3-D Shape and Pose Estimation of Articulated Objects From Image Sequences

针对“有线手套”等保守手势界面工具在人机交互中存在的不便，研究了一种新的非触觉手势识别方法，该方法在用户身体没有任何特殊佩戴或标记的情况下，从电视摄像机拍摄的图像序列中估计人体的三维形状和运动。首先，研究了一种基于单目轮廓图像序列的手部形状和位姿估计方法。给定一个粗略形状的模型，该方法估计粗略的手姿。然后计算出手指长度、手掌大小和关节角度等模型参数的概率分布。结合每一帧图像中观察到的图像特征和人类手部的先验知识，如关节角度的可能范围和每个手指长度之间的关系，更新分布，更精确地修改模型形状。该估计算法在三维高速图形发生器和个人计算机搭建的平台上实现，并对计算机图形图像和真实手图像进行了实验。结果表明，在获得不同形状手的多个视图后，单目图像可以很好地估计关节角度、手指长度和手掌大小。由于轮廓包含的三维形状信息非常有限，因此研究了一种利用图像运动和深度信息的方法。通过对该方法的精确验证，在图像特征精度不高的情况下，由于概率计算中的近似误差，会导致形状和姿态的错误估计。研究发现，要解决这一问题，需要采用不使用概率的其他方法。在此基础上，提出了一种将可能的形状参数区域近似为椭球体的估计方法来代替概率方法。结果表明，该方法即使在图像特征错误的情况下也能正确地估计出目标。然而，人们也发现这种方法在突然输入时出现了很大的错误。

In order to improve inconvenience of a conservative gesture interface instrument for human-machine interaction such as "wired-glove", we studied the newly non-tactile gesture recognizing method which estimates the 3-Dimensional shape and movement of human body from image sequences captured by TV-cameras without any special wears or marking on a user's body.First, a hand shape and pose estimation method from monocular silhouette image sequences was studied.Given a roughly shaped model initially, the method estimates rough hand poses.Then it calculates a probability distribution of the model parameters such as finger lengths, palm size and joint angles.Combining observed image features in every image frame and prior knowledge about human hand such as possible ranges of joint angles and relationship between each finger lengths, the distribution is updated and the model shape is modified more precisely.This estimation algorithm was implemented on a platform constructed from a 3-D highspeed graphics generator and personal computers and experimented for comuter graphics images and real hand images.It was demonstrated the joints angles, finger lengths and palm size can be well-estimated from a monocular images if many views of various shaped hand are obtained.Since silhouettes contain very limited information about 3-D shape, a method using image motion and depth information was also studied.By precise verification of this method, the shape and pose are wrongly estimated due to approximation errors in probability calculation if accuracy of the image features isnot good.It found other method not using probability is required to resolve the problem.Next another estimation method which aproximate the possible shape parameter region as a ellipsoid was tested for subsutitution of the probablistic method.It was found this method can correctly estimate even if the image features are errornous.However, it was also found this method is so week in a sudden large errornous input.