Foveated active tracking with redundant 2D motion parameters

This work presents a real-time active vision tracking system based on log-polar image motion estimation with 2D geometric deformation models. We present a very efficient parametric motion estimation method, where most computation can be done offline. We propose a redundant parameterization for the geometric deformations, which improve the convergence range of the algorithm. A foveated image representation provides extra computational savings and attenuation of background effects. A proper choice of motion models and a hierarchical organization of the iterations provide additional robustness. We present a fully integrated system with real-time performance and robustness to moderate deviations from the assumed deformation models.     

航摄动态图像序列稳定技术仿真研究

通过分析块匹配算法的优缺点,采用一种其改进的算法应用于动态图像序列的运动矢量检测。该算法先分别把参考图像和当前图像分成若干个运动矢量探测区域,然后在各子区域选取一定数量的代表点进行相关匹配,求出各子区域的局部运动矢量,对所得到的运动矢量进行分析后,决定全局运动矢量。采用这种方法既能保持低的计算量,同时又具有较高的运动矢量检测性能。分析了影响图像的补偿精度主要因素,提出了相应的对策。用该算法对红外航摄图像序列进行稳定仿真试验,证明了该算法用于动态图像序列稳定的有效性。     

基于多约束融合的人手臂三维运动分析

为了在没有任何特殊标志的情况下,实现从单目序列图象中分析、估计人手臂的三维运动,提出了一种多约束融合的方法,该方法是利用棍棒模型来模拟人的手臂,首先通过处理单目图象序列来自动获取图象序列中手臂关节点的对应;然后再利用多约束融合及基于图象序列中关节点的对应,即估计尺度意义下关节点的三维相对运动轨迹;最后利用真实图象来获得相应人手臂的三维运动轨迹,并将其与通过运动捕捉系统获得的人手臂的真实三维运动轨迹进行了比较实验。实验结果表明,该方法用于对人手臂的运动分析非常有效。     

采用改进的几何算法快速估计图像旋转角度

不规则的旋转运动会明显的降低图像序列的质量,旋转运动估计是实现电子图像稳定的关键技术.采用仿射变换模型可以有效地估计图像二维运动参数,但是求解最小二乘解的计算量太大.提出了一种简单的估计图像旋转角度的方法.利用匹配块相对于旋转中心位移矢量对称的特性,消除平移运动引起的运动矢量,通过简单的几何计算即町获得旋转参数,进一步,通过合理设置阈值,降低局部运动估计误差对全局结果的影响.理论分析和仿真结果表明,在帧间图像旋转角度小于10°的条件下,应用方法能够简单有效的估计出旋转角度.     

Perceptual Organization Based Computational Model for Robust Segmentation of Moving Objects

The role of perceptual organization in motion analysis has heretofore been minimal. In this work we present a simple but powerful computational model and associated algorithms based on the use of perceptual organizational principles, such as temporal coherence (or common fate) and spatial proximity, for motion segmentation. The computational model does not use the traditional frame by frame motion analysis; rather it treats an image sequence as a single 3D spatio-temporal volume. It endeavors to find organizations in this volume of data over three levels—signal, primitive, and structural. The signal level is concerned with detecting individual image pixels that are probably part of a moving object. The primitive level groups these individual pixels into planar patches, which we call the temporal envelopes. Compositions of these temporal envelopes describe the spatio-temporal surfaces that result from object motion. At the structural level, we detect these compositions of temporal envelopes by utilizing the structure and organization among them. The algorithms employed to realize the computational model include 3D edge detection, Hough transformation, and graph based methods to group the temporal envelopes based on Gestalt principles. The significance of the Gestalt relationships between any two temporal envelopes is expressed in probabilistic terms. One of the attractive features of the adopted algorithm is that it does not require the detection of special 2D features or the tracking of these features across frames. We demonstrate that even with simple grouping strategies, we can easily handle drastic illumination changes, occlusion events, and multiple moving objects, without the use of training and specific object or illumination models. We present results on a large variety of motion sequences to demonstrate this robustness.     

