A New Neural Model for Invariant Pattern Recognition.
For most of the pattern recognition applications, it is often required to correctly recognize patterns even if they have variations in position, rotation, and/or scale. In this paper, to achieve the goal of invariant pattern recognition we propose a new neural model which consists of a cascade connection of four two-dimensional layers. The first three layers of the neural model perform the processes of position normalization, rotation normalization and feature extraction, respectively. The last layer is responsible for both recognition job and scale normalization by specially designing its output neurons to possess a scale invariant property. Finally, simulation results are given to demonstrate that the proposed model is simple and effective for invariant pattern recognition. Copyright 1996 Elsevier Science Ltd
Wang
Shuenn Shyang
SS
Department of Electrical Engineering, Tatung Institute of Technology, 40 Chungshan North Road, 3rd Sec., Teipei, Taiwan, People's Republic of China
Lin
Wen Gou
WG