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A practical neural network model for morphological filtering and a simulated annealing optimal algorithm for the network parameters training are proposed in this paper. It is pointed out that the opti- mal designing process of the morphological filtering network in fact is the optimal learning process of adjusting network parameters (structuring element, or SE for short) to accommodate image environment. Then the network structure may possess the characteristics of image targets, and so give specific infor- mation to the SE. Morphological filters formed in this way become certainly intelligent and can provide good filtering results and robust adaptability to complex changing image. For application to motional image target detection, dynamic training algorithm is applied to the designing process using asymptotic shrinking error and appropriate network weights adjusting. Experimental results show that the algorithm has invariant property with respect to shift, scale and rotation of moving target in continuing detection of moving targets.
A practical neural network model for morphological filtering and a simulated annealing optimal algorithm for the network parameters training are proposed in this paper. It is pointed out that the opti- mal designing process of the morphological filtering network in fact is the optimal learning process of adjusting Then the network structure may possess the characteristics of image targets, and so give specific infor- mation to the SE. network parameters (structuring element, or SE for short) to accommodate image environment. provide good filtering results and robust adaptability to complex changing image. For application to motional image target detection, dynamic training algorithm is applied to the designing process using asymptotic shrinking error and appropriate network weights adjusting. Experimental results show that the algorithm has invariant property with respect to shift, scale and rotation of movin g target in continuing detection of moving targets.