Learning Objects and Grasp Affordances through Autonomous Exploration
ICVS '09 Proceedings of the 7th International Conference on Computer Vision Systems: Computer Vision Systems
Spatial-Temporal Junction Extraction and Semantic Interpretation
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
A strategy for grasping unknown objects based on co-planarity and colour information
Robotics and Autonomous Systems
Discriminative mixture-of-templates for viewpoint classification
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
A two-level real-time vision machine combining coarse- and fine-grained parallelism
Journal of Real-Time Image Processing
Temporal accumulation of oriented visual features
Journal of Visual Communication and Image Representation
Learning visual representations for perception-action systems
International Journal of Robotics Research
Continuous surface-point distributions for 3D object pose estimation and recognition
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part III
Probabilistic object models for pose estimation in 2D images
DAGM'11 Proceedings of the 33rd international conference on Pattern recognition
Enabling grasping of unknown objects through a synergistic use of edge and surface information
International Journal of Robotics Research
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We present an object representation framework that encodes probabilistic spatial relations between 3D features and organizes these features in a hierarchy. Features at the bottom of the hierarchy are bound to local 3D descriptors. Higher level features recursively encode probabilistic spatial configurations of more elementary features. The hierarchy is implemented in a Markov network. Detection is carried out by a belief propagation algorithm, which infers the pose of high-level features from local evidence and reinforces local evidence from globally consistent knowledge, effectively producing a likelihood for the pose of the object in the detection scene. We also present a simple learning algorithm that autonomously builds hierarchies from local object descriptors. We explain how to use our framework to estimate the pose of a known object in an unknown scene. Experiments demonstrate the robustness of hierarchies to input noise, viewpoint changes, and occlusions.