Adaptive fractional differential method based on CSGV to extract image texture feature

Aiguo Song; Wei Wang

doi:10.1117/12.2035859

10 October 2013 Adaptive fractional differential method based on CSGV to extract image texture feature

Aiguo Song, Wei Wang

Proceedings Volume 8916, Sixth International Symposium on Precision Mechanical Measurements; 891604 (2013) https://doi.org/10.1117/12.2035859
Event: Sixth International Symposium on Precision Mechanical Measurements, 2013, Guiyang, China

Abstract

As the sensitivity of the fractional differential algorithm for detail image texture extraction and the difficulty of the best fractional differential order fingding, a novel adaptive fractional differential method is proposed, which can adaptively select the fractional differential order according to the mask window size, definition of fractional differential equations, the composite sub-band gradient vector (CSGV) obtained from the sub-images through a wavelet decomposition of a texture image, and human visual property. The fractional differential operator mask based on G-L formula is designed and realized by employing the adaptive order. The evaluation parameters of image texture feature extraction such as the image information entropy and multi-scale structural similarity (MS-SSIM) are used for quantitative analysis of the extraction method in experiment The experiment results show that for grey texture image this method is able to extract image texture and edge details completely, which approximate the results of optimal fractional differential order and more satisfies human visual sense. It is an effective approach to extract fine texture features of images.

Citation Download Citation

Aiguo Song and Wei Wang "Adaptive fractional differential method based on CSGV to extract image texture feature", Proc. SPIE 8916, Sixth International Symposium on Precision Mechanical Measurements, 891604 (10 October 2013); https://doi.org/10.1117/12.2035859

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