Linearized proximal alternating minimization algorithm for motion deblurring by nonlocal regularization

Non-blind motion deblurring problems are highly ill-posed and so it is quite difficult to find the original sharp and clean image. To handle ill-posedness of the motion deblurring problem, we use nonlocal total variation (abbreviated as TV) regularization approaches. Nonlocal TV can restore periodic textures and local geometric information better than local TV. But, since nonlocal TV requires weighted difference between pixels in the whole image, it demands much more computational resources than local TV. By using the linearization of the fidelity term and the proximal function, our proposed algorithm does not require any inversion of blurring operator and nonlocal operator. Therefore, the proposed algorithm is very efficient for motion deblurring problems. We compare the numerical performance of our proposed algorithm with that of several state-of-the-art algorithms for deblurring problems. Our numerical results show that the proposed method is faster and more robust than state-of-the-art algorithms on motion deblurring problems.     

3D head tracking under partial occlusion

A new algorithm for 3D head tracking under partial occlusion from 2D monocular image sequences is proposed. The extended superquadric (ESQ) is used to generate a geometric 3D face model in order to reduce the shape ambiguity during tracking. Optical flow is then regularized by this model to estimate the 3D rigid motion. To deal with occlusion, a new motion segmentation algorithm using motion residual error analysis is developed. The occluded areas are successfully detected and discarded as noise. Furthermore, accumulation error is heavily reduced by a new post-regularization process based on edge flow. This makes the algorithm more stable over long image sequences. The algorithm is applied to both synthetic occlusion sequence and real image sequences. Comparisons with the ground truth indicate that our method is effective and is not sensitive to occlusion during head tracking.     

Temporal motion models for monocular and multiview 3D human body tracking

We explore an approach to 3D people tracking with learned motion models and deterministic optimization. The tracking problem is formulated as the minimization of a differentiable criterion whose differential structure is rich enough for optimization to be accomplished via hill-climbing. This avoids the computational expense of Monte Carlo methods, while yielding good results under challenging conditions. To demonstrate the generality of the approach we show that we can learn and track cyclic motions such as walking and running, as well as acyclic motions such as a golf swing. We also show results from both monocular and multi-camera tracking. Finally, we provide results with a motion model learned from multiple activities, and show how this models might be used for recognition.     

一种自适应运动估值视频代码转换的新方法

针对窄带网络的视频信号传输问题,分析了传统视频代码转换帧速率转换时,由于运动矢量非最佳化所造成的图象质量下降的原因,并提出了一种基于量化误差的自自动化运动矢量模型,从而减小了搜索域,使最佳化输出运行矢量能进行快速运动估值;同时根据灰度系统理论,提出了一种有效的灰度预测搜索方法,另外,又根据DCT系数理论模型。采用自适应快速视频编码方法,进一步提高了编码速度,实验结果表明：该方法不仅改善了视频图象质量,而且计算复杂度也大大减小。     

Motion Analysis with the Radon Transform on Log-Polar Images

Image projections provide an effective way of describing image contents or estimate particular kinds of motion. However, most (if not all) of previous literature on projections has been done on Cartesian images. In contrast, the work described in this paper is aimed at exploring how projections can be defined on log-polar images and how they perform in estimating motion. In the proposed algorithm, a set of projection signals is computed in two consecutive frames. Then, 1D affine motion between each pair of corresponding projection signals is estimated. Finally, 2D image affine motion is derived from the set of estimated 1D motion parameters, using a 2D-1D motion mapping model (MMM). A reduced, 5-parameter, affine motion model can be estimated with this MMM. The algorithm is implemented in both, log-polar and Cartesian images. Synthetic motion is used for a careful analysis of the strengths and weaknesses of the algorithm. The comparison of the results with log-polar and Cartesian images reveal that the limitations of the approach are due to the MMM, rather than to the inherent difficulties and distortions introduced by the space-variant nature of log-polar images. Another significant finding is that Cartesian images require much more pixels than log-polar images to get comparable estimation performance.

