Title of article :
Image texture analysis: methods and comparisons
Bharati، نويسنده , , Manish H. and Liu، نويسنده , , Jay M. MacGregor، نويسنده , , John F.، نويسنده ,
Issue Information :
دوفصلنامه با شماره پیاپی سال 2004
Surface texture is an important quality characteristic of many products. This paper provides an overview of several different approaches to image texture analysis and demonstrates their use on the problem of classifying a set of rolled steel sheets into various quality grades. Methods covered include traditional statistical approaches such as gray level co-occurrence matrix (GLCM) methods, multivariate statistical approaches based on PCA and PLS, and wavelet texture analysis.
ional multivariate classification approaches, such as PLS-DA, applied directly to the images are shown to fail because of the loss of spatial identity of the variables (pixels) in those approaches, and the lack of congruency of the images. However, approaches that re-introduce spatial information, such as performing two-dimensional FFT on the images prior to applying multivariate methods can perform well. A new approach that re-introduces spatial information through image shifting and stacking, followed by multivariate image analysis (MIA) is presented and shown to work well. It can also be used to develop optimal spatial filters for extracting texture information. Wavelet texture analysis (WTA) methods are discussed and insight into their space/frequency decomposition behavior is used to show why they are generally considered to be state of the art in texture analysis.
Wavelet texture analysis , Principal component analysis , partial least squares , Texture analysis , Gray Level Co-Occurrence Matrix , Multivariate image analysis
Journal title :
Chemometrics and Intelligent Laboratory Systems