Instantiating Deformable Models with a Neural Net
Christopher K. I. Williams, Michael Revow
and Geoffrey E. Hinton
Department of Computer Science
University of Toronto
Abstract
Deformable models are an attractive approach to recognizing objects
which have considerable within-class variability such as handwritten characters. However,
there are severe search problems associated with fitting the models to data which could be
reduced if a better starting point for the search were available. We show that by training
a neural network to predict how a deformable model should be instantiated from an input
image, such improved staring points can be obtained. This method has been implemented for
a system that recognizes handwritten digits using deformable models and the results show
that the search time can be significantly reduced without compromising recognition
performance.
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Computer Vision and Image Understanding, Vol. 68, No. 1, Oct
1997, pp. 120-126
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