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Instantiating Deformable Models with a Neural Net

Research output: Contribution to journalArticle

Original languageEnglish
Pages (from-to)120-126
Number of pages7
JournalComputer Vision and Image Understanding
Issue number1
Publication statusPublished - Oct 1997


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 starting 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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