Using a Neural Net to Instantiate a Deformable Model
Christopher Williams, Michael Revow
and Geoffrey E. Hinton
Department of Computer Science
University of Toronto
Deformable models are an attractive approach to recognizing
non-rigid objects which have considerable within class variability. However, there
are severe search problems associated with fitting the models to data. We show that
by using neural networks to provide better starting points, the search time can be
significantly reduced. The method is demonstrated on a character recognition task.
In previous work we have developed an approach to handwritten
character recognition based on the use of deformable models (Hinton, Williams and Revow,
1992a; Revow, Williams and Hinton, 1993). We have obtained good performance
with this method, but a major problem is that the search procedure for fitting each model
to an image is very computationally intensive, because there is no efficient algorithm
(like dynamic programming) for this task. In this paper we demonstrate that it is
possible to 'compile down' some of the knowledge gained while fitting models to data to
obtain better starting points that significantly reduce the search time.
Download: [postscript] [pdf]
Advances in Neural Information Processing Systems 7. G. Tesauro,
D. S. Touretzky and T. K. Leen (Eds), pp 965-972 MIT Press, Cambridge MA.
[home page] [publications]