Parametrized SOMs for object recognition and pose estimation

被引：0
|
作者：
Saalbach, A [1 ]
Heidemann, G [1 ]
Ritter, H [1 ]
机构：
[1] Univ Bielefeld, Fac Technol, D-33501 Bielefeld, Germany
来源：
ARTIFICIAL NEURAL NETWORKS - ICANN 2002 | 2002年 / 2415卷
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
We present the "Parametrized Self-Organizing Map" (PSOM) as a method for 3D object recognition and pose estimation. The PSOM can be seen as a continuous extension of the standard Self-Organizing Map which generalizes the discrete set of reference vectors to a continuous manifold. In the context of visual learning, manifolds based on PSOMs can be used to represent the appearance of various objects. We demonstrate this approach and its merits in an application example.
引用
收藏
页码：902 / 907
页数：6
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