基于边界链码的幅度谱图像识别研究

基于边界链码方法，将二值图像中的目标轮廓边界链码变换为有序数组，并对有序数组进行频域变换，获得边界有序数组的幅度谱分布，得出了边界有序数组频域幅度谱与边界链码起点选取的无关性。当目标边界比例变化及含有噪声干扰时，讨论了边界数组的幅度谱分布情况。试验结果表明，当轮廓边界链码起点不定及变形目标外形轮廓变化不大情况下，仍可以利用幅度谱相似性对目标外形进行有效的识别。     

基于边界链码幅度谱的图像型火灾探测算法

在传统的图像型火灾探测中,对火焰颜色、边界等空间域特征进行识别的频率域方法研究较少。为此,提出一种基于边界链码有序数组幅度谱分析的图像型火灾探测算法。该算法将图像中目标轮廓的边界链码变换为有序数组,并对有序数组进行频域变换,获得其幅度谱分布。分析信号的频率域,得到图像空域特征与频域频谱分布之间的联系。将边界链码幅度谱分析理论应用于图像型火灾探测,并通过仿真实验验证了该算法的有效性。     

代理签名与阈下信道的封闭

１９８３年,Ｓｉｍｍｏｎｓ＾［１］提出了阈下信道的概念,并阐述了如何在一个可认证的消息中隐藏一个秘密信息,１９８５年和１９９４年,Ｓｉｍｍｏｎｓ分别描述了如何利用ＥｌＧａｍａｌ签名方案和ＤＳＳ建立阈下信道并下阈下信道的若干应用＾［２－３］,１９９８年和２０００年笔者分别文献［４］和文献［５］中共建立了六个封闭阈下信道的签名方案,文章将基于代理多重签名和一次性代理签名又建立了两个封闭阈下信道的新型签     

平面弹性方程外问题的非重叠型区域分解算法

１．引言 区域分解算法是八十年代兴起的偏微分方程求解新技术．基于有限元法的区域分解算法对求解有界区域问题行之有效［２,４,９］．边界元方法则是处理无界区域问题的强有力的工具［１,１０,１７］,有限元与边界元耦合法得到广泛应用 ［３,５,７］．近年又发展了基于自然边界归化的区域分解算法,特别适用于无界区域问题［８,１１,１２］．迄今这方面的文章主要是针对二维Ｐｏｉｓｓｏｎ方程及双调和方程的［１３－１６］． 本文讨论平面弹性方程的Ｄｉｒｉｃｈｌｅｔ外边值问题其中Ω是充分光滑闭曲线Г０之外的无界区域,ｕ…     

基于对象关系网状转换器的图像描述模型

针对图像描述生成模型缺乏空间关系信息且图像特征利用不充分的问题,结合对象关系网状转换器,提出一种改进的图像描述模型。利用Faster R-CNN提取图像的外观和边界框特征,并将提取的特征输入到改进的转换器中经过编解码生成图像描述。通过将对象外观和边界框特征合并为关系特征的方式对编码器自我注意力层的注意力权值进行改进,以强化目标间的关联性。将编码器和解码器的连接设计为网状结构,从而充分利用图像特征。实验结果表明,与基于单一注意力的Top-down基线模型相比,该模型的BLUE@1和CIDEr评价指标值分别提高了7.6和3.7个百分点,显著提升了描述语句的准确性。     

基于差分矩因子的灰度图像矩快速算法

由于不变矩对图像的平移放大旋转的不敏感性,因此在图像处理、模式识别、场景匹配和计算机视觉等领域获得越来越广泛的应用．但是,求矩运算过程复杂,计算量大,使它的应用受到限制．快速求矩算法不少,但大多限于二值图像．文中提出一种新的适用于灰度图像的快速求矩算法．算法基于文中提出和证明的差分求和定理,即两个离散函数数组的乘积,等于将其中一个差分、另一个累进求和后的乘积．将矩因子作为一个函数数组,图像作为另一个函数数组,对矩因子数组实施多次差分,差分结果使得矩因子数组除边界1个或几个数组元素外,其余数组元素值皆为0．这样需对所有数组元素的乘积变为只对边界1个或几个数组元素的乘积．由于边界上不为0的数组元素值几乎都为1,这实际上就无需乘法计算．该算法原理简单,编程容易,求矩结果精确,适用于任意灰度图像．利用该算法,对任意大小和任意级别的灰度图像,无需任何乘法计算,且加法运算次数也大幅减少．和其它求矩算法相比,计算复杂性大大降低．     

