物体渐变技术现状与发展

物体渐变技术在虚拟现实、工业模拟、科学计算可视化、动画生成等领域有着广泛的应用，具有十分重要的意义，近年来，有许多研究者提出了一些效果不错的算法，对这些算法进行了分析，并对物体渐变的基本原理、物体的表示方法与渐变技术间的紧密联系、二维物体渐变技术和三维物体渐变技术做了较全面的综述，探讨了现有物体渐变技术中需要改进的关键问题，并给出了渐变技术在未来的发展方向。     

一种新的三维物体的渐变算法

本文研究了动画生成的物体渐变技术，基于图形的矢量表示法，提出了一种通过网格化一个三维物体来生成中间渐变物体的算法。给出了算法的形式化描述，并在Marlab上实现了该算法。计算结果表明，按这种算法产生的动画过渡平滑，节奏自然，画面流畅，且该算法具有稳定、高效、逼真和易控等优点。     

图象Morphing过程中的控制

本文研究了图象渐变动画技术,重点讨论了基于网格的二维图象渐变技术.本文提出一种图象渐变的过程控制方法,这种控制方法用一个控制序列对图象渐变过程进行控制,该序列用于网格变换时,可以控制源图象和目标图象分别在中间帧图象中所占的比例;用于颜色混合时,可以去掉中间帧图象上的一些不良效果.另外,本文用Java语言实现了基于网格的二维图象渐变动画和动画过程控制,取得了较好的渐变效果.     

彩色灰度交叉融合的彩色图像渐变技术

研究图像渐变技术,存在着渐变过程不够自然以及渐变质量不够稳定的现象.为提高图像质量,针对上述图像问题,提出了一种新的彩色图像渐变技术,把渐变过程分为两个阶段,每个阶段分别进行彩色图像的灰度图转换和色彩饱和度分量调节.采用了一种新的非线性插值算法,与线性算法相比,渐变效果明显改善;与其它非线性算法相比,渐变的效果更加自然稳定,同时渐变处理的代价较小.实验结果表明,产生的渐变序列更加自然、平滑,图像效果好,同时渐变的效果有非常好的适应性.     

基于四面体坐标系的三维变形方法及应用

为了直观、精确地控制模型的形变,提出一种基于自定义四面体坐标系的三维变形计算方法.首先从几何上给出四面体坐标系的定义,阐述并证明了其关于几何变换的一些性质,使得拓扑变形易于实现,并可应用在三维变形技术中;然后描述了基于四面体坐标系的2种三维变形算法:嵌入式变形与基于特征的精确变形算法.通过多个变形实例结果证明,该方法能够有效地实现物体的变形以及物体间的渐变.     

一种新的二维形状渐变技术

文中提出了一种二维形状渐变的新方法，即基于连分式插值metamorphosis技术。用此方法可以实现多个不同形状之间的渐变。它是一新的非线性插值算法，与其他算法相比，该方法具有计算精度高、多边形过渡平滑、易于编程实现等优点。     

一种多幅图像间的渐变技术

提出了一种图像渐变的新方法，即基于连分式插值Morphing技术。用此方法可以实现多幅不同图像之间的渐变。它是一新的非线性插值算法，与其他算法相比，该方法具有计算精度高、图像过渡平滑、易于编程实现等优点。更重要的是，它还具有信息隐藏的功能。     

三角形渐变动画

介绍了目前常用的三角形渐变动画方法，并根据三角形运动变化的物理原理提出了一种三角形混合方法——物理模型法，为了衡量三角形渐变动画的效果，提出了三角形相似度与法向变化率两个新参数．实验结果表明，与其他现有方法相比，本文方法生成的渐变动画效果在保形性和法向光滑性方面都比较令人满意，并已应用于实际动画制作中。     

计算机辅助绘制黑白唐卡EI北大核心CSCD

唐卡是佛教文化中一种独具特色的绘画艺术形式,构图复杂、绘制精细,黑白唐卡是其中一个独特的分支.针对手工绘制黑白唐卡物体表面的明暗渐变非常耗时耗力的问题,提出一个计算机辅助绘制黑白唐卡的系统.首先对输入的唐卡黑白线条图物体区域进行颜色标记;然后针对区域内部从一条边界向其他边界由暗变亮的物体,综合区域遮挡关系和几何特征提取线条两侧明暗特征;再针对区域内部从边界向内部由暗变亮的物体,通过距离变换或人工交互设置物体灰度控制图像;最后将明暗特征和控制图像代入泊松方程求解,得到黑白唐卡中各类物体精细明暗渐变.文中给出了黑白唐卡的绘制效果.     

基于FDTD法的电磁仿真的优化算法

在对研究对象所在空间进行电磁仿真的过程中,如果采用经典的均匀网格划分法处理具有复杂结构的物体(例如在同一物体上,某些部位之间的尺寸差别很大),势必造成网格数量巨大,计算成本过大的问题.为解决这个问题,提出了一种基于FDTD算法的渐变非均匀网格划分技术,在保证计算精度的基础上比均匀网格划分法占用更少内存和计算时间.仿真结果表明,该改进方法明显提高了计算效率.     

