Conditional-mean estimation via jump-diffusion processes inmultiple target tracking/recognition
IEEE Transactions on Signal Processing
Automatic target recognition organized via jump-diffusion algorithms
IEEE Transactions on Image Processing
Probability Models for Clutter in Natural Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Order Parameters for Detecting Target Curves in Images: When Does High Level Knowledge Help?
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: Part II
Group Actions, Homeomorphisms, and Matching: A General Framework
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: Part II
On Advances in Statistical Modeling of Natural Images
Journal of Mathematical Imaging and Vision
Target-Centered Models and Information-Theoretic Segmentation for Automatic Target Recognition
Multidimensional Systems and Signal Processing
Information-Theoretic Bounds on Target Recognition Performance Based on Degraded Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Guest Editorial: Computational Vision at Brown
International Journal of Computer Vision - Special Issue on Computational Vision at Brown University
Intrinsic Statistics on Riemannian Manifolds: Basic Tools for Geometric Measurements
Journal of Mathematical Imaging and Vision
Statistical Computing on Manifolds: From Riemannian Geometry to Computational Anatomy
Emerging Trends in Visual Computing
Joint manifolds for data fusion
IEEE Transactions on Image Processing - Special section on distributed camera networks: sensing, processing, communication, and implementation
Computer Vision and Image Understanding
Statistics of shape via principal geodesic analysis on lie groups
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Image and Vision Computing
Fitting smoothing splines to time-indexed, noisy points on nonlinear manifolds
Image and Vision Computing
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Deformable template representations of observed imagery, model the variability of target pose via the actions of the matrix Lie groups on rigid templates. In this paper, we study the construction of minimum mean squared error estimators on the special orthogonal group, SO(n), for pose estimation. Due to the nonflat geometry of SO(n), the standard Bayesian formulation, of optimal estimators and their characteristics, requires modifications. By utilizing Hilbert-Schmidt metric defined on GL(n), a larger group containing SO(n), a mean squared criterion is defined on SO(n). The Hilbert-Schmidt estimate (HSE) is defined to be a minimum mean squared error estimator, restricted to SO(n). The expected error associated with the HSE is shown to be a lower bound, called the Hilbert-Schmidt bound (HSB), on the error incurred by any other estimator. Analysis and algorithms are presented for evaluating the HSE and the HSB in case of both ground-based and airborne targets.