目标边界点集的层次化描述及其形状检索应用

提出用于轮廓线形状和区域形状图像检索的形状描述方法,该方法将目标形状的边界（包括内边界）表示为一个无序的点集,沿各方向对点集的迭代分割,建立层次化的边界点集描述模型.通过对各层形状边界的分割比和分散度的几何特征度量,产生各层的形状特征描述,对它们进行组合,建立对目标形状的层次化描述.两个目标形状的差异性度量定义为它们的层次化描述子的L-1距离.该方法具有：（1）通用性.能够描述轮廓线形状和区域形状这两种不同类型的形状;（2）可扩展性.基于所提出的分层描述框架,可以将分割比和分散度这两种几何度量进行扩展,纳入更多其他几何特征度量,以进一步提高形状描述的精度;（3）多尺度描述特性.提出的分层的描述机制,使得描述子具有内在的由粗到细的形状表征能力;（4）较低的计算复杂性.由于仅仅计算目标图像的边界像素点,使得算法具有较高的计算效率.用MPEG-7 CE-2区域形状图像库和MPEG-7 CE-1轮廓线形状图像库这两个标准测试集对该方法进行评估,并与同类的其他形状描述方法进行比较,实验结果表明：提出的方法在综合考虑检索精确率、检索效率和一般应用能力等指标的情况下,其性能上要优于各种参与比较的方法.     

完备的双子空间边界近邻鉴别分析

针对边界Fisher鉴别分析算法不能够有效解决小样本问题,提出了一种完备的双子空间边界近邻鉴别分析算法。该算法通过理论分析将MFA的目标函数分解成两部分,对此目标函数的求解,首先要对高维样本进行PCA降维至一个低维子空间, 而这一过程并不损失任何有效的鉴别信息,对此通过定理1和定理2进行了证明;然后再分别求出类内边界近邻互补子空间的两投影矩阵。最后人脸库上的实验结果表明了所提方法的有效性。     

无穷扇形区域调和边值问题的重叠型区域分解法

51．引言边界元方法在力学和科学工程计算中有着广泛的应用问．它特别适合求解无界区域上的问题［‘’，‘’1．边界元和有限元耦合［‘，＼以及作适当的人工边界处理后再在有界区域上应用有限元技术＊＼都是处理无界区域问题时常用的方法．另一方面，近年发展起来的区域分解法不仅为并行计算提供了有效手段＊’］，也为边界元方法在无界区域问题上的应用提供了新的途径．其中，无界区域上基于自然边界归化的重叠型和不重叠型区域分解算法＊’，“－‘’］，同时具备了边界元法和区域分解法的优点．它将无界区域n分解为一个很小的有界区域01…     

FDTD计算中一种UPML吸收边界与其内部计算区域的统一建模方法

基于C 语言的多态性实现了单轴各向异性完全匹配层(UPML)吸收边界与吸收边界内部计算区域的统一建模.其主要思想是:首先构造基类-Yee元胞类及其继承类来分别封装UPML内部介质和UPML的电磁特性;然后分别创建基于以上两个类的对象数组来给UPML 及其内部计算区域开辟计算空间;再构造基类类型的指针数组,并用以上数组的地址赋值;最后,所有的计算在指针数组空间完成.该方法避免了UPML与其内部计算区域间的数据传递,简化了编程.数值实验验证了UPML的吸收效果,证明了方法的有效性.     

