Recognition and Tracking of 3D Objects
Proceedings of the 30th DAGM symposium on Pattern Recognition
IEICE - Transactions on Information and Systems
CAD-based recognition of 3D objects in monocular images
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Structural descriptors for category level object detection
IEEE Transactions on Multimedia
Probabilistic 3D object recognition based on multiple interpretations generation
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part IV
Journal of Visual Communication and Image Representation
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We present an object recognition algorithm that uses model and image line features to locate complex objects in high clutter environments. Finding correspondences between model and image features is the main challenge in most object recognition systems. In our approach, corresponding line features are determined by a three-stage process. The first stage generates a large number of approximate pose hypotheses from correspondences of one or two lines in the model and image. Next, the pose hypotheses from the previous stage are quickly ranked by comparing local image neighborhoods to the corresponding local model neighborhoods. Fast nearest neighbor and range search algorithms are used to implement a distance measure that is unaffected by clutter and partial occlusion. The ranking of pose hypotheses is invariant to changes in image scale, orientation, and partially invariant to affine distortion. Finally, a robust pose estimation algorithmis applied for refinement and verification, starting from the few best approximate poses produced by the previous stages. Experiments on real images demonstrate robust recognition of partially occluded objects in very high clutter environments.