An introduction to splines for use in computer graphics & geometric modeling
An introduction to splines for use in computer graphics & geometric modeling
Principal Warps: Thin-Plate Splines and the Decomposition of Deformations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hands: a pattern theoretic study of biological shapes
Hands: a pattern theoretic study of biological shapes
Bayesian methods for adaptive models
Bayesian methods for adaptive models
Steerable-scalable kernels for edge detection and junction analysis
Image and Vision Computing - Special issue: 2nd European Conference on Computer Vision
Active shape models—their training and application
Computer Vision and Image Understanding
An Active Testing Model for Tracking Roads in Satellite Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern theory: a unifying perspective
Perception as Bayesian inference
Prior Learning and Gibbs Reaction-Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
What Is the Set of Images of an Object Under All Possible Illumination Conditions?
International Journal of Computer Vision
Modeling surround suppression in V1 neurons with a statistically-derived normalization model
Proceedings of the 1998 conference on Advances in neural information processing systems II
X Vision: Combining Image Warping and Geometric Constraints for Fast Visual Tracking
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume II - Volume II
Learning bilinear models for two-factor problems in vision.
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
A unified approach to coding and interpreting face images
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Alignment by maximization of mutual information
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Region tracking through image sequences
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Object Localization by Bayesian Correlation
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Separability of Pose and Expression in Facial Tracking and Animation
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Automatic Detection and Tracking of Human Motion with a View-Based Representation
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Switching observation models for contour tracking in clutter
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
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A Bayesian approach to object localisation is feasible given suitable likelihood models for image observations. Such a likelihood involves statistical modelling--and learning--both of the object foreground and of the scene background. Statistical background models are already quite well understood. Here we propose a "conditioned likelihood" model for the foreground, conditioned on variations both in object appearance and illumination. Its effectiveness in localising a variety of objects is demonstrated.