Localizing Overlapping Parts by Searching the Interpretation Tree
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using Polygons to Recognize and Locate Partially Occluded Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Classification of Partial 2-D Shapes Using Fourier Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Robotics Research
On a cyclic string-to-string correction problem
Information Processing Letters
A Theory of Multiscale, Curvature-Based Shape Representation for Planar Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
Active shape models—their training and application
Computer Vision and Image Understanding
Robot grasp synthesis algorithms: a survey
International Journal of Robotics Research
Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Representation and Detection of Deformable Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Object Shape: From Drawings to Images
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Robust contour matching via the order-preserving assignment problem
IEEE Transactions on Image Processing
Manipulator and object tracking for in-hand 3D object modeling
International Journal of Robotics Research
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Robot manipulators typically rely on complete knowledge of object geometry in order to plan motions and compute grasps. However, when an object is not fully in view it can be difficult to form an accurate estimate of the object's shape and pose, particularly when the object deforms. In this paper we describe a generative model of object geometry based on Mardia and Dryden's 芒聙聹Probabilistic Procrustean Shape芒聙聺, which captures both non-rigid deformations and object variability in a class. We extend their shape model to the setting where point correspondences are unknown using Scott and Nowak's COPAP framework. We use this model to recognize objects in a cluttered image and to infer their complete two-dimensional boundaries with a novel algorithm called OSIRIS. We show examples of learned models from image data and demonstrate how the models can be used by a manipulation planner to grasp objects in cluttered visual scenes.