Illumination Planning for Object Recognition Using Parametric Eigenspaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic Visual Learning for Object Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition by Elastic Bunch Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
SFS Based View Synthesis for Robust Face Recognition
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Learning appearance and transparency manifolds of occluded objects in layers
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
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Variations in camera-captured images usually occur naturally. For example, the appearance of an object usually differs for every pose and degradation effect might occur during the capturing process. While we could use a simple manifold to represent the variability of pose, relying on the simple manifold technique to deal with both pose and degradation problems is not possible, since a simple manifold does not take into account the information of sample distributions in feature space. In this paper, we propose a technique which embeds viewdependent covariance matrix in object manifold to develop a robust 3D object recognition system. Here, the view-dependent covariance matrices were obtained in an efficient way by interpolating eigenvectors and eigenvalues along the manifold. Experiment results showed that our developed 3D object recognition system could accurately recognize 3D objects even from images which are influenced by geometric distortions and quality degradation effects.