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The cellular neural/nonlinear network(CNN)is a powerful tool for image and video signal processing,robotic and biological visions.This paper discusses a general method for designing template of the global connectivitydetection(GCD)CNN,which provides parameter inequalities for determining parameter intervals for implementing thecorresponding functions.The GCD CNN has stronger ability and faster rate for determining global connectivity in binarypatterns than the GCD CNN proposed by Zarandy.An example for detecting the connectivity in complex patterns isgiven.
The cellular neural / nonlinear network (CNN) is a forcing tool for image and video signal processing, robotic and biological visions. This paper discusses a general method for designing template of the global connectivity section (GCD) CNN, which provides parameter inequalities for determining parameter intervals for implementing thecorresponding functions. GCD CNN has stronger ability and faster rate for determining global connectivity in binarypatterns than the GCD CNN proposed by Zarandy. An example for detecting the connectivity in complex patterns isgiven.