Human motions representation and recognition by directional motion history images

In this paper, we describe a technique for representing and recognizing human motions using directional motion history images. A motion history image is a single human motion image produced by superposing binarized successive motion image frames so that older frames may have smaller weights. It has, however, difficulty that the latest motion overwrites older motions, resulting in inexact motion representation and therefore incorrect recognition. To overcome this difficulty, we propose directional motion history images which describe a motion with respect to four directions of movement, i.e. up, down, right and left, employing optical flow. The directional motion history images are thus a set of four motion history images defined on four optical flow images. Experimental results show that the proposed technique achieves better performance in the recognition of human motions than the existent motion history images. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

基于时空相关性的自适应运动估计方法

图象序列中,在时间和空间上相邻的运动信息之间具有很强的相关性,利用这种相关性可以得出了一个初始运动矢量,然后再根据这个初始运动矢量对当前块的运动类型进行分类,决定下一步搜索范围的大小,实验表明：这种运动估计方法性能接近全搜索方法,计算复杂度却大大降低了。     

A pattern based PSO approach for block matching in motion estimation

Block matching motion estimation is a popular method in developing video coding applications. A new algorithm has been proposed for reducing the number of search points using a pattern based particle swarm optimization (PSO) for motion estimation. The conventional particle swarm optimization has been modified to provide accurate solutions in motion estimation problems. This leads to very low computational cost and good estimation accuracy. Due to the center biased nature of the videos, the proposed approach uses an initial pattern to speed up the convergence of the algorithm. Simulation results show that improvements over other fast block matching motion estimation algorithms could be achieved with 31%～63% of search point reduction, without degradation of image quality.     

利用无移动运动技术实现运动图像的显示

对于运动图像的显示，本文引入了一种无移动运动技术，这项技术的核心思想是利用一对正交的定向滤波核，并通过不断改变其相位得到一系列“运动”的图像，使图像内的物体在实际位置并不发生改变的情况下，产生视觉上的运动效果，本文将其应用于一幅BMP位图图像，并取得了很好的效果。     

基于投影特征的快速图像稳定算法

本文针对摄像机的栽体既有抖动又有正常扫描运动的图像稳定问题提出了一种快速算法。该算法首先把二雏图像数据投影为一维数据，并在投影数据上估计图像序列的偏移量；然后通过卡尔曼滤波从图像序列的偏移量序列中消除抖动带来的影响，从而保持正常的扫描运动；最后依据滤波后的偏移矢量对图像进行运动补偿。实验结果表明，该算法
法在保持与块匹配算法相当的稳像准确度的前提下，大大减少了计算量。     

Model-based recognition of 3D objects from single images

In this work, we treat major problems of object recognition which have received relatively little attention lately. Among them are the loss of depth information in the projection from a 3D object to a single 2D image, and the complexity of finding feature correspondences between images. We use geometric invariants to reduce the complexity of these problems. There are no geometric invariants of a projection from 3D to 2D. However, given certain modeling assumptions about the 3D object, such invariants can be found. The modeling assumptions can be either a particular model or a generic assumption about a class of models. Here, we use such assumptions for single-view recognition. We find algebraic relations between the invariants of a 3D model and those of its 2D image under general projective projection. These relations can be described geometrically as invariant models in a 3D invariant space, illuminated by invariant “light rays,” and projected onto an invariant version of the given image. We apply the method to real images     

采用去抖动模糊算法的稠密三维重建

针对无人机在航拍大场景对象进行三维重建时因抖动产生的图像模糊现象,以及二维图像序列经运动恢复结构SFM后得到的点云较为稀疏,可视化差等不足,采用去抖动模糊算法恢复模糊图像的原始图像信息,然后在运动恢复结构的基础上进行基于点云的稠密三维重建,最后对稠密重建后的点云进行泊松表面重建以得到表面致密、均匀的三维模型。实验结果表明,去抖动模糊算法可以有效地提高图像的质量,大场景对象经过基于点云的稠密三维重建后得到的重建效果逼真,可视化强。