Recognition by Linear Combinations of Models
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part I
Geometry and photometry in three-dimensional visual recognition
Geometry and photometry in three-dimensional visual recognition
The nature of statistical learning theory
The nature of statistical learning theory
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Kernel PCA and de-noising in feature spaces
Proceedings of the 1998 conference on Advances in neural information processing systems II
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
View-Based Adaptive Affine Tracking
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Feature Correspondence by Interleaving Shape and Texture Computations
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Learning A Single Active Face Shape Model across Views
RATFG-RTS '99 Proceedings of the International Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems
Linear Object Classes and Image Synthesis from a Single Example Image
Linear Object Classes and Image Synthesis from a Single Example Image
Algebraic Functions for Recognition
Algebraic Functions for Recognition
Journal of Cognitive Neuroscience
Manifold estimation in view-based feature space for face synthesis across poses
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part I
| Hi-index | 0.00 |
In principle, the recovery and reconstruction of a 3D object from its 2D view projections require the parameterisation of its shape structure and surface reflectance properties. Explicit representation and recovery of such 3D information is notoriously difficult to achieve. Alternatively, a linear combination of 2D views can be used which requires the establishment of dense correspondence between views. This in general, is difficult to compute and necessarily expensive. In this paper we examine the use of affine and local feature-based transformations in establishing correspondences between very large pose variations. In doing so, we utilise a generic-view template, a generic 3D surface model and Kernel PCA for modelling shape and texture nonlinearities across views. The abilities of both approaches to reconstruct and recover faces from any 2D image are evaluated and compared.