2D Texture Features

Abstract

Because texture of object is very valuable information in computer vision, it is important to describe it somehow. And for this serve texture features. Optimal selection of features is very important for recognizing texture. In this bachelor thesis were used local binary patterns (LBP) as a method of gaining texture feature. In this method is not its value the texture feature, but histogram of percent occurrence values in the entire texture. To compare histograms there is used Euclidean distance, Bhattacharyya distance or Mahalanobis distance. Main purpose of this thesis is mutually comparing of texture clasification by several variants of LBP and evaluation of their outcomes by Euclidean distance, Bhattacharyya distance or Mahalanobis distance