MATNET: A neural network for medial axis transformation
Yung‐Sheng Chen, Wen‐Hsing Hsu · Journal of the Chinese Institute of Engineers · 1993
This paper describes a novel neural network, called MATNET, to perform the medial axis transformation which is often used to extract a stick‐figure‐like representation from a binary object for pattern analysis or recognition. The MATNET is derived from the structure of the retina, which consists of five neural layers, namely, receptors, horizontal cells, bipolar cells, ganglion cells, and response. In principle, the horizontal cell is implemented for distance computation; the bipolar cell (B‐net) and the ganglion cell (G‐net) are implemented for calculation of local minimum and local maximum, respectively. The B‐net and G‐net are concerned with the maximal neural network (Maxnet). The properties of Maxnet are also discussed. Experimental results show that the MATNET performs reasonably.