Clifford Geometric Algebra: A Promising Framework for Computer Vision, Robotics and Learning

  • Eduardo Bayro-Corrochano
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3287)

Abstract

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.

Keywords

Support Vector Machine Inverse Kinematic Stereo Vision Geometric Algebra Object Manipulation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

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    Bayro-Corrochano, E.: Conformal geometric algebra for robot perception. In: Bayro-Corrochano, E. (ed.) The Hanbook on Computational Geometry for Patter Recognition, Computer Vision, Neuralcomputing and Robotics. ch. 11, Springer, Heidelberg (2004) (to appear)Google Scholar
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    Li, H., Hestenes, D., Rockwood, A.: Generalized homogeneous coordinates for computational geometry. In: Sommer, G. (ed.) Geometric Computing with Clifford Algebra, pp. 27–59. Springer, Heidelberg (2001)Google Scholar
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    Vapnik, V.: Statistical Learning Theory. Wiley, New York (1998)MATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Eduardo Bayro-Corrochano
    • 1
  1. 1.Computer Science Department, GEOVIS LaboratoryCentro de Investigación y de Estudios Avanzados, CINVESTAVGuadalajaraMexico

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