Chapter 3. Learning Shape and Motion from Image Sequences

  1. Simon Haykin
  1. Gaurav S. Patel1,
  2. Sue Becker2 and
  3. Ron Racine2

Published Online: 13 MAR 2002

DOI: 10.1002/0471221546.ch3

Kalman Filtering and Neural Networks

Kalman Filtering and Neural Networks

How to Cite

Patel, G. S., Becker, S. and Racine, R. (2001) Learning Shape and Motion from Image Sequences, in Kalman Filtering and Neural Networks (ed S. Haykin), John Wiley & Sons, Inc., New York, USA. doi: 10.1002/0471221546.ch3

Editor Information

  1. Communications Research Laboratory, McMaster University, Hamilton, Ontario, Canada

Author Information

  1. 1

    Department of Electrical and Computer Engineering, McMaster University, Hamilton, Ontario, Canada

  2. 2

    Department of Psychology, McMaster University, 1280 Main Street West, Hamilton, Ontario, Canada L8S 4K1

Publication History

  1. Published Online: 13 MAR 2002
  2. Published Print: 1 OCT 2001

Book Series:

  1. Wiley Series on Adaptive and Learning Systems for Signal Processing, Communications, and Control

Book Series Editors:

  1. Simon Haykin

Series Editor Information

  1. Communications Research Laboratory, McMaster University, Hamilton, Ontario, Canada

ISBN Information

Print ISBN: 9780471369981

Online ISBN: 9780471221548

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Keywords:

  • shape;
  • motion;
  • image sequences;
  • neurological foundations;
  • perceptual foundations;
  • network description;
  • experiments

Summary

The node-decoupled extended Kalman filter (NDEKF) algorithm is used to deal with high-dimensional signals: moving visual images. Many complexities arise in visual processing that are not present in one-dimensional prediction problems: the scene may be cluttered with background objects, the object of interest may be occluded, and the system may have to deal with tracking differently shaped objects at different times. This chapter looks at the problem of tracking objects that vary in both shape and location. A neural network model makes use of short-term continuity to track a range of different geometric shapes. Three experiments are presented to evaluate the model's abilities.