Qualitative Geometry for Shape Recognition

We propose an algorithm providing an abstract representation of any polygonal object O in terms of spheres. The result is a graph-based skeleton capturing the general shape of O and its inner structure (respective positions of convex parts and their thickness). We define a first-order logic language expressing in a qualitative way the needed notions (distance, size and angle). Last, we propose methods to compare shapes using this graph-based skeleton of objects.     

Geometric Invariant Shape Representations Using Morphological Multiscale Analysis

In this paper, we present a new geometric invariant shape representation using morphological multiscale analysis. The geometric invariant is based on the area and perimeter evolution of the shape under the action of a morphological multiscale analysis. First, we present some theoretical results on the perimeter and area evolution across the scales of a shape. In the case of similarity transformations, the proposed geometric invariant is based on a scale-normalized evolution of the isoperimetric ratio of the shape. In the case of general affine geometric transformations the proposed geometric invariant is based on a scale-normalized evolution of the area. We present some numerical experiments to evaluate the performance of the proposed models. We present an application of this technique to the problem of shape classification on a real shape database and we study the well-posedness of the proposed models in the framework of viscosity solution theory.     

Rotation Invariant Kernels and Their Application to Shape Analysis

Shape analysis requires invariance under translation, scale, and rotation. Translation and scale invariance can be realized by normalizing shape vectors with respect to their mean and norm. This maps the shape feature vectors onto the surface of a hypersphere. After normalization, the shape vectors can be made rotational invariant by modeling the resulting data using complex scalar-rotation invariant distributions defined on the complex hypersphere, e.g., using the complex Bingham distribution. However, the use of these distributions is hampered by the difficulty in estimating their parameters and the nonlinear nature of their formulation. In the present paper, we show how a set of kernel functions that we refer to as rotation invariant kernels can be used to convert the original nonlinear problem into a linear one. As their name implies, these kernels are defined to provide the much needed rotation invariance property allowing one to bypass the difficulty of working with complex spherical distributions. The resulting approach provides an easy, fast mechanism for 2D & 3D shape analysis. Extensive validation using a variety of shape modeling and classification problems demonstrates the accuracy of this proposed approach.     

Freeform Shape Representations for Efficient Geometry Processing

The most important concepts for the handling and storage of freeform shapes in geometry processing applications are parametric representations and volumetric representations. Both have their specific advantages and drawbacks. While the algebraic complexity of volumetric representations is independent from the shape complexity, the domain of a parametric representation usually has to have the same structure as the surface itself (which sometimes makes it necessary to update the domain when the surface is modified). On the other hand, the topology of a parametrically defined surface can be controlled explicitly while in a volumetric representation, the surface topology can change accidentally during deformation. A volumetric representation reduces distance queries or inside/outside tests to mere function evaluations but the geodesic neighborhood relation between surface points is difficult to resolve. As a consequence, it seems promising to combine parametric and volumetric representations to effectively exploit both advantages. In this talk, a number of projects are presented and discussed in which such a combination leads to efficient and numerically stable algorithms for the solution of various geometry processing tasks. Applications include global error control for mesh decimation and smoothing, topology control for level‐set surfaces, and shape modeling with unstructured point clouds.     

A Shape Modulus for Fractal Geometry Generation

We present an efficient new method for computing Mandelbrot-like fractals (Julia sets) that approximate a user-defined shape. Our algorithm is orders of magnitude faster than previous methods, as it entirely sidesteps the need for a time-consuming numerical optimization. It is also more robust, succeeding on shapes where previous approaches failed. The key to our approach is a versor-modulus analysis of fractals that allows us to formulate a novel shape modulus function that directly controls the broad shape of a Julia set, while keeping fine-grained fractal details intact. Our formulation contains flexible artistic controls that allow users to seamlessly add fractal detail to desired spatial regions, while transitioning back to the original shape in others. No previous approach allows Mandelbrot-like details to be “painted” onto meshes.     

