Object recognition with structural descriptions and deformable models
This paper presents first results for an object recognition system based on a viewer centered approach. Object models are derived from a self-organizing neural net using a variant of the Neural Gas algorithm. Neurons are generated and shifted according to spatial locations of geometrical features. As a result, a graph oriented representation of the neurons positions and their interconnections reflect the structure of the model objects. The matching process utilizes a deformation strategy to adapt the topology of the model graphs to the features in a presented scene. The amount of deformation for the resulting graphs together with an analysis of gray value moments indicate the objects shown in the scene.
object recognition structural descriptions deformable models