三维物体的形态图表达方法

三维物体的表达方法是计算机视觉中的关键问题之一，现有的各种三维物体表达方法虽然在各自的识别中得到应用，但都存在各自的局限性，用形态图表达三维物体是一种以视点为中心的表达方法，由于它列举了一个物体所有可能的“定性”形象，即它可使用最少的二维投影线图（特征视图）来表达一个完整的三维物体，因此使三维物体识别转化为2D与2D的匹配，该文首先定义了二维线图拓扑结构等价的判别准则，然后给出了构造透明物体形态图的方法，最后根据拓扑结构等价准则来得到不透明物体的形态图和特征图，并用圆锥与圆柱相交的实例进行了验证。     

一种起始点无关的小波系数形状匹配

小波变换的多分辨率特征使其在计算机视觉中得到广泛的应用,在形状匹配中,小波变换对起始点的依赖制约了小波变换的应用。为了克服小波变换对起始点的依赖,引入Zernike矩,提出一种起始点无关的小波系数形状匹配算法。对输入图像进行预处理后提取目标轮廓,生成具有平移、尺度不变的形状链状表达,并通过小波变换进行多尺度分析。最后计算各个尺度下的各阶Zernike矩,来解决小波变换的起始点问题,实现形状表达的旋转不变性。实验结果表明该算法适用于轮廓较明显的目标,同时具有速度快、精度高、鲁棒性强的优点。     

Object detection,shape recovery,and 3D modelling by depth-encoded hough voting

Detecting objects, estimating their pose, and recovering their 3D shape are critical problems in many vision and robotics applications. This paper addresses the above needs using a two stages approach. In the first stage, we propose a new method called DEHV – Depth-Encoded Hough Voting. DEHV jointly detects objects, infers their categories, estimates their pose, and infers/decodes objects depth maps from either a single image (when no depth maps are available in testing) or a single image augmented with depth map (when this is available in testing). Inspired by the Hough voting scheme introduced in [1], DEHV incorporates depth information into the process of learning distributions of image features (patches) representing an object category. DEHV takes advantage of the interplay between the scale of each object patch in the image and its distance (depth) from the corresponding physical patch attached to the 3D object. Once the depth map is given, a full reconstruction is achieved in a second (3D modelling) stage, where modified or state-of-the-art 3D shape and texture completion techniques are used to recover the complete 3D model. Extensive quantitative and qualitative experimental analysis on existing datasets [2], [3], [4] and a newly proposed 3D table-top object category dataset shows that our DEHV scheme obtains competitive detection and pose estimation results. Finally, the quality of 3D modelling in terms of both shape completion and texture completion is evaluated on a 3D modelling dataset containing both in-door and out-door object categories. We demonstrate that our overall algorithm can obtain convincing 3D shape reconstruction from just one single uncalibrated image.     

Robust shape normalization of 3D articulated volumetric models

3D shape normalization is a common task in various computer graphics and pattern recognition applications. It aims to normalize different objects into a canonical coordinate frame with respect to rigid transformations containing translation, rotation and scaling in order to guarantee a unique representation. However, the conventional normalization approaches do not perform well when dealing with 3D articulated objects.To address this issue, we introduce a new method for normalizing a 3D articulated object in the volumetric form. We use techniques from robust statistics to guide the classical normalization computation. The key idea is to estimate the initial normalization by using implicit shape representation, which produces a novel articulation insensitive weight function to reduce the influence of articulated deformation. We also propose and prove the articulation insensitivity of implicit shape representation. The final solution is found by means of iteratively reweighted least squares. Our method is robust to articulated deformation without any explicit shape decomposition. The experimental results and some applications are presented for demonstrating the effectiveness of our method.     

