Least-Squares Fitting of Two 3-D Point Sets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Grouping symmetrical structures for object segmentation and description
Computer Vision and Image Understanding
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Non-parametric Model for Background Subtraction
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Evaluation of Features Detectors and Descriptors Based on 3D Objects
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
One-Shot Learning of Object Categories
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiclass Object Recognition with Sparse, Localized Features
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Semi-autonomous Learning of Objects
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Bilateral Symmetry Detection for Real-time Robotics Applications
International Journal of Robotics Research
Interactive segmentation for manipulation in unstructured environments
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Interactive learning of visually symmetric objects
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Integrating visual perception and manipulation for autonomous learning of object representations
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
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Robots usually carry out object segmentation and modeling passively. Sensors such as cameras are actuated by a robot without disturbing objects in the scene. In this paper, we present an intelligent robotic system that physically moves objects in an active manner to perform segmentation and modeling using vision. By visually detecting bilateral symmetry, our robot is able to segment and model objects through controlled physical interactions. Extensive experiments show that our robot is able to accurately segment new objects autonomously. We also show that our robot is able to leverage segmentation results to autonomously learn visual models of new objects by physically grasping and rotating them. Object recognition experiments confirm that the robot-learned models allow robust recognition. Videos of the robotic experiments are also made available.