Texturecanbe definedasa function ofspatialpixelintensity. Research on texture is a very important task in computer vision and its applications. Texture recognition in human vision is easily can be defined but in machine vision and image processing, has its special complexities. All the system in this field is based on square architecture so the algorithms are faced with some problems such as intricacy, huge amount of calculation and inadequate information extracted from image's feature vectors due to square grid’s limitation. About 40 years ago was brought a novel hexagonal architecture method in image processing. Due to the advantages of this structure compared to square architecture, soon attracted the attention of many researchers. Some important advantages are equaled distances betweehy;one pixel and its adjacent pixels (isotropy) and good quality in edge and corner detection. In this thesis, a state-of-the-art method for texture extraction in hexagonal structure is presented. A completed local binary pattern (CLBP) in hexagonal structure is applied for information extraction. In this method, the texture information of each pixel express with three parameters, first, the magnitude local difference between central pixel and its neighbors second, the sign local difference between central pixel and its neighbors and third, central gray level in each pattern. A feature vector for each image is achieved by applying mentioned descriptor and a dictionary is created by normal" Keywords : Texture extraction, Recognition, Machine vision, Hexagonal structure, Isotropy, A completed local binary pattern in hexagonal structure(CLBPH), ltr"
