By Topic

Box-like Superquadric Recovery in Range Images by Fusing Region and Boundary Information

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)
Katsoulas, D. ; Intitute for Informatics & Telecommun., NCSR Demokritos, Athens ; Kosmopoulos, D.I.

This work contributes to the robotic bin-picking problem, and more specifically to the problem of localizing piled box-like objects. We employ range imagery, and use box-like superquadrics for modeling the target objects. Our approach for superquadric segmentation is an extension of the widespread recover-and-select framework, which employs only region information and therefore suffers from the region over-growing problem. Our approach equally considers both region and boundary-based information for performing the recovery task. Extensive experimentation with a variety of target object configurations demonstrates that it outperforms the recover-and-select framework in terms of both robustness and computational efficiency. Moreover, if implemented in a parallel hardware environment, our approach can operate in real time

Published in:

Pattern Recognition, 2006. ICPR 2006. 18th International Conference on  (Volume:1 )

Date of Conference:

0-0 0