基于全局运动仿射模型的视频目标移除

给出一种基于全局运动仿射模型的视频目标移除方法.用仿射模型描述非移除区域的全局运动,通过提取相邻帧的匹配对进行模型参数估计;对于每一帧中待修补的移除区域,根据仿射模型之间的递推关系和运动一致性准则,从其它帧中寻找该区域的信息并进行修补.实验表明,方法能够较好的对运动前景、台标等目标进行移除.     

一种评价仿射不变性特征定位误差的新方法

特征点匹配是计算机视觉中的一个基本问题。不同视点图像中相应特征点邻域窗口之间存在几何上的透视畸变,这可用平面单应映射来表示,而目前大多匹配算法将该映射用仿射变换模型来近似,即用具有仿射不变性的特征进行图像的匹配。仿射变换的线性特点不仅能降低算法复杂度,还能保证迭代过程收敛的稳定性,然而并没有人对该近似的可行性及如何减小近似误差给出定量分析。本文首先回顾了各种几何层次上的特征点匹配策略,重点针对具有仿射不变性特征点的定位误差进行研究和定量分析,通过本文提出的椭圆曲线规范化法推导出该近似所造成相应特征点定位误差的解析表达;然后用真实图像的实验结果验证了本文分析方法的必要性和正确性;最后给出相应的分析结果和结论,并指出提高大基线图像特征点匹配精度的相应措施。     

视频序列的全景图拼接技术

提出了一种对视频序列进行全景图拼接的方法。主要讨论了有大面积的非刚性运动物体出现的序列，不过此方法也同样适用于无运动物体的纯背景序列。为计算各帧间的投影关系，用仿射模型来描述摄像机运动，并用特征点匹配的方法计算出模型中各参数的值。由于用相关法计算的匹配结果准确率比较低，所以用RANSAC（Random Sampling Consensus）对匹配结果进行了筛选，可以准确求出摄像机运动参数。利用运动参数进行投影，然后用多帧相减并求交集，估计出每帧图像中运动物体存在的区域，最后计算得到了全景图。该方法的结果与前人得到的结果进行了比较，证明用此方法能获得质量较高的全景图。     

融合SFM和动态纹理映射的视频流三维表情重建

为从未标定的单目视频序列中重建出具有真实感的三维人脸表情序列,提出一种仅需较少约束的自动化方法.首先用ASM算法从视频首帧自动标定人脸特征,并采用仿射矫正光流方法跟踪运动中的人脸特征;然后结合一般人脸模型,采用从运动恢复形状的方法重建出三维个性化人脸模型以及表情运动;最后采用动态纹理映射来代替传统的静态纹理映射,以产生真实感视觉外观.另外,使用基于特征脸的图像压缩方法,在尽量保持图像质量的前提下缩小原始视频占用的存储空间.实验结果表明,该方法能产生具有相当真实感的三维人脸表情序列,且在时间域和空间域上都保持了较高性能.     

基于改进多尺度乘积LoG算子的仿射不变形状匹配算法

目标在成像过程中发生的几何变形多数情况下可用仿射变换来描述。据此,提出一种利用角点进行仿射不变形状匹配的算法。首先引入多尺度乘积LoG(MPLoG)算子检测轮廓角点,并根据角点间距自适应地提取轮廓特征点,从而获取形状关键特征;为解决目标的仿射变形问题,采用Grassmann流形Gr(2,n)来表征和度量两形状之间的相似度;最后通过迭代式序列移位匹配算法来克服Grassmann流形对起始点的依赖并完成形状的匹配。对形状数据进行仿真实验的结果表明,所提算法能够有效地实现形状检索和识别,并对噪声有较强的鲁棒性。     

基于改进多尺度乘积LoG算子的仿射不变形状匹配算法

目标在成像过程中发生的几何变形多数情况下可用仿射变换来描述。据此,提出一种利用角点进行仿射不变形状匹配的算法。首先引入多尺度乘积LoG(MPLoG)算子检测轮廓角点,并根据角点间距自适应地提取轮廓特征点,从而获取形状关键特征;为解决目标的仿射变形问题,采用Grassmann流形Gr(2,n)来表征和度量两形状之间的相似度;最后通过迭代式序列移位匹配算法来克服Grassmann流形对起始点的依赖并完成形状的匹配。对形状数据进行仿真实验的结果表明,所提算法能够有效地实现形状检索和识别,并对噪声有较强的鲁棒性。     

运动摄像机下多目标检测与跟踪

针对摄像机运动情况下多目标的检测与跟踪问题,提出一种将Global K均值与模板匹配相结合的方法.利用六参数仿射模型得到摄像机运动参数,对图像进行全局运动补偿,用GlobalK均值算法对前景点进行循环聚类,判断目标数目并进行跟踪,通过对目标区域进行模板匹配使跟踪结果更准确.实验结果表明,该方法能够在运动摄像机下稳定、实时地跟踪多个目标,对发生形变的目标基本也能稳定跟踪.     

