Probability Models for Clutter in Natural Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
On Advances in Statistical Modeling of Natural Images
Journal of Mathematical Imaging and Vision
Universal Analytical Forms for Modeling Image Probabilities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Analytical Image Models and Their Applications
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Analysis of Planar Shapes Using Geodesic Paths on Shape Spaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Intrinsic Bayesian Active Contours for Extraction of Object Boundaries in Images
International Journal of Computer Vision
Joint manifolds for data fusion
IEEE Transactions on Image Processing - Special section on distributed camera networks: sensing, processing, communication, and implementation
| Hi-index | 754.84 |
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