Structural edge detection using wavelet domain statistical model

Shubin Zhao

doi:10.1117/12.832989

30 October 2009 Structural edge detection using wavelet domain statistical model

Shubin Zhao

Proceedings Volume 7495, MIPPR 2009: Automatic Target Recognition and Image Analysis; 74954H (2009) https://doi.org/10.1117/12.832989
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China

Abstract

Edges play an important role in most computer vision and image analysis systems, and extracting structural edges is the main goal of edge detection for many applications. But due to the presence of noise and texture, structural edge detection is not a trivial work. In this paper, an approach is presented for structural edge detection, which is formulated as a statistical pattern recognition problem in wavelet transform domain. In the approach, both inter-scale and intra-scale dependences among wavelet coefficients are utilized, where the former dependences are encoded by inter-scale coefficient ratios and the latter by anisotropic MRF model. To reduce the computational complexity, independent mixture of Gaussian is used to model wavelet coefficients, which corresponds to Rayleigh distribution for gradient magnitude, and posterior probabilities are computed to measure the edge strengths. Inter- and intra-scale dependences among coefficients are utilized to suppress noise and texture, and these measures can significantly improve edge continuity in scale space. To show the effectiveness of the presented algorithm, experiments are conducted on various kinds of real-world images, and several results are given for assessment.

Citation Download Citation

Shubin Zhao "Structural edge detection using wavelet domain statistical model", Proc. SPIE 7495, MIPPR 2009: Automatic Target Recognition and Image Analysis, 74954H (30 October 2009); https://doi.org/10.1117/12.832989

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