Making large-scale support vector machine learning practical
Advances in kernel methods
MLESAC: a new robust estimator with application to estimating image geometry
Computer Vision and Image Understanding - Special issue on robusst statistical techniques in image understanding
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Bootstrap learning for place recognition
Eighteenth national conference on Artificial intelligence
Model-Based Object Recognition - A Survey of Recent Research
Model-Based Object Recognition - A Survey of Recent Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
Discriminative Learning of Markov Random Fields for Segmentation of 3D Scan Data
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Putting Objects in Perspective
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Relational object maps for mobile robots
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Navigating, Recognizing and Describing Urban Spaces With Vision and Lasers
International Journal of Robotics Research
Plane-based object categorisation using relational learning
Machine Learning
Hi-index | 0.00 |
The ability to extract a rich set of semantic workspace labels from sensor data gathered in complex environments is a fundamental prerequisite to any form of semantic reasoning in mobile robotics. In this paper, we present an online system for the augmentation of maps of outdoor urban environments with such higher-order, semantic labels. The system employs a shallow supervised classification hierarchy to classify scene attributes, consisting of a mixture of 2D/3D geometric and visual scene information, into a range of different workspace classes. The union of classifier responses yields a rich, composite description of the local workspace. We present extensive experimental results, using two large urban data sets collected by our research platform.