Optimal combinations of pattern classifiers
Pattern Recognition Letters
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Three-Dimensional Model-Based Object Recognition and Segmentation in Cluttered Scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Foundations and Trends® in Computer Graphics and Vision
Probabilistic categorization of kitchen objects in table settings with a composite sensor
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Object Recognition in 3D Point Clouds Using Web Data and Domain Adaptation
International Journal of Robotics Research
Parts-based 3D object classification
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Combined 2D-3D categorization and classification for multimodal perception systems
International Journal of Robotics Research
Ant colony optimization inspired algorithm for 3D object segmentation
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
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Detecting objects in clutter is an important capability for a household robot executing pick and place tasks in realistic settings. While approaches from 2D vision work reasonably well under certain lighting conditions and given unique textures, the development of inexpensive RGBD cameras opens the way for real-time geometric approaches that do not require templates of known objects. This paper presents a part-graph-based hashing method for classifying objects in clutter, using an additive feature descriptor. The method is incremental, allowing easy addition of new training data without recreating the complete model, and takes advantage of the additive nature of the feature to increase efficiency. It is based on a graph representation of the scene created from considering possible groupings of over-segmented scene parts, which can in turn be used in classification. Additionally, the results over multiple segmentations can be accumulated to increase detection accuracy. We evaluated our approach on a large RGBD dataset containing over 15000 Kinect scans of 102 objects grouped in 16 categories, which we arranged into six geometric classes. Furthermore, tests on complete cluttered scenes were performed as well, and used to showcase the importance of domain adaptation.