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 geome...

• Type: Journal
• Year: 2002
• Where: IJPRAI
• Authors: Jezekiel Ben-Arie, Zhiqian Wang
• Keywords: Description Network, IJPRAI 2002, Neural Networks, Shape Description