Neural Networks, IEEE - INNS - ENNS International Joint Conference on
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Abstract

In this paper we present an approach for contour based object representation. To this end we use a curvature signal gained by a level-set segmentation method. The advantage of that curvature signal is that it generates no computational overhead as it is a byproduct of standard level-set segmentation methods. Different methods for the description of the segmented objects, so called object descriptors are presented. The object descriptors are all invariant against translation, rotation and scale of the object. Furthermore we show a sparse and memory efficient representation of the descriptors for a series of objects. Finally an approach for classification of unknown objects based on “memorized” objects is proposed.
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