Illumination Planning for Object Recognition Using Parametric Eigenspaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Principal component neural networks: theory and applications
Principal component neural networks: theory and applications
EM algorithms for PCA and SPCA
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Robust recognition using eigenimages
Computer Vision and Image Understanding - Special issue on robusst statistical techniques in image understanding
A Bayesian Computer Vision System for Modeling Human Interactions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Linear fitting with missing data for structure-from-motion
Computer Vision and Image Understanding
Principal Component Analysis with Missing Data and Its Application to Polyhedral Object Modeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Dynamic Appearance-Based Recognition
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Face recognition from one example view
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Robust Localization Using Panoramic View-Based Recognition
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
Journal of Cognitive Neuroscience
Robust principal component analysis by self-organizing rules based on statistical physics approach
IEEE Transactions on Neural Networks
A Framework for Robust Subspace Learning
International Journal of Computer Vision - Special Issue on Computational Vision at Brown University
Incremental and robust learning of subspace representations
Image and Vision Computing
Subspace manifold learning with sample weights
Image and Vision Computing
MIRAGE '09 Proceedings of the 4th International Conference on Computer Vision/Computer Graphics CollaborationTechniques
SCIA '09 Proceedings of the 16th Scandinavian Conference on Image Analysis
A framework for robust and incremental self-localization of a mobile robot
ICVS'03 Proceedings of the 3rd international conference on Computer vision systems
Eigenmotion-based detection of intestinal contractions
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
Robust least-squares image matching in the presence of outliers
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
On-Line, incremental learning of a robust active shape model
DAGM'06 Proceedings of the 28th conference on Pattern Recognition
Long term carefully learning for person detection application to intelligent surveillance system
Proceedings of the 9th International Conference on Advances in Mobile Computing and Multimedia
Conservative visual learning for object detection with minimal hand labeling effort
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
An eigenbackground subtraction method using recursive error compensation
PCM'06 Proceedings of the 7th Pacific Rim conference on Advances in Multimedia Information Processing
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Appearance-based modeling of objects and scenes using PCA has been successfully applied in many recognition tasks. Robust methods which have made the recognition stage less susceptible to outliers, occlusions, and varying illumination have further enlarged the domain of applicability. However, much less research has been done in achieving robustness in the learning stage. In this paper, we propose a novel robust PCA method for obtaining a consistent subspace representation in the presence of outlying pixels in the training images. The method is based on the EM algorithm for estimation of principal subspaces in the presence of missing data. By treating the outlying points as missing pixels, we arrive at a robust PCA representation. We demonstrate experimentally that the proposed method is efficient. In addition, we apply the method to a set of panoramic images to build a representation that enables surveillance and view-based mobile robot localization.