A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Self-Organizing Maps
SIGGRAPH '78 Proceedings of the 5th annual conference on Computer graphics and interactive techniques
Theoretical and Experimental Analysis of a Two-Stage System for Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural robot detection in robocup
Biomimetic Neural Learning for Intelligent Robots
Orientation histograms for face recognition
ANNPR'06 Proceedings of the Second international conference on Artificial Neural Networks in Pattern Recognition
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An object classification system built of a simple colour based visual attention method, and a prototype based hierarchical classifier is established as a link between subsymbolic and symbolic data processing. During learning the classifier generates a hierarchy of prototypes. These prototypes constitute a taxonomy of objects. By assigning confidence values to the prototypes a classification request may also return symbols with confidence values. For performance evaluation the classifier was applied to the task of visual object categorization of three data sets, two real-world and one artificial. Orientation histograms on subimages were utilized as features.With the currently very simple feature extraction method, classification accuracies in the range of 69% to 90% were attained.