Face Recognition by Elastic Bunch Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Network-Based Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distortion Invariant Object Recognition in the Dynamic Link Architecture
IEEE Transactions on Computers
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Finding Faces in Cluttered Still Images with Few Examples
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
A Self-Organizing Network that Can Follow Non-stationary Distributions
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Maplets for correspondence-based object recognition
Neural Networks - 2004 Special issue: New developments in self-organizing systems
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We present a method which continuously learns representations of arbitrary objects. These object representations can be stored with minimal user interaction (1-Click Learning). Appropriate training material has the form of image sequences containing the object of interest moving against a cluttered static background. Using basically the method of unsupervised growing neural gas modified to adapt to non-stationary distributions on binarized difference images, a model of the moving object is learned in real-time. Using the learned object representation the system can recognize the object or objects of the same class in single still images of different scenes. The new samples can be added to the learned object model to further improve it.