earticle

영어

Texture analysis such as segmentation and classification plays a vital role in computer vision and pattern recognition and is widely applied to many areas such as industrial automation, bio-medical image processing and remote sensing. Over the last decade, several studies on texture analysis propose to model texture as a probabilistic process that generates small texture patches. In these studies, texture is represented by means of a frequency histogram that measures how often texture patches from a codebook occur in the texture. In the codebook, the texture patches are represented by a collection of filter bank responses. The resulting representations are called textons. A recent study claims that textons based on gray values outperform textons based on filter responses. Textons refer to fundamental micro structures in natural images and are considered as the atoms of pre-attentive human visual perception. This paper describes a novel technique of image segmentation for texture images based on six different texton patterns and morphological transforms.