Noise-tolerant texture classification and image segmentation

Bradley Pryor Kjell; Pearl Y. Wang

doi:10.1117/12.25186

1 February 1991 Noise-tolerant texture classification and image segmentation

Bradley Pryor Kjell, Pearl Y. Wang

Proceedings Volume 1381, Intelligent Robots and Computer Vision IX: Algorithms and Techniques; (1991) https://doi.org/10.1117/12.25186
Event: Advances in Intelligent Robotics Systems, 1990, Boston, MA, United States

Abstract

Most previous work on image texture has not studied the effect of noise on texture classification or on textured image segmentation. Classification studies typically use samples of unknown textures that were digitized under the same noise-free conditions as the samples in the training set . In this paper noise-tolerant texture classification is demonstrated. Noise-tolerant texture classification allows the samples of unknown textures to be digitized under different noise and illumination conditions than the samples in the training set. The features extracted from the texture samples are relatively tolerant of noise and illumination gradients resulting in reliable classification. The method is based on edges found in texture samples using a noise-tolerant edge detector similar to the Canny operator [4]. Texture features are average distances separating edges in various orientations [10 11]. Since the edges are reliably extracted from noisy samples texture features based on these edges are noise-tolerant. This method is investigated experimentally and compared with cooccurrence matrix features [6] and cooccurrence matrix features of directionally-smoothed samples. The edge-based features may be used for texture-based image segmentation. This is demonstrated by partitioning an image into areas of uniform texture even when there are illumination and noise gradients over the image.

Citation Download Citation

Bradley Pryor Kjell and Pearl Y. Wang "Noise-tolerant texture classification and image segmentation", Proc. SPIE 1381, Intelligent Robots and Computer Vision IX: Algorithms and Techniques, (1 February 1991); https://doi.org/10.1117/12.25186

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available