Christopher K. I. Williams, Michael Revow and Geoffrey E. Hinton. Department of Computer Science, University of Toronto
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.