Elements of information theory
Elements of information theory
Discrimination thresholds for channel-coded systems
Biological Cybernetics
Support Vector Machines for 3D Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Signal Processing for Computer Vision
Signal Processing for Computer Vision
AFPAC '00 Proceedings of the Second International Workshop on Algebraic Frames for the Perception-Action Cycle
Channel Smoothing: Efficient Robust Smoothing of Low-Level Signal Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
P-Channels: Robust Multivariate M-Estimation of Large Datasets
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Reconstruction of probability density functions from channel representations
SCIA'05 Proceedings of the 14th Scandinavian conference on Image Analysis
The application of an oblique-projected Landweber method to a model of supervised learning
Mathematical and Computer Modelling: An International Journal
Problem solving through imitation
Image and Vision Computing
Integral P-channels for fast and robust region matching
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
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In this paper we propose a new approach to real-time view-based object recognition and scene registration. Object recognition is an important sub-task in many applications, as e.g., robotics, retrieval, and surveillance. Scene registration is particularly useful for identifying camera views in databases or video sequences. All of these applications require a fast recognition process and the possibility to extend the database with new material, i.e., to update the recognition system online. The method that we propose is based on P-channels, a special kind of information representation which combines advantages of histograms and local linear models. Our approach is motivated by its similarity to information representation in biological systems but its main advantage is its robustness against common distortions as clutter and occlusion. The recognition algorithm extracts a number of basic, intensity invariant image features, encodes them into P-channels, and compares the query P-channels to a set of prototype P-channels in a database. The algorithm is applied in a cross-validation experiment on the COIL database, resulting in nearly ideal ROC curves. Furthermore, results from scene registration with a fish-eye camera are presented.