Shape description and invariant recognition employing connectionist approach

This paper presents a new approach for shape description and invariant recognition by geometric-normalization implemented by neural networks. The neural system consists of a shape description network, a normalization network and a recognition stage based on fuzzy pyramidal neural networks. The description network uses a novel approach for hierarchical shape segmentation and representation which expands the image shapes into localized feature tokens. These feature tokens form a compact description of the shape and its components that include information on their location, size and orientation. The description network, which is composed of a novel pyramidal architecture called the Vectorial Gradual Lattice Pyramid, processes in parallel a new vectorial scale space representation of the shape. A novel measure called Cancellation Energy is used to determine the feature tokens. The normalization network utilizes the location, size and orientation information in the feature tokens to geometric-normalize the shape or its components with respect to these parameters. The recognition network which has a pyramidal structure, uses a fuzzy representation of these normalized feature tokens to achieve robust invariant recognition. Experimental results demonstrate robust recognition in large variations of scale, rotation, translation and also in moderate affine transformations and partial occlusion.