Conference paper
On parameter estimation in deformable models
Deformable templates have been intensively studied in image analysis through the last decade, but despite its significance the estimation of model parameters has received little attention. We present a method for supervised and unsupervised model parameter estimation using a general Bayesian formulation of deformable templates.
In the supervised estimation the parameters are estimated using a likelihood and a least squares criterion given a training set. For most deformable template models the supervised estimation provides the opportunity for simulation of the prior model. The unsupervised method is based on a modified version of the EM algorithm.
Experimental results for a deformable template used for textile inspection are presented
| Language: | English |
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| Publisher: | IEEE |
| Year: | 1998 |
| Pages: | 762-766 |
| Proceedings: | 14th International Conference on Pattern Recognition |
| Series: | International Conference on Pattern Recognition |
| ISBN: | 0818685123 and 9780818685125 |
| ISSN: | 10514651 |
| Types: | Conference paper |
| DOI: | 10.1109/ICPR.1998.711258 |
| ORCIDs: | Carstensen, Jens Michael |
Bayes method Bayes methods Bayesian methods Deformable models Guidelines Image analysis Inspection Mathematical model Minimax techniques Parameter estimation Tiles automatic optical inspection deformable templates image analysis image matching image texture learning systems least squares least squares approximations maximum likelihood estimation parameter estimation supervised estimation textile industry textile inspection unsupervised estimation
