New Generation Computing - Selected papers from the international workshop on algorithmic learning theory,1990
A Rule-Based Approach to Represent Spatio-Temporal Relations in Video Data
ADVIS '00 Proceedings of the First International Conference on Advances in Information Systems
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
In Defense of One-Vs-All Classification
The Journal of Machine Learning Research
Geometry For Computer Graphics: Formulae, Examples And Proofs
Geometry For Computer Graphics: Formulae, Examples And Proofs
Results on Range Image Segmentation for Service Robots
ICVS '06 Proceedings of the Fourth IEEE International Conference on Computer Vision Systems
Efficient search and verification for function based classification from real range images
Computer Vision and Image Understanding
Cognitive maps for mobile robots-an object based approach
Robotics and Autonomous Systems
Learning function-based object classification from 3D imagery
Computer Vision and Image Understanding
Online generation of scene descriptions in urban environments
Robotics and Autonomous Systems
Robust edge extraction for Swissranger SR-3000 range images
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Learning multi-class theories in ILP
ILP'10 Proceedings of the 20th international conference on Inductive logic programming
Learning logic rules for scene interpretation based on markov logic networks
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
Meta-interpretive learning: application to grammatical inference
Machine Learning
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We use Inductive Logic Programming (ILP) to learn classifiers for generic object recognition from point clouds, as generated by 3D cameras, such as the Kinect. Each point cloud is segmented into planar surfaces. Each subset of planes that represents an object is labelled and predicates describing those planes and their relationships are used for learning. Our claim is that a relational description for classes of 3D objects can be built for robust object categorisation in real robotic application. To test the hypothesis, labelled sets of planes from 3D point clouds gathered during the RoboCup Rescue Robot competition are used as positive and negative examples for an ILP system. The robustness of the results is evaluated by 10-fold cross validation. In addition, common household objects that have curved surfaces are used for evaluation and comparison against a well-known non-relational classifier. The results show that ILP can be successfully applied to recognise objects encountered by a robot especially in an urban search and rescue environment.