Clifford geometric algebra: A promising framework for computer vision, robotics and learning

被引：0

作者：

Bayro-Corrochano, E ^{[1
]}

机构：

[1] CINVESTAV, Ctr Invest & Estud Avanzados, Dept Comp Sci, GEOVIS Lab, Guadalajara 44550, Jalisco, Mexico

来源：

PROGRESS IN PATTERN RECOGNITION, IMAGE ANALYSIS AND APPLICATIONS | 2004年 / 3287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：

In this paper the authors use the framework of geometric algebra for applications in computer vision, robotics and learning. This mathematical system keeps our intuitions and insight of the geometry of the problem at hand and it helps us to reduce considerably the computational burden of the problems. The authors show that framework of geometric algebra can be in general of great advantage for applications using stereo vision, range data, laser, omnidirectional and odometry based systems. For learning the paper presents the Clifford Support Vector Machines as a generalization of the real- and complex-valued Support Vector Machines.

引用

页码：25 / 36

页数：12