Abstract
The “Point Distribution Model”, derived by analysing the modes of variation of a set of training examples, can be a useful tool in machine vision. One of the drawbacks of this approach to date is that the training data is acquired with human intervention where fixed points must be selected by eye from example images. A method is described for generating a similar flexible shape model automatically from real image data. A cubic B-spline is used as the shape vector for training the model. Large training sets are used to generate a robust model of the human profile for use in the labelling and tracking of pedestrians in real-world scenes. Furthermore, an extended model is described which incorporates direction of motion, allowing the extrapolation of direction from shape.
Chapter PDF
Similar content being viewed by others
Keywords
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
Kass M., Witkin A., and Terzopoulos D. Snakes: Active contour models. First International Conference on Computer Vision, pages 259–268, 1987.
Yuille A.L., Cohen D.S., and Hallinan P. Feature extraction from faces using deformable templates. Computer Vision and Pattern Recognition, pages 104–109, 1989.
Blake A., Curwen R., and Zisserman A. A framework for spatio-temporal control in the tracking of visual contours. International Journal of computer Vision, 1993.
Cootes T.J., Taylor C.J., Cooper D.H., and Graham J. Training models of shape from sets of examples. In British Machine Vision Conference, pages 9–18, September 1992.
Hogg D. Model-based vision: A program to see a walking person. Image and Vision Computing, 1(1):5–20, 1983.
Rohr K. Incremental recognition of pedestrians from image sequences. Computer Vision and Pattern Recognition, pages 8–13, 1993.
Pentland A. and Horowitz B. Recovery of non-rigid motion and structure. IEEE Trans. on Pattern Analysis and Machine Intelligence, 13(7):730–742, July 1991.
Murphy N., Byrne N., and O'Leary K. Long sequence analysis of human motion using eigenvector decomposition. In Proc. SPIE, September 1993.
Worrall A. and Hyde J. A fast algorithm for background generation. VIEWS Working Paper RU-03-WP-T.1.1.1.1-1.
Hill A., Thornham A., and Taylor C.J. Model-based interpretation of 3d medical images. In British Machine Vision Conference, volume 2, pages 339–349, 1993.
Bartels R., Beatty J., and Barsky B. An Introduction to Splines for use in Computer Graphics and Geomteric Modeling. Morgan Kaufmann, 1987.
Li-Qun X., Young D., and Hogg D. Building a model of a road junction using moving vehicle information. In British Machine Vision Conference, pages 443–452, September 1992.
Cootes T.F. and Taylor C.J. Active shape models — 'smart snakes'. In British Machine Vision Conference, pages 276–285, September 1992.
Shapiro L. and Brady M. Rejecting outliers and estimating errors in an orthogonal regression framework. Ouel, Robotics Research Group, University of Oxford, February 1993.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1994 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Baumberg, A., Hogg, D. (1994). Learning flexible models from image sequences. In: Eklundh, JO. (eds) Computer Vision — ECCV '94. ECCV 1994. Lecture Notes in Computer Science, vol 800. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-57956-7_34
Download citation
DOI: https://doi.org/10.1007/3-540-57956-7_34
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-57956-4
Online ISBN: 978-3-540-48398-4
eBook Packages: Springer Book Archive