By Topic

Unsupervised hierarchical multi-scale image segmentation level set, wavelet and additive splitting operator

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Jeon, M. ; Inst. for Biodiagnostics, Nat. Res. Council of Canada, Winnipeg, Man., Canada ; Alexander, M. ; Pizzi, N. ; Pedrycz, W.

This paper presents an unsupervised hierarchical multi-scale segmentation method for multi-phase images based on a single level set, a multi-scale analysis using wavelets, and the semi-implicit additive operator splitting (AOS) scheme which is stable, fast, and easy to implement The method successively segments image subregions found at each step of the hierarchy using a decision criterion based on the variance of intensity across the current subregion. Each step starts with segmenting a down-sized image, and the solution is mapped back to the original size and used as an initial contour for further processing. While there is some overhead related to processing a down-sized image, there is a substantial speedup in processing the full-sized image and selecting the subimage to be segmented.

Published in:

Fuzzy Information, 2004. Processing NAFIPS '04. IEEE Annual Meeting of the  (Volume:2 )

Date of Conference:

27-30 June 2004