In this paper

Multi-Scale two-dimensional wavelet transform is imported to analysze fabric surface wrinkle in order to acquire the finer image information. Firstly

fabric image can be filtered through Gaussian filter

and decomposed by wavelet transform; meanwhile

high frequency information is extracted. Secondly

four kinds of wrinkle feature parameter are applied to calculate the fabric wrinkle degree with different wrinkle replica

which are horizontal variance

vertical variance

horizontal offset and vertical offset separately. Through analyzing the correlation coefficient between feature parameter and wrinkle grade

which indicates the four kinds of wrinkle feature parameter can be taken as the input value for pattern recognition. Finally

Kohonen self-organized neural network is also used to evaluate fabric wrinkle grade objectively. The wrinkle feature parameters are input to the Kohonen self-organized neural network

through training and studying process

the output value can be obtained

different wrinkle grade of fabric replica will be classified by applying self-organized neural network

and wrinkle grade of 26 different type fabrics can be evaluated according to this result. For describing the assessment result with quantify

the correlation coefficient is calculated between objective assessment and subjective assessment in order to validate the feasibility of this method.