摘要：Texture plays an important role in numerous computer vision applications. Many methods for describing and analyzing of textured surfaces have been proposed. Variations in the appearance of texture caused by changing illumination and imaging conditions, for example, set high requirements on different analysis methods. In this thesis, methods for extracting texture features and recognizing texture categories using grey level first-order and second-order statistics, edge detectors and local binary pattern features are proposed. Unsupervised clustering methods are used for building a labeled training set for a classifier and for studying the performances of these features.Texture plays an important role in numerous computer vision applications. Many methods for describing and analyzing of textured surfaces have been proposed. Variations in the appearance of texture caused by changing illumination and imaging conditions, for example, set high requirements on different analysis methods. In this thesis, methods for extracting texture features and recognizing texture categories using grey level first-order and second-order statistics, edge detectors and local binary pattern features are proposed. Unsupervised clustering methods are used for building a labeled training set for a classifier and for studying the performances of these features.
关键词：texture analysis; classification; grey level statistics; local binary pattern
