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

通过 GRBF 网络识别 3D 对象的形状变化

• 批准号: 10680387
• 负责人: OKAMOTO Masahiro
• 金额: $ 2.18万
• 依托单位: Kyushu Institute of Technology
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (C)
• 财政年份: 1998
• 资助国家: 日本
• 起止时间: 1998 至 1999
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-10680387/
• 关键词: neural network; 3D object recognition; structural learning; Gaussian GRBF network; hand-shape change; hand's motion capture; artificial intelligence; data-glove

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%.

从图像中自动获取三维模型是一个非常困难的问题。克服对3D模型的需要的一种方法是利用通过旋转3D对象的2D视图（2D投影）的集合来表示对象的方法。波焦等等人和Maruyama等人提出了一种称为GRBF（广义径向基函数）网络的基于视图的对象识别方法，该方法依赖于多个2D视图而不是3D模型。该网络类似于传统的人工神经网络，然而，隐藏层中的每个单元由径向基函数（如高斯）表示。本文将GRBF网络应用于手形变化的识别，如抓取、拍动点和张开等，并设计了基于GRBF网络的手形变化捕捉系统。我们通过计算机模拟和使用数据手套来展示它们的性能。由于序列运动由大量的帧组成，我们可以从中提取几个典型的运动帧，这些帧可以应用到GRBF网络中进行学习。训练后的GRBF网络识别率达到84%以上。

项目成果

M.Hirakawa et.al.: "Recognition of the sequential motion of the hand by the GRBF network"Proc. of the 5th Intl. Symp. On Artificial Life and Robotics (AROB 5th '00). 2. 534-538 (2000)

M.Hirakawa 等人：“GRBF 网络对手部连续运动的识别”Proc。

Masahiro Okamoto, Miwako Hirakawa, Noriaki Kinoshita, Takanori Katsuki, Tetsuya Kinoshita, Masami Ishibashi: "Recognition of shapes and shape changes in 3D-objects by GRBF network: A structural learning algorithm to explore small-sized networks"In: Featur

Masahiro Okamoto、Miwako Hirakawa、Noriaki Kinoshita、Takanori Katsuki、Tetsuya Kinoshita、Masami Ishibashi：“通过 GRBF 网络识别 3D 对象的形状和形状变化：探索小型网络的结构学习算法”：特征

M. Okamoto et.al.: "Recognition of shapes and shape changes in 3D-objectives by GRBF network: A structural learning algorithm to explore small-sized networks"Feature analysis, clustering and classification: soft computing approaches (ed. By Nikhil R. Pal,

M. Okamoto 等人：“通过 GRBF 网络识别 3D 目标中的形状和形状变化：一种探索小型网络的结构学习算法”特征分析、聚类和分类：软计算方法（Nikhil R 编辑）

Miwako Hirakawa, Masami Ishibashi, Taeko Murakami, Masahiro Okamoto: "Recognition of the sequential motion of the hand by the GRBF network"Proc. of the 5th Intl. Symp. on Artificial Life and Robotics (AROB 5th '00). 534-538 (2000)

Miwako Hirakawa、Masami Ishibashi、Taeko Murakami、Masahiro Okamoto：“GRBF 网络对手的顺序运动的识别”Proc。

M.Okamoto et.al.: "recognition of shapes and shape changes in 3D-objects by GRBF network:a structural learning algorithm to explore small-sized networds"Feature analysis,clustering and classification:soft computing approaches(ed,by Nikhil R.Pal,World Scie

M.Okamoto 等人：“通过 GRBF 网络识别 3D 对象的形状和形状变化：一种探索小型网络的结构学习算法”特征分析、聚类和分类：软计算方法（编者：Nikhil R）