The comparative recognition of monochrome and color images using networks of n-tuple and Min/Max nodes utilizing ‘grouped nodes’

This paper documents the results obtainable when either networks of n-tuple (NT) logic nodes or Min/Max (MM) nodes are used to recognize monochrome and color images. Networks of n-tuple nodes are based upon the n-tuple methodology originated by Bledsoe and Browning. Networks of Min/Max nodes employ similar techniques but are capable of directly processing grey-scale values. Both networks are illustrated with appropriate diagrams. The comparative results indicate that color recognition generally provides improved confidence levels. Also, overall, for `equivalent´ networks there are insignificant differences amongst the responses. By means of implementing grouping of the nodes, higher confidence levels can be obtained. Of importance, if a suitable threshold is applied to the summed values of grouped nodes, the nets can be configured to provide confidence levels approaching 100%. The documented methodologies are relatively simple to implement in either hardware or software, easy to use and provide rapid training and recognition times.