By Topic

Reliability Estimation for Statistical Shape Models

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

2 Author(s)
Sukno, F.M. ; Dept. of Inf. & Commun. Technol., Univ. Barcelona, Barcelona ; Frangi, A.F.

One of the drawbacks of statistical shape models is their occasional failure to converge. Although visually this fact is usually easy to recognize, there is no automatic way to detect it. In this paper, we introduce a generic reliability measure for statistical shape models. It is based on a probabilistic framework and uses information extracted by the model itself during the matching process. The proposed method was validated with two variants of active shape models in the context facial image analysis. Experimental results on more than 3700 facial images showed a high degree of correlation between the segmentation accuracy and the estimated reliability metric.

Published in:

Image Processing, IEEE Transactions on  (Volume:17 ,  Issue: 12 )