Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
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
Machine Learning
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
MEBN: A language for first-order Bayesian knowledge bases
Artificial Intelligence
Motion Segmentation and Depth Ordering Using an Occlusion Detector
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining Time-Of-Flight depth and stereo images without accurate extrinsic calibration
International Journal of Intelligent Systems Technologies and Applications
On fast surface reconstruction methods for large and noisy point clouds
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Leaving flatland: toward real-time 3D navigation
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Object categorization in clutter using additive features and hashing of part-graph descriptors
SC'12 Proceedings of the 2012 international conference on Spatial Cognition VIII
Efficient object categorization with the surface-approximation polynomials descriptor
SC'12 Proceedings of the 2012 international conference on Spatial Cognition VIII
Towards a comprehensive chore list for domestic robots
Proceedings of the 8th ACM/IEEE international conference on Human-robot interaction
Robotics and Autonomous Systems
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In this paper, we investigate the problem of 3D object categorization of objects typically present in kitchen environments, from data acquired using a composite sensor. Our framework combines different sensing modalities and defines descriptive features in various spaces for the purpose of learning good object models. By fusing the 3D information acquired from a composite sensor that includes a color stereo camera, a time-of-flight (TOF) camera, and a thermal camera, we augment 3D depth data with color and temperature information which helps disambiguate the object categorization process. We make use of statistical relational learning methods (Markov Logic Networks and Bayesian Logic Networks) to capture complex interactions between the different feature spaces. To show the effectiveness of our approach, we analyze and validate the proposed system for the problem of recognizing objects in table settings scenarios.