利用仿射几何特性提取图像中的仿射不变特征

利用仿射几何的性质从图像中提取仿射不变特征,提出了扩展质心(extended centroid,EC)和仿射区域划分(affine region cutting,ARC)的概念,通过迭代ARC求得多个仿射区域的扩展质心序列,将扩展质心序列按一定规则组合成一系列三角形,然后根据仿射几何的性质,由各个三角形的面积构造不变特征。该不变特征提取方法具有速度快、简单灵活的特点,所构造的特征量对照度变化、噪声干扰、部分遮挡以及小角度3维旋转具有较好的稳定性,实验结果验证了该方法的有效性。     

一种基于目标边界的不变特征提取方法

提出了一种基于目标边界的不变特征提取方法。导出了用物体角点坐标表示的低阶边界矩的闭合形式,构造了基于边界矩的仿射变换不变量。该方法只需要对物体角点进行简单的代数运算,因此,该方法简单明了,计算量很小。实验结果证明了该方法的有效性。     

点模型的多分辨率形状编辑

提出了一种基于几何细节映射的点模型的形状编辑方法.几何细节是曲面的一个重要属性,定义几何细节为原始曲面及其基曲面之间的向量差,该基曲面由多层次B样条所构成.通过基曲面上的局部仿射坐标,则可以得到与之对应的多分辨率几何细节表示,曲面的低频信息和高频信息易被用户所指定的频段分离.通过调节基曲面的形状,再将这些几何细节映射上去,可以对模型进行保细节的变形;如果将几何细节映射到其他物体上,将可以得到几何细节迁移的结果.为点模型开发了多种特征保持的编辑算子,实验结果表明,所提出的方法是一种有效的点模型造型算法.     

STAR ‐ Laplacian Spectral Kernels and Distances for Geometry Processing and Shape Analysis

In geometry processing and shape analysis, several applications have been addressed through the properties of the spectral kernels and distances, such as commute‐time, biharmonic, diffusion, and wave distances. Our survey is intended to provide a background on the properties, discretization, computation, and main applications of the Laplace‐Beltrami operator, the associated differential equations (e.g., harmonic equation, Laplacian eigenproblem, diffusion and wave equations), Laplacian spectral kernels and distances (e.g., commute‐time, biharmonic, wave, diffusion distances). While previous work has been focused mainly on specific applications of the aforementioned topics on surface meshes, we propose a general approach that allows us to review Laplacian kernels and distances on surfaces and volumes, and for any choice of the Laplacian weights. All the reviewed numerical schemes for the computation of the Laplacian spectral kernels and distances are discussed in terms of robustness, approximation accuracy, and computational cost, thus supporting the reader in the selection of the most appropriate method with respect to shape representation, computational resources, and target application.     

高斯卷积角:用于叶片图像检索的形状描述不变量

植物叶片图像的识别是计算机视觉和图像处理技术在生物学和现代农业中的一个重要应用.其挑战性在于植物叶片种类数量巨大,且许多叶片图像具有很大的类间相似性,使得描述叶片图像的类间差异变得非常困难.提出一种称为高斯卷积角的叶片形状描述方法.该方法用高斯函数与叶片轮廓点的左右邻域向量的卷积产生高斯卷积角,再通过改变高斯函数的尺度...     

An SL(2) Invariant Shape Median

Median averaging is a powerful averaging concept on sets of vector data in finite dimensions. A generalization of the median for shapes in the plane is introduced. The underlying distance measure for shapes takes into account the area of the symmetric difference of shapes, where shapes are considered to be invariant with respect to different classes of affine transformations. To obtain a well-posed problem the perimeter is introduced as a geometric prior. Based on this model, an existence result can be established in the class of sets of finite perimeter. As alternative invariance classes other classical transformation groups such as pure translation, rotation, scaling, and shear are investigated. The numerical approximation of median shapes uses a level set approach to describe the shape contour. The level set function and the parameter sets of the group action on every given shape are incorporated in a joint variational functional, which is minimized based on step size controlled, regularized gradient descent. Various applications show in detail the qualitative properties of the median.     

一种基于几何的形计算机制

提出一种几何问题几何化的形计算机制。它综合了几何、代数、画法几何及现代计 算工具等理论、方法与技术,实现“三维思维,二维图解,一维计算”多维空间的融合。从更宏 观的几何角度构筑算法框架,是对常规数计算的补充,可用于相当宽泛的一类几何计算。     

微分几何编码识别物体的形状

为更好地识别目标形状,编码方法需要对目标的刚体变换具有不变性,同时最大限度保持目标的原有信息。鉴于刚体平面曲线作变换时其曲率的不变性,提出了基于轮廓曲率提取的目标边界编码方法,并对此方法实施了离散化处理。提出了基于改进的KMP算法（D.K.Knuth,V.R.Pratt和J．H．Morris）的曲线匹配方法,并对目标轮廓的重建作了描述。实验证明,利用微分几何的思想描述目标边界,提取方法简单,存储量小,其编码针对目标刚体变换具有不变性,为识别提供了较大的方便。     

