Fast Reflectional Symmetry Detection Using Orientation Histograms

A simple and fast reflectional symmetry detection algorithm has been developed in this paper. The algorithm employs only the original gray scale image and the gradient information of the image, and it is able to detect multiple reflectional symmetry axes of an object in the image. The directions of the symmetry axes are obtained from the gradient orientation histogram of the input gray scale image by using the Fourier method. Both synthetic and real images have been tested using the proposed algorithm.     

基于几何对称性的颅骨复原技术

颅骨图像在几何上具有对称性特征,根据转动惯量在几何对称性对象上所具有的良好性质,提出一种改进的计算对称轴的算法,该算法利用计算机语言编程实现。结果表明,利用改进后的算法,找到了颅骨这类CT图像的对称轴,并根据对称性修复了图像,完成了颅骨图像的破洞修复,将轮廓点和边界周长的平均误差分别控制在7.50%和3.05%,得到了较好的结果。     

利用分布式主动智能体检测灰度图像的对称轴

图像的对称性研究是模式识别和计算几何的重要内容,在目标识别、视觉监视和形状表示等研究中有一定的应用价值.提出一种利用分布式主动智能体提取灰度图像中的基本反射对称轴的新方法,它通过对智能体在局部图像环境中定居、发展以及迁徙和死亡等行为的模拟来检测、编组和连接显著的局部对称轴.该方法可以提取任意灰度图像中的基本反射对称轴,并且易于并行实现,关于自然图像的实验结果表明了它的有效性.     

Iterative Graph Cuts for Image Segmentation with a Nonlinear Statistical Shape Prior

Shape-based regularization has proven to be a useful method for delineating objects within noisy images where one has prior knowledge of the shape of the targeted object. When a collection of possible shapes is available, the specification of a shape prior using kernel density estimation is a natural technique. Unfortunately, energy functionals arising from kernel density estimation are of a form that makes them impossible to directly minimize using efficient optimization algorithms such as graph cuts. Our main contribution is to show how one may recast the energy functional into a form that is minimizable iteratively and efficiently using graph cuts.     

基于Beamlet的图像线特征提取方法

提出一种基于Beamlet变换图像线特征提取方法。用基于方向窗的方向滤波突显图像中不同方向的线特征,通过阈值处理将灰度图像转换为一组二值图像,利用逻辑或运算融合成一幅图像,用Beamlet变换从融合图像中提取图像的线特征和曲线特征。实验结果表明,在信噪比很低的情况下该方法能够得到满意的结果。     

Symmetry detection by generalized complex (GC) moments: aclose-form solution

This paper presents a unified method for detecting both reflection-symmetry and rotation-symmetry of 2D images based on an identical set of features, i.e., the first three nonzero generalized complex (GC) moments. This method is theoretically guaranteed to detect all the axes of symmetries of every 2D image, if more nonzero GC moments are used in the feature set. Furthermore, we establish the relationship between reflectional symmetry and rotational symmetry in an image, which can be used to check the correctness of symmetry detection. This method has been demonstrated experimentally using more than 200 images     

A fully automated scheme for mammographic segmentation and classification based on breast density and asymmetry

This paper presents a fully automated segmentation and classification scheme for mammograms, based on breast density estimation and detection of asymmetry. First, image preprocessing and segmentation techniques are applied, including a breast boundary extraction algorithm and an improved version of a pectoral muscle segmentation scheme. Features for breast density categorization are extracted, including a new fractal dimension-related feature, and support vector machines (SVMs) are employed for classification, achieving accuracy of up to 85.7%. Most of these properties are used to extract a new set of statistical features for each breast; the differences among these feature values from the two images of each pair of mammograms are used to detect breast asymmetry, using an one-class SVM classifier, which resulted in a success rate of 84.47%. This composite methodology has been applied to the miniMIAS database, consisting of 322 (MLO) mammograms -including 15 asymmetric pairs of images-, obtained via a (noisy) digitization procedure. The results were evaluated by expert radiologists and are very promising, showing equal or higher success rates compared to other related works, despite the fact that some of them used only selected portions of this specific mammographic database. In contrast, our methodology is applied to the complete miniMIAS database and it exhibits the reliability that is normally required for clinical use in CAD systems.     

基于不对称度计算的胆脂瘤诊断

针对常规诊断的主观经验判断，提出以图像不对称性理论为依据，建立一套通过数值计算自动比对图像对称度的计算机辅助诊断软件系统，客观度量临床胆脂瘤病例的诊断。系统以病人头颅的CT图像作为输入，采用逐次求精的方法来快速搜索图像的最佳对称轴，进而计算图像的对称度，并通过与经验阈值的比较，给出初步的病理判断，以利于快速准确地诊断胆脂瘤病例。     

模糊及强噪声条件下中心线的提取

提出一种“重心预取多尺度求精”的中心线提取算法,即利用重心估算大致的中心线,并在线上的关键点附近,特别是分叉处进行多尺度求精,从三维图象中提取树状分支物体的中心线,该算法综合了重心法估算的简单性和尺度空间分析法的准确性等特点,特别适合在边界模糊、强噪声条件下的应用。该算法已成功地应用在医学ＣＴ心脏三维图象的可视化中,文中同时说明了如何从中心线生成曲截面展开图,首次实现了分支血管的曲截面显示。     

