By Topic

Cooperative robust estimation using layers of support

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)
Darrell, T. ; Media Lab., MIT, Cambridge, MA, USA ; Pentland, A.P.

We present an approach to the problem of representing images that contain multiple objects or surfaces. Rather than using an edge-based approach to represent the segmentation of a scene, we propose a multilayer estimation framework which uses support maps to represent the segmentation of the image into homogeneous chunks. This support-based approach can represent objects that are split into disjoint regions, or have surfaces that are transparently interleaved. Our framework is based on an extension of robust estimation methods that provide a theoretical basis for support-based estimation. We use a selection criteria derived from the minimum description length principle to decide how many support maps to use in describing an image. Our method has been applied to a number of different domains, including the decomposition of range images into constituent objects, the segmentation of image sequences into homogeneous higher-order motion fields, and the separation of tracked motion features into distinct rigid-body motions

Published in:

Pattern Analysis and Machine Intelligence, IEEE Transactions on  (Volume:17 ,  Issue: 5 )