基于 Windows GDI+ 的几何形状测量

介绍了利用GD I+的G raph icsPath类和R eg ion类对几何线条的长度、几何线条所围区域的面积的测量方法,并对其中的关键部分提供了示例代码。与传统的数学微积分方法相比,不需要复杂的数学运算和高深的数学功底。     

基于参数化求和不变量与特征重整的形状匹配

从特征提取和特征匹配两方面考虑,提出了一种鲁棒的形状匹配方法。首先,基于求和不变量,设计了基于面积的形状参数化和归一化方法,提出了参数化求和不变量,该不变量基于形状局部描述且采用积分算子计算,具有较好的鲁棒性和仿射不变性。然后,为进一步提高形状匹配的鲁棒性,在特征匹配上,分析了参数化求和不变量的先验信息,设计了基于特征重整的匹配距离函数,并通过动态规划进行实现。仿真实验表明了所提方法的有效性。     

Affine Invariant Texture Segmentation and Shape from Texture by Variational Methods

We address the problem of texture segmentation by using a novel affine invariant model. The introduction of affine invariance as a requirement for texture analysis goes beyond what is known of the human performance and also beyond the psychophysical theories. We propose to compute texture features using affine invariant intrinsic neighborhoods and affine invariant intrinsic orientation matrices. We discuss several possibilities for the definition of the channels and give comparative experimental results where an affine invariant Mumford-Shah type energy functional is used to compute the multichannel affine invariant segmentation. We prove that the method is able to retrieve faithfully the texture regions and to recover the shape from texture information in images where several textures are present. The numerical algorithm is multiscale.     

Effects of Errors in the Viewing Geometry on Shape Estimation

A sequence of images acquired by a moving sensor contains information about the three-dimensional motion of the sensor and the shape of the imaged scene. Interesting research during the past few years has attempted to characterize the errors that arise in computing 3D motion (egomotion estimation) as well as the errors that result in the estimation of the scene's structure (structure from motion). Previous research is characterized by the use of optic flow or correspondence of features in the analysis as well as by the employment of particular algorithms and models of the scene in recovering expressions for the resulting errors. This paper presents a geometric framework that characterizes the relationship between 3D motion and shape in the presence of errors. We examine how the three-dimensional space recovered by a moving monocular observer, whose 3D motion is estimated with some error, is distorted. We characterize the space of distortions by its level sets, that is, we characterize the systematic distortion via a family of iso-distortion surfaces, which describes the locus over which the depths of points in the scene in view are distorted by the same multiplicative factor. The framework introduced in this way has a number of applications: Since the visible surfaces have positive depth (visibility constraint), by analyzing the geometry of the regions where the distortion factor is negative, that is, where the visibility constraint is violated, we make explicit situations which are likely to give rise to ambiguities in motion estimation, independent of the algorithm used. We provide a uniqueness analysis for 3D motion analysis from normal flow. We study the constraints on egomotion, object motion, and depth for an independently moving object to be detectable by a moving observer, and we offer a quantitative account of the precision needed in an inertial sensor for accurate estimation of 3D motion.     

一种基于扩散几何的非刚体三维形状分割方法

三维形状分割是三维形状分析中的一个重要问题.为了使分割结果能适应非刚体丰富的姿态变化,提出一种基于扩散几何的三维网格分割方法.该方法采用波核特征的局部极值点作为非刚体网格模型表面的显著特征点;进而将显著特征点作为初始聚类中心,采用K-均值聚类算法来获得分割结果.实验结果表明,文中方法不仅对处于不同姿态的非刚体三维形状具有良好的分割一致性,而且对噪声、孔洞等具有较好的鲁棒性.     

二维流形上一组等距与尺度不变的形状描述子

在流形上构造了一组具有等距不变与尺度不变的形状描述子.该方法首先利用Biharmonic距离构造核函数,然后利用勒贝格积分构造具有等距不变的描述子,再利用曲面的勒贝格测度对形状描述子进行标准化,得到了一组新的描述子.这种新的形状描述子既具有等距不变性,又具有尺度不变性,反映了几何形状的内蕴性质.在非刚体模型上进行了大量实验,通过实验结果可以看出,该方法对于发生等距变形的物体具有很好的识别和区分效果.