3D anatomical shape atlas construction using mesh quality preserved deformable models

3D anatomical shape atlas construction has been extensively studied in medical image analysis research, owing to its importance in model-based image segmentation, longitudinal studies and populational statistical analysis, etc. Among multiple steps of 3D shape atlas construction, establishing anatomical correspondences across subjects, i.e., surface registration, is probably the most critical but challenging one. Adaptive focus deformable model (AFDM) [1] was proposed to tackle this problem by exploiting cross-scale geometry characteristics of 3D anatomy surfaces. Although the effectiveness of AFDM has been proved in various studies, its performance is highly dependent on the quality of 3D surface meshes, which often degrades along with the iterations of deformable surface registration (the process of correspondence matching). In this paper, we propose a new framework for 3D anatomical shape atlas construction. Our method aims to robustly establish correspondences across different subjects and simultaneously generate high-quality surface meshes without removing shape details. Mathematically, a new energy term is embedded into the original energy function of AFDM to preserve surface mesh qualities during deformable surface matching. More specifically, we employ the Laplacian representation to encode shape details and smoothness constraints. An expectation–maximization style algorithm is designed to optimize multiple energy terms alternatively until convergence. We demonstrate the performance of our method via a set of diverse applications, including a population of sparse cardiac MRI slices with 2D labels, 3D high resolution CT cardiac images and rodent brain MRIs with multiple structures. The constructed shape atlases exhibit good mesh qualities and preserve fine shape details. The constructed shape atlases can further benefit other research topics such as segmentation and statistical analysis.     

Symbolic signatures for deformable shapes

Recognizing classes of objects from their shape is an unsolved problem in machine vision that entails the ability of a computer system to represent and generalize complex geometrical information on the basis of a finite amount of prior data. A practical approach to this problem is particularly difficult to implement, not only because the shape variability of relevant object classes is generally large, but also because standard sensing devices used to capture the real world only provide a partial view of a scene, so there is partial information pertaining to the objects of interest. In this work, we develop an algorithmic framework for recognizing classes of deformable shapes from range data. The basic idea of our component-based approach is to generalize existing surface representations that have proven effective in recognizing specific 3D objects to the problem of object classes using our newly introduced symbolic-signature representation that is robust to deformations, as opposed to a numeric representation that is often tied to a specific shape. Based on this approach, we present a system that is capable of recognizing and classifying a variety of object shape classes from range data. We demonstrate our system in a series of large-scale experiments that were motivated by specific applications in scene analysis and medical diagnosis.     

Curve/surface representation and evolution using vector level sets with application to the shape-based segmentation problem

In this paper, we revisit the implicit front representation and evolution using the vector level set function (VLSF) proposed in (H. E. Abd El Munim, et al., Oct. 2005). Unlike conventional scalar level sets, this function is designed to have a vector form. The distance from any point to the nearest point on the front has components (projections) in the coordinate directions included in the vector function. This kind of representation is used to evolve closed planar curves and 3D surfaces as well. Maintaining the VLSF property as the distance projections through evolution will be considered together with a detailed derivation of the vector partial differential equation (PDE) for such evolution. A shape-based segmentation framework will be demonstrated as an application of the given implicit representation. The proposed level set function system will be used to represent shapes to give a dissimilarity measure in a variational object registration process. This kind of formulation permits us to better control the process of shape registration, which is an important part in the shape-based segmentation framework. The method depends on a set of training shapes used to build a parametric shape model. The color is taken into consideration besides the shape prior information. The shape model is fitted to the image volume by registration through an energy minimization problem. The approach overcomes the conventional methods problems like point correspondences and weighing coefficients tuning of the evolution (PDEs). It is also suitable for multidimensional data and computationally efficient. Results in 2D and 3D of real and synthetic data will demonstrate the efficiency of the framework     

A Survey of 3D Solid Reconstruction from 2D Projection Line Drawings

The reconstruction of a 3D object from its 2D projection(s) and its corresponding problem of 3D object recognition are two of the important research areas in the field of computer vision and artificial intelligence. Reconstruction involves determining the geometric and topological relationship of an object's atomic parts whereas recognition involves identifying an object by some form of template matching. Nagendra and Gujar¹ gave a survey of several papers on reconstruction of 3D object from its 2D views. In this paper we present a taxonomy of 3D object reconstruction from 2D projection line drawings. We base the classification on the number of 2D views of the 3D solid object, the degree of user interaction necessary for correct reconstruction, and the internal representation used in the reconstruction process. We discuss the basic issues associated with this problem, review the relevant literature and present topics for future research.