Implicit Curve Based on Radial Basis Function Network

A new method for closed curve construction is introduced, which is based on the combination of RBF (radial basis function) neural network and the principle of i mplicit curve construction. The algorithm, firstly, constructs the input and output of the RBF neural network from the constraint points, secondly changes the implicit function that represents object boundary into explicit function, thirdly uses RBF neural network to fit the curve of the explicit function, and finally obtains the fitting curves that represent the object boundary from the simulation surface. The main difference between the new method and other methods is that it is unnecessary for the new method to minimize the sum of the squares of the E uclidean distance or to solve linear system. The method not only has better resu lts than the method based on BP neural network, and also has some merit of local ity that other methods do not have. It has good numerical stability and robustne ss in dealing with noisy or missing data. Experimental results are given to veri fy the effectiveness of recovering incomplete images and object boundary reconst ruction.