Statistical model of interpolation and its application to texture segmentation

摘要

The work addresses the problem of pattern recognition as the task of n-dimensional interpolation of the probability density function with subsequent thresholding. Interpolating function that describes given learning data set is treated as a random process. The task is reduced to simultaneous linear equations. The proposed method can be considered as the construction of an RBF neural network with nonstandard transfer function. The transfer function is chosen to minimize energy of the random process spectrum. Applications of the developed algorithm to the task of texture segmentation are given and comparison with alternative approaches is carried out.