和定位.在此基础上,结合道路特征的图形和图像特征,提出了基于局部方向能量的线状目标检测算法,并根据道路的拓扑特征和几何特征进行假设验证、编组、融合,提取有效的道路线特征,应用于城市遥感图像中不同宽度和材质的主干道路和小路的提取.该算法计算复杂度小,在阴影遮挡和道路影像不明显的情况下对道路线特征具有良好的分辨能力.对 Gauss比较函数的定位和抗零点漂移性能也进行了详细的分析.     

基于多尺度分析的三维人脸识别*

为提高人脸识别的精度,加快人脸识别的速度,提出了基于多尺度分析的三维人脸识别方法。过多的特征点容易使判别混乱,出现自相矛盾的过饱和现象,因此采用必要数和最大数的策略来限制特征点的规模。为充分利用各尺度下的人脸特征进行识别,设计了基于ICP与PCA、LDA结合的综合特征匹配方法。实验结果表明,多尺度三维人脸识别方法识别率高于各种标准识别方法,抗噪声性能突出。     

无人机航拍图像配准算法研究

针对无人机航拍图像对尺度变化不明显的问题,在经典SIFT特征匹配算法的基础上,提出了一种改进的CS-SIFT特征匹配算法．该算法通过建立S层金字塔,达到降低多尺度空间和减少特征点数量的目的．在特征向量的匹配中,利用准欧氏距离替代常用欧氏距离,并通过极限几何约束,消除部分错误配准点对,进一步提高特征匹配效率．Matlab仿真结果表明,改进后的算法具有较高的匹配精度和较少的匹配时间,适用于对实时性要求较高的无人机航拍系统．     

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.     

采用尺度空间理论的红外弱小目标检测方法

为了检测红外场景中尺寸大小变化的弱小目标,针对传统滤波方法中固定大小滤波核对此类特性目标检测表现出的不足,提出一种基于尺度空间理论的红外弱小目标检测方法。首先对弱小目标特性进行分析,提出采用点扩散函数形式的目标模型来描述弱小目标;采用固定自适应邻域的方法对原始红外图像进行预处理,抑制背景杂波,增强目标能量;依据尺度规范化后的拉普拉斯尺度空间对图像不同元素滤波响应的不同,获取图像中的可疑目标,利用可疑目标点与其周围像素的梯度关系得到可疑目标点的中心坐标,并据此得到其在图中的尺寸大小;对每个可疑目标划分一个自适应大小窗口,获取分割阈值,分割出真实目标。实验结果表明,该方法能较好地检测出弱小目标,且具有较低的虚警率。     

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.