Recognition of shape-changes in 3D-objects by GRBF network

从图像中自动获取3D模型通常是一个非常困难的问题。克服3D模型需求的一种方式是开发用于通过2D视图(2D投影)旋转3D对象的集合来表示对象的方法。Poggio et.艾尔和Maruyama等人。提出了一种基于视点的目标识别方法GRBF(Generalized Radial Basis Function)网络，它依赖于多个2D视图而不是3D模型。该网络类似于传统的人工神经网络，但隐含层中的每个单元都用高斯等径向基函数表示。本文将GRBF网络应用于抓手、拍击点、张开等手形变化的识别，并设计了基于GRBF网络的手部运动捕捉系统。我们通过计算机模拟和使用数据手套来展示它们的性能。由于序列运动由许多帧组成，我们可以提取出几个典型的运动帧，并将这些帧应用到GRBF网络中进行学习。训练好的GRBF网络识别率可达84%以上。

It is usually a very difficult problem to acquisition of 3D models from images automatically. One way to overcome the need of 3D model is to exploit methods for representing objects by a collection of 2D views (2D projection) rotating 3D object. Poggio et. al. and Maruyama et al. have proposed such a view-based object-recognition method named GRBF (Generalized Radial Basis Function) network which relies on multiple 2D views instead of 3D models. This network resembles to the conventional artificial neural network, however, each unit in a hidden layer is represented by radial basis function such as Gaussian. In this paper, we have applied GRBF network to the recognition of hand shape-changes such as grasp, flap point and open, and have designed the system to capture motions of the hand with the GRBF. We show their performance by computer simulations and by using data glove. Since sequential motion consists of a lot of frames, we can pick up typical several canonical frames of the motion and these frames can be applied to the GRBF network for learning. The trained GRBF network could achieve a recognition over 84%.