基于改进Canny边缘检测算子的电子稳像算法研究*

针对特征点匹配电子稳像算法中提取的特征点容易受到噪声干扰的问题,在研究传统的特征点匹配电子稳像算法的基础上,提出了一种将改进的Canny边缘检测技术应用于电子稳像中的方法。利用改进的Canny算子对图像进行边缘检测,并通过对边缘检测的图像进行合理分区,在子区域内选择灰度最大边缘点作为特征点,并采用距离不变准则对特征点的有效性进行验证,建立二维运动模型进行全局运动估计,以此确定仿射模型的运动参数;最后利用卡尔曼滤波的方法来进行运动补偿,从而达到输出稳定视频图像的目的。实验结果表明,用这种方法提取的特征点具有     

采用映射参数模型实现图象运动分割

介绍了采用映射参数模型进行分层结构型分割的方法,并采用Matlab语言对该算法的部分内容进行了验证。该方法利用时空图象亮度梯度和运动信息来估算三维运动和结构参数,分割区域用运动和位置参数定义,这些参数被称为映射参数,通过变化检测,映射参数估算,运动区域和背景检测来实现运动分割。     

基于合成孔径雷达(SAR)图像的智能匹配

本文提出一种合成孔径雷达(SAR)与惯导(INS)结合的图像智能匹配方法。利用图像边缘的B样条模型,在仿射形状空间中进行匹配,并且将结构信息融入其中,提高匹配性能。     

表演驱动的矢量化二维表情动画

为了方便快捷生成生动的卡通表情动画,提出将表情数据重定向到卡通形象上的算法.该算法由用户在卡通形象上标记出与表情数据相对应拓扑结构的控制点,以此控制点集合与表情数据首帧控制点集合做比对;使用得到的比例关系将后续表情数据重定向到卡通形象上,从而形成针对卡通形象的表情数据.实验结果表明,运用文中算法可以制作出较为生动形象的卡通表情动画.     

Semi-automatic cartoon generation by motion planning

To reduce tedious work in cartoon animation, some computer-assisted systems including automatic Inbetweening and cartoon reusing systems have been proposed. In existing automatic Inbetweening systems, accurate correspondence construction, which is a prerequisite for Inbetweening, cannot be achieved. For cartoon reusing systems, the lack of efficient similarity estimation method and reusing mechanism makes it impractical for the users. The semi-supervised graph-based cartoon reusing approach proposed in this paper aims at generating smooth cartoons from the existing data. In this approach, the similarity between cartoon frames can be accurately evaluated by calculating the distance based on local shape context, which is expected to be rotation and scaling invariant. By the semi-supervised algorithm, given an initial frame, the most similar cartoon frames in the cartoon library are selected as candidates of the next frame. The smooth cartoons can be generated by carrying out the algorithm repeatedly to select new cartoon frames after the cartoonists specifying the motion path in a background image. Experimental results of the candidate frame selection in our cartoon dataset suggest the effectiveness of the proposed local shape context for similarity evaluation. The other experiments show the excellent performance on cartoon generation of our approach.     

基于时空约束的运动编辑和运动重定向

近年来兴起的运动捕获已成为人体动画中最有应用前景的技术之一，目前运动捕获手段很多，但是通常成本高，而且捕获到的运动类型比较单一，为了提高运动捕获数据的重用性，生成与复杂场景协调的多样的动画，必须对捕获的运动数据进行编辑和重定向处理，介绍了一种基于时空约束的运动编辑和运动重定向方法，通过规定一组时空约束条件，建立相应的目标函数，采用逆向运动学和数值优化方法求解出满足约束条件的运动姿势，实验结果表明，该方法可以生成多种满足不同场景婪泊逼真运动，提出了数据的重用性。     

从运动捕获数据中提取关键帧

在四元数表示下导出了两个旋转之间差异的一种简单形式，将人体各关节上总的旋转变化作为帧间距，设计了一种从运动捕获数据中提取关键帧的高效算法，通过向量的线性插值和四元数的球面线性插值重建了原始动画。实验表明，该算法具有很好的数据压缩效果，而且提取出的关键帧在视觉上对原始动画具有概括力。     

Exaggerating Character Motions Using Sub‐Joint Hierarchy

Motion capture cannot generate cartoon‐style animation directly. We emulate the rubber‐like exaggerations common in traditional character animation as a means of converting motion capture data into cartoon‐like movement. We achieve this using trajectory‐based motion exaggeration while allowing the violation of link‐length constraints. We extend this technique to obtain smooth, rubber‐like motion by dividing the original links into shorter sub‐links and computing the positions of joints using Bézier curve interpolation and a mass‐spring simulation. This method is fast enough to be used in real time.     

