Conference paper
Efficient RGB-D object categorization using cascaded ensembles of randomized decision trees
2015 IEEE International Conference on Robotics and Automation (ICRA), pp.1295-1302
2015 IEEE International Conference on Robotics and Automation (ICRA) (Seattle, Washington, 26/05/2015–30/05/2015)
2015
Abstract
This paper presents an efficient framework for the categorization of objects in real-world scenes (captured with an RGB-D sensor). The proposed framework uses ensembles of randomized decision trees in a hierarchical cascaded architecture to compute consistent object-class inferences of unseen objects. Specifically, the proposed framework computes object-class probabilities at three levels of an image hierarchy (i.e., pixel-, surfel-, and object-levels) using Random Forest classifiers. Next, these probabilities are fused together to compute a cumulative probabilistic output which is used to infer object categories. This fusion results in an improved object categorization performance compared with the state-of-the-art methods.
Details
- Title
- Efficient RGB-D object categorization using cascaded ensembles of randomized decision trees
- Authors/Creators
- U. Asif (Author/Creator) - The University of Western AustraliaM. Bennamoun (Author/Creator) - The University of Western AustraliaF. Sohel (Author/Creator) - The University of Western Australia
- Publication Details
- 2015 IEEE International Conference on Robotics and Automation (ICRA), pp.1295-1302
- Conference
- 2015 IEEE International Conference on Robotics and Automation (ICRA) (Seattle, Washington, 26/05/2015–30/05/2015)
- Identifiers
- 991005546331907891
- Murdoch Affiliation
- Murdoch University
- Language
- English
- Resource Type
- Conference paper
Metrics
53 Record Views