Earticle

초록

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.

목차

Abstract
 1. Introduction
 2. Mathematical Morphology
 3. Methodology
 4. Results and Discussions
 5. Conclusions
 Acknowledgements
 References

키워드

저자

• M. Joseph Prakash [ Assoc. Professor, IT Dept., GIET JNTUK ]
• Saka Kezia [ Assoc. Professor, ECE Dept., CIET, JNTUK ]
• I. Santhi Prabha [ Professors, JNTUK ]
• V. Vijaya Kumar [ Professors, JNTUK ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605