Dentry, R., Pugeault, N. and Piater, J. H. (2009) A probabilistic framework for 3D visual object representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(10), pp. 1790-1803. (doi: 10.1109/TPAMI.2009.64)
Full text not currently available from Enlighten.
Abstract
We present an object representation framework that encodes probabilistic spatial relations between 3D features and organizes these features in a hierarchy. Features at the bottom of the hierarchy are bound to local 3D descriptors. Higher level features recursively encode probabilistic spatial configurations of more elementary features. The hierarchy is implemented in a Markov network. Detection is carried out by a belief propagation algorithm, which infers the pose of high-level features from local evidence and reinforces local evidence from globally consistent knowledge, effectively producing a likelihood for the pose of the object in the detection scene. We also present a simple learning algorithm that autonomously builds hierarchies from local object descriptors. We explain how to use our framework to estimate the pose of a known object in an unknown scene. Experiments demonstrate the robustness of hierarchies to input noise, viewpoint changes, and occlusions.
Item Type: | Articles |
---|---|
Status: | Published |
Refereed: | Yes |
Glasgow Author(s) Enlighten ID: | Pugeault, Dr Nicolas |
Authors: | Dentry, R., Pugeault, N., and Piater, J. H. |
College/School: | College of Science and Engineering > School of Computing Science |
Journal Name: | IEEE Transactions on Pattern Analysis and Machine Intelligence |
Publisher: | IEEE |
ISSN: | 0162-8828 |
ISSN (Online): | 1939-3539 |
Published Online: | 17 March 2009 |
University Staff: Request a correction | Enlighten Editors: Update this record