Robust regression and outlier detection
Robust regression and outlier detection
Illumination Planning for Object Recognition Using Parametric Eigenspaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
The nature of statistical learning theory
The nature of statistical learning theory
Segmentation and recognition of 3D objects using parametric eigenspace representation
Selected papers from the 9th Scandinavian conference on Image analysis : theory and applications of image analysis II: theory and applications of image analysis II
Probabilistic Visual Learning for Object Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation
International Journal of Computer Vision
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Robust recognition using eigenimages
Computer Vision and Image Understanding - Special issue on robusst statistical techniques in image understanding
Independent component analysis: algorithms and applications
Neural Networks
Stochastic Complexity in Statistical Inquiry Theory
Stochastic Complexity in Statistical Inquiry Theory
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
ExSel++: A General Framework to Extract Parametric Models
CAIP '95 Proceedings of the 6th International Conference on Computer Analysis of Images and Patterns
Optic flow calculation using robust statistics
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Dynamic Appearance-Based Recognition
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Appearance Matching of Occluded Objects Using Coarse-to-fine Adaptive Masks
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Robust Recognition of Scaled Eigenimages through a Hierarchical Approach
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Mixtures of Local Linear Subspaces for Face Recognition
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Face recognition from one example view
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
How Are Three-Deminsional Objects Represented in the Brain?
How Are Three-Deminsional Objects Represented in the Brain?
An Eigenspace Update Algorithm for Image Analysis
An Eigenspace Update Algorithm for Image Analysis
Journal of Cognitive Neuroscience
Steerable wedge filters for local orientation analysis
IEEE Transactions on Image Processing
Learning in linear neural networks: a survey
IEEE Transactions on Neural Networks
Image statistics: from data collection to applications in graphics
ACM SIGGRAPH 2010 Courses
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The appearance-based approaches to vision problems have recently received a renewed attention in the vision community due to their ability to deal with combined effects of shape, reflectance properties, pose in the scene, and illumination conditions. Besides, appearance-based representations can be acquired through an automatic learning phase which is not the case with traditional shape representations. The approach has led to a variety of successful applications, e. g., visual positioning and tracking of robot manipulators, visual inspection, and human face recognition. In this paper we will review the basic methods for appearance-based object recognition. We will also identify the major limitations of the standard approach and present algorithms how these limitations can be alleviated leading to an object recognition system which is applicable in real world situations.