Pattern recognition system and method using Gabor functions

摘要

A pattern recognition system and method which generates a feature vector by multiplying an image vector with a sparse matrix. The sparse matrix is generated from a Gabor function which is a sinusoidal wave multiplied by a Gaussian function. The Gabor function is a function of a set of parameters including a parameter related to the direction of the sinusoidal wave, a parameter related to a center of the Gabor function, and a parameter related to a wavelength of the sinusoidal wave. The wavelength takes at least two values, with a first wavelength value lower than or substantially equal to the distance between two adjacent centers of the Gabor function, and the first wavelength value is lower than a second wavelength value and higher than or substantially equal to half the second wavelength value.

主权项

1. A method for identifying a pattern in an input image, comprising the steps of
a) normalizing the input image to a normalized matrix representing a normalized image, b) generating an image vector from the normalized matrix, c) multiplying the image vector with a sparse matrix using a matrix vector multiplication to generate a feature vector wherein the sparse matrix is generated from a Gabor function which is a sinusoidal wave multiplied by a Gaussian function and wherein the Gabor function is a function of at least one variable indicating a position in the normalized matrix and of a set of parameters including a parameter related to the direction of the sinusoidal wave, a parameter related to a centre of the Gabor function, and a parameter related to a wavelength of the sinusoidal wave, d) creating with the feature vector a density of probability for a predetermined list of models, e) selecting the model with the highest density of probability as the best model, and f) classifying the best model as the pattern of the input image, wherein there are at least two centres of the Gabor function, and wherein the wavelength takes at least two values, with a first wavelength value lower than or substantially equal to the distance between two adjacent centres of the Gabor function, and the first wavelength value is lower than a second wavelength value and higher than or substantially equal to half the second wavelength value
wherein the models are characterized by a covariance matrix and by an average vector,wherein the density of probability is calculated by the formulap⁡(r)=1(2⁢π)k⁢∑⁢exp[-(r-μ)t⁢∑(r-μ)2], wherein the symbol r represents the feature vector, the symbol Σ represents the covariance matrix, the symbol μ represents the average vector and k is equal to the number of elements of the feature vector, wherein the set of parameters of the Gabor function includes the standard deviation of the Gaussian function which takes values lower than the distance between two adjacent centres of the Gabor function and higher than the half distance between two adjacent centres of the Gabor function.