Name
Abstract | ||
|---|---|---|
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 |
Year | DOI | Venue |
|---|---|---|
1998 | 10.1109/ICPR.1998.711258 | Pattern Recognition, 1998. Proceedings. Fourteenth International Conference |
Keywords | Field | DocType |
Bayes methods,automatic optical inspection,image matching,image texture,learning systems,least squares approximations,maximum likelihood estimation,textile industry,Bayes method,deformable templates,image analysis,learning systems,least squares,maximum likelihood estimation,parameter estimation,supervised estimation,textile inspection,unsupervised estimation | Training set,Least squares,Pattern recognition,Expectation–maximization algorithm,Image texture,Computer science,Artificial intelligence,Template,Estimation theory,Bayesian formulation,Model parameter | Conference |
Volume | ISSN | ISBN |
1 | 1051-4651 | 0-8186-8512-3 |
Citations | PageRank | References |
3 | 0.47 | 3 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rune Fisker | 1 | 33 | 3.96 |
| Jens Michael Carstensen | 2 | 83 | 14.27 |