Name
Abstract | ||
|---|---|---|
This article investigates the asymptotic performance of Bayesian target recognition algorithms using deformable-template representations. Rigid computer-aided design (CAD) models represent the underlying targets; low-dimensional matrix Lie-groups (rotation and translation) extend them to particular instances. Remote sensors observing the targets are modeled as projective transformations, converting three-dimensional scenes into random images. Bayesian target recognition corresponds to hypothesis selection in the presence of nuisance parameters; its performance is quantified as the Bayes' error. Analytical expressions for this error probability in small noise situations are derived, yielding asymptotic error rates for exponential error probability decay |
Year | DOI | Venue |
|---|---|---|
2000 | 10.1109/18.850712 | IEEE Transactions on Information Theory |
Keywords | Field | DocType |
Bayes methods,CAD,Lie groups,clutter,error statistics,image recognition,image representation,matrix algebra,random processes,remote sensing,3D scenes,Bayes error,Bayesian target recognition algorithms,asymptotic error rates,asymptotic performance analysis,clutter,computer-aided design models,deformable-template representations,exponential error probability decay,hypothesis selection,low-dimensional matrix Lie-groups,noise,nuisance parameters,performance,projective transformations,random images,remote sensors,rotation,translation | CAD,Lie group,Pattern recognition,Expression (mathematics),Clutter,Computer science,Matrix (mathematics),Stochastic process,Artificial intelligence,Bayesian probability,Bayes' theorem | Journal |
Volume | Issue | ISSN |
46 | 4 | 0018-9448 |
Citations | PageRank | References |
11 | 3.08 | 7 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ulf Grenander | 1 | 308 | 80.59 |
| Anuj Srivastava | 2 | 2853 | 199.47 |
| Michael I Miller | 3 | 3123 | 422.82 |