Learning of boundary patterns to recognize the gradually changed patterns within the boundary
Chia-Lun John Hu · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2005
If the binary image pattern, e.g., the edge-detected boundary of an object, is varying in real time among several extreme boundaries, then learning just the extreme boundaries by an OLNN (one-layered neural network) will allow the OLNN to recognize any unlearned, time-varying patterns of the object varying among these extreme boundaries. This is possible because of the unique property of CONVEX LEARNING existing in the OLNN. This paper will first derive this property from mathematical point of view, and then verify it with some simple experiments. The main advantage of this neural network is that it can recognize very similar objects not only from the static patterns it learns but also from the ways how these objects vary in real time even these varying patterns are NOT learned one by one at each time. Consequently the recognition is much more accurate than just learning the static patterns alone.