Performance‐driven animation of hand‐drawn cartoon faces

We present a novel performance‐driven approach to animating cartoon faces starting from pure 2D drawings. A 3D approximate facial model automatically built from front and side view master frames of character drawings is introduced to enable the animated cartoon faces to be viewed from angles different from that in the input video. The expressive mappings are built by artificial neural network (ANN) trained from the examples of the real face in the video and the cartoon facial drawings in the facial expression graph for a specific character. The learned mapping model makes the resultant facial animation to properly get the desired expressiveness, instead of a mere reproduction of the facial actions in the input video sequence. Furthermore, the lit sphere, capturing the lighting in the painting artwork of faces, is utilized to color the cartoon faces in terms of the 3D approximate facial model, reinforcing the hand‐drawn appearance of the resulting facial animation. We made a series of comparative experiments to test the effectiveness of our method by recreating the facial expression in the commercial animation. The comparison results clearly demonstrate the superiority of our method not only in generating high quality cartoon‐style facial expressions, but also in speeding up the animation production of cartoon faces. Copyright © 2011 John Wiley & Sons, Ltd.     

面向关节坐标运动数据重定向的通用双向循环自编码器

针对面向关节坐标表示的骨骼运动数据重定向网络缺乏通用性的问题,提出一种能够实现源骨骼到多种骨骼运动重定向的通用双向循环自编码器.该自编码器由基于关节坐标表示的运动数据以重建误差为损失函数训练得到.在完成训练后,首先用自编码器计算源运动数据对应的隐变量和重建运动,然后对重建运动施加骨骼长度约束、足迹约束、根关节位置约束以及骨骼角度约束,并将损失反向传播至隐变量空间中优化隐变量,通过多次迭代得到重定向后运动.在CMU运动数据库上的实验结果表明,提出的自编码器及4种约束能够实现基于关节坐标表示的运动数据的重定向,并且得到的重定向运动在骨骼长度误差、骨骼角度误差、末端效应器轨迹以及平滑性上具有更好的效果.     

The squash-and-stretch stylization for character motions

The squash-and-stretch describes the rigidity of the character. This effect is the most important technique in traditional cartoon animation. In this paper, we introduce a method that applies the squash-and-stretch effect to character motion. Our method exaggerates the motion by sequentially applying the spatial exaggeration technique and the temporal exaggeration technique. The spatial exaggeration technique globally deforms the pose in order to make the squashed or stretched pose by modeling it as a covariance matrix of joint positions. Then, the temporal exaggeration technique computes a time-warping function for each joint, and applies it to the position of the joint allowing the character to stretch its links appropriately. The motion stylized by our method is a sequence of squashed and stretched poses with stretching limbs. By performing a user survey, we prove that the motion created using our method is similar to that used in 2D cartoon animation and is funnier than the original motion for human observers who are familiar with 2D cartoon animation.     

基于改进射线矢量法的手部轮廓与手势实时跟踪方法

目前,在视频分析和处理过程中,运动物体的实时检测和与轮廓跟踪作为计算机视觉分析识别的基础,已变得越来越重要了.改进了传统的射线矢量法表示物体形状的方法,并结合拆分法和聚合法对单帧图像进行分割,以得到完整而准确的手部曲线,在相邻帧之间采用了Kalman滤波器估计帧间手部运动的轨迹来实现跟踪过程.该方法很好地克服了传统射线矢量法所无法表示的形状的缺陷,能够准确地跟踪手部的运动以及各种手势.     

Principal Geodesic Analysis in the Space of Discrete Shells

Important sources of shape variability, such as articulated motion of body models or soft tissue dynamics, are highly nonlinear and are usually superposed on top of rigid body motion which must be factored out. We propose a novel, nonlinear, rigid body motion invariant Principal Geodesic Analysis (PGA) that allows us to analyse this variability, compress large variations based on statistical shape analysis and fit a model to measurements. For given input shape data sets we show how to compute a low dimensional approximating submanifold on the space of discrete shells, making our approach a hybrid between a physical and statistical model. General discrete shells can be projected onto the submanifold and sparsely represented by a small set of coefficients. We demonstrate two specific applications: model‐constrained mesh editing and reconstruction of a dense animated mesh from sparse motion capture markers using the statistical knowledge as a prior.