基于凸壳技术求解二维点集对称轴的方法

通过物体的对称性,人们可以推断物体的结构并估计它的形状,从而恢复被遮挡或丢失部分的信息。针对二维点集,提出了一种新的求解信息完整和不完整点集对称轴的方法。首先根据凸壳算法求出点集的凸壳,对于信息完整点集,点集的对称轴必是凸壳的对称轴,因此可以借助求解凸壳的对称轴来求解点集的对称轴;对于信息不完整点集,当遗失的点为凸壳内部点时,点集的对称轴也必为凸壳对称轴,当凸壳上的点有遗失时,则可通过求凸壳边的中垂线,以及长度相等两邻边组成角的角平分线来确定点集的对称轴。该方法解决了现有算法只能求解封闭和信息完整图形的对称轴的不足,实验结果表明该方法是高效、可行的。     

Analyzing skewed symmetries

Symmetry is pervasive in both man-made objects and nature. Since symmetries project to skew symmetries, finding axes of skew symmetry is an important vision task. This paper presents a linear time algorithm for finding the axes of skew symmetry, where the degree of symmetry is known. First, we present a review and critique of current methods for finding the axes of skew symmetry. Next, we decompose the problem of finding skew symmetry into the subproblems of solving for the rotational parameter of a shear symmetry and recovering the shear parameter of a reflexive symmetry. Using this approach, the authors derive a direct, non-heuristic moment-based technique for finding the axes of skew symmetry. For skew symmetric figures with degree of symmetry less than five we obtain a closed-form solution. The method does not rely on continuous contours but assumes there is no occlusion and requires knowing the contour's degree of symmetry. It is the first algorithm to find the axes of skew symmetry inO(n) time, where n is the number of contour points. The method is especially suited to industrial applications where the degree of symmetry is often knowna priori. Examples of the method are presented for both real and synthetic images, and an error analysis of the method is given.     

Pose normalization of 3D models via reflective symmetry on panoramic views

A novel pose normalization method, based on reflective symmetry computed on panoramic views, is presented. Qualitative and experimental investigation in 3D data-sets has led us to the observation that most objects possess a single plane of symmetry. Our approach is thus guided by this observation. Initially, through an iterative procedure, the symmetry plane of a 3D model is estimated, thus computing the first axis of the model. This is achieved by rotating the 3D model and computing reflective symmetry scores on panoramic view images. The other principal axes of the 3D model are estimated by computing the variance of the 3D model’s panoramic views. The proposed method is incorporated in a hybrid scheme, that serves as the pose normalization method in a state-of-the-art 3D object retrieval system. The effectiveness of this system, using the hybrid pose normalization scheme, is evaluated in terms of retrieval accuracy and the results clearly show improved performance against current approaches.     

Symmetry as a continuous feature

Symmetry is treated as a continuous feature and a continuous measure of distance from symmetry in shapes is defined. The symmetry distance (SD) of a shape is defined to be the minimum mean squared distance required to move points of the original shape in order to obtain a symmetrical shape. This general definition of a symmetry measure enables a comparison of the “amount” of symmetry of different shapes and the “amount” of different symmetries of a single shape. This measure is applicable to any type of symmetry in any dimension. The symmetry distance gives rise to a method of reconstructing symmetry of occluded shapes. The authors extend the method to deal with symmetries of noisy and fuzzy data. Finally, the authors consider grayscale images as 3D shapes, and use the symmetry distance to find the orientation of symmetric objects from their images, and to find locally symmetric regions in images     

Learning parametric specular reflectance model by radial basis function network

For the shape from shading problem, it is known that most real images usually contain specular components and are affected by unknown reflectivity. In the paper, these limitations are addressed and a neural-based specular reflectance model is proposed. The idea of this method is to optimize a proper specular model by learning the parameters of a radial basis function network and to recover the object shape by the variational approach with this resulting model. The obtained results are very encouraging and the performance is demonstrated by using the synthetic and real images in the case of different specular effects and noisy environments.     

On the detection of the axes of symmetry of symmetric and almostsymmetric planar images

A simple algorithm is presented for finding all the axes of symmetry of symmetric and almost symmetric planar images having nonuniform gray-level (intensity images). The technique, which can also be used in conjunction with planar curves, is based on the identification of the centroids of the given image and other related sets of points, followed by a maximization of a specially defined coefficient of symmetry. Owing to the nature of the required procedures, which are strictly based on the computation of mean values, the method is not particularly affected by the presence of noise and is especially suited for application in conjunction with digitized figures     

Bayesian image and pattern reconstruction from incomplete and noisy data

A Bayesian iterative method can be the basis for a wide range of technologies in the field of pattern recognition and image reconstruction. It involves finding the most probable solutions for images or patterns, if functionals describing the likelihood function and a priori information, respectively, are already known. The article describes the basic principles and recent advances in the development of BIM and its applications in various fields, mainly in tomography and restoration of functions from incomplete and noisy data.     

A technique for finding the symmetry axes of implicit polynomial curves under perspective projection

This paper presents an algebraic technique for detecting the symmetry axes of a perspectively projected plane curve. The procedure applies to implicit polynomials which has been fitted to 2D image data acquired by means of a photo camera or a TV set. The effectiveness of the proposed method has been demonstrated experimentally by using both synthetic and real images.     

Toward object-based heuristics

Recovering the 3-D shape of an object from its 2-D image contour is an important problem in computer vision. In this correspondence, the author motivates and develops two object-based heuristics. The structured nature of objects is the motivation for the nonaccidental alignment criterion: parallel coordinate axes within the object's bounding contour correspond to object-centered coordinate axes. The regularity and symmetry inherent in many man-made objects is the motivation for the orthogonal basis constraint. An oblique set of coordinate axes in the image is presumed to be the projection of an orthogonal set of 3-D coordinate axes in the scene. These object-based heuristics are used to recover shape in both real and synthetic images