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 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. © 1997 Academic Press.
| Original language | English |
|---|---|
| Pages (from-to) | 120-126 |
| Number of pages | 7 |
| Journal | Computer Vision and Image Understanding |
| Volume | 68 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 1 Oct 1997 |
Bibliographical note
NOTICE: this is the author’s version of a work that was accepted for publication in Computer Vision and Image Understanding. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Williams, Christopher K. I.; Revow, Michael and Hinton, Geoffrey E. (1997). Instantiating deformable models with a neural net. Computer Vision and Image Understanding, 68 (1), 120-126. DOI http://dx.doi.org/10.1006/cviu.1997.0540Keywords
- deformable models
- within-class variability
- handwritten digits