Metamorphosis of arbitrary triangular meshes

Three-dimensional metamorphosis (or morphing) establishes a smooth transition from a source object to a target object. The primary issue in 3D metamorphosis is to establish surface correspondence between the source and target objects, by which each point on the surface of the source object maps to a point on the surface of the target object. Having established this correspondence, we can generate a smooth transition by interpolating corresponding points from the source to the target positions. We handle 3D geometric metamorphosis between two objects represented as triangular meshes. To improve the quality of 3D morphing between two triangular meshes, we particularly consider the following two issues: 1) metamorphosis of arbitrary meshes; 2) metamorphosis with user control. We can address the first issue using our recently proposed method based on harmonic mapping (T. Kanai et al., 1998). In that earlier work, we developed each of the two meshes (topologically equivalent to a disk and having geometrically complicated shapes), into a 2D unit circle by harmonic mapping. Combining those two embeddings produces surface correspondence between the two meshes. However, this method doesn't consider the second issue: how to let the user control surface correspondence. The article develops an effective method for 3D morphing between two arbitrary meshes of the same topology. We extend our previously proposed method to achieve user control of surface correspondence     

Representation and geometric computation using the extended Gaussian image

In this paper, some interesting tools to handle convex objects are presented. The approach is based on the representation of convex objects by their extended Gaussian image (EGI). A new approach for 3D convex object metamorphosis is explored. Some measures of similarity based on the EGI representation are also examined.     

物体变形的广义形态变换方法

将广义形态变换理论用于非刚体运动的描述和内插，通过对非刚体的凸剖分把非刚体的运动分解为非刚体的变形与子凸集的旋转，提出物体近似骨架的概念；通过近似骨架实现子凸集匹配，实现了任意非同拓扑结构(包括有孔及凹多面体)物体的变形．广义形态变换具有基于体元的变形方法和基于边界形状的变形方法的优点，同时克服了它们的缺点．实验表明，该方法变形物体边界光滑、定位精度高、计算速度快，可应用于CAD、虚拟现实和生物医学工程。     

A unified approach to moving object detection in 2D and 3D scenes

The detection of moving objects is important in many tasks. Previous approaches to this problem can be broadly divided into two classes: 2D algorithms which apply when the scene can be approximated by a flat surface and/or when the camera is only undergoing rotations and zooms, and 3D algorithms which work well only when significant depth variations are present in the scene and the camera is translating. We describe a unified approach to handling moving object detection in both 2D and 3D scenes, with a strategy to gracefully bridge the gap between those two extremes. Our approach is based on a stratification of the moving object detection problem into scenarios which gradually increase in their complexity. We present a set of techniques that match the above stratification. These techniques progressively increase in their complexity, ranging from 2D techniques to more complex 3D techniques. Moreover, the computations required for the solution to the problem at one complexity level become the initial processing step for the solution at the next complexity level. We illustrate these techniques using examples from real-image sequences     

3D Euclidean versus 2D non-Euclidean: two approaches to 3D recoveryfrom images

Methods of 3D recovery in computer vision for computing the shape and motion of an object from projected images when an object model is available are classified into two types: the 3D Euclidean approach, which is based on geometrical constraints in 3D Euclidean space, and the 2D non-Euclidean space. Implications of these two approaches are discussed, and some illustrating examples are presented     

3D Hough transform for sphere recognition on point clouds

Three-dimensional object recognition on range data and 3D point clouds is becoming more important nowadays. Since many real objects have a shape that could be approximated by simple primitives, robust pattern recognition can be used to search for primitive models. For example, the Hough transform is a well-known technique which is largely adopted in 2D image space. In this paper, we systematically analyze different probabilistic/randomized Hough transform algorithms for spherical object detection in dense point clouds. In particular, we study and compare four variants which are characterized by the number of points drawn together for surface computation into the parametric space and we formally discuss their models. We also propose a new method that combines the advantages of both single-point and multi-point approaches for a faster and more accurate detection. The methods are tested on synthetic and real datasets.     

Dense statistic versus sparse feature-based approach for 3D object recognition

In this article we introduce and compare two approaches towards automatic classification of 3D objects in 2D images. The first one is based on statistical modeling of wavelet features. It estimates probability density functions for all possible object classes considered in a particular recognition task. The second one uses sparse local features. For training, SURF features are extracted from the training images. During the recognition phase, features from the image are matched geometrically, providing the best fitting object for the query image. Experiments were performed for different training sets using more than 40 000 images with different backgrounds. Results show very good classification rates for both systems and point out special characteristics for each approach, which make them more suitable for different applications.     

Estimation of Three-Dimensional Object Orientation for Computer Vision Systems with Feedback

The determination of orientation information of a 3D object is a crucial step in hypothesis-verification approach to object recognition. In this paper. two approaches are presented to estimate the orientation parameters of a 3D object from a single image. The first one is based on camera model estimation and analysis. The second one is a hierarchical search method. Simulation experiments are conducted to evaluate the performance of the estimations.     

基于深度学习的视觉目标检测技术综述

视觉目标检测旨在定位和识别图像中存在的物体,属于计算机视觉领域的经典任务之一,也是许多计算机视觉任务的前提与基础,在自动驾驶、视频监控等领域具有重要的应用价值,受到研究人员的广泛关注。随着深度学习技术的飞速发展,目标检测取得了巨大的进展。首先,本文总结了深度目标检测在训练和测试过程中的基本流程。训练阶段包括数据预处理、检测网络、标签分配与损失函数计算等过程,测试阶段使用经过训练的检测器生成检测结果并对检测结果进行后处理。然后,回顾基于单目相机的视觉目标检测方法,主要包括基于锚点框的方法、无锚点框的方法和端到端预测的方法等。同时,总结了目标检测中一些常见的子模块设计方法。在基于单目相机的视觉目标检测方法之后,介绍了基于双目相机的视觉目标检测方法。在此基础上,分别对比了单目目标检测和双目目标检测的国内外研究进展情况,并展望了视觉目标检测技术发展趋势。通过总结和分析,希望能够为相关研究人员进行视觉目标检测相关研究提供参考。