Gray-scale image edge detection method based on pulse coupling neural network
A pulse-coupled neural and gray-scale image technology, applied in image analysis, image data processing, instruments, etc., can solve the problems of widening and discontinuity of borders, achieve the effect of increasing processing speed and overcoming widening of edges
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[0026] The beneficial effects of the present invention will be described in detail below in conjunction with the examples, aiming to help readers better understand the essence of the present invention, but not to limit the implementation and protection scope of the present invention.
[0027] First, construct a single-layer two-dimensional, locally connected pulse-coupled neural network, in which the number of neurons is equal to the number of pixels in the grayscale image, and the neurons correspond to and connect to each pixel one by one, and are connected to the adjacent neurons at the same time. Connection, the constructed model is as follows:
[0028] f jk [i]=S jk +F jk [i-1]·e αF +V F ·(M*Y[i-1]) jk
[0029] L jk [i]=L jk [i-1]·e αL +V L ·(K*Y[i-1]) jk ,
[0030] In the formula, K and M are connection weight matrices, * means convolution operation, Y is the information of neuron firing or not, αL and αF are time decay constants, V L and V F is the connecti...
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