3DPO: A three-dimensional part orientation system
International Journal of Robotics Research
CAD-Based Computer Vision: From CAD Models to Relational Graphs
IEEE Transactions on Pattern Analysis and Machine Intelligence
BONSAI: 3D Object Recognition Using Constrained Search
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part I
Model-based object recognition in dense-range images—a review
ACM Computing Surveys (CSUR)
Shape Spectrum Based View Grouping and Matching of 3D Free-Form Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scene Understanding by Rule Evaluation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Point Signatures: A New Representation for 3D Object Recognition
International Journal of Computer Vision
Learning Recognition and Segmentation Using the Cresceptron
International Journal of Computer Vision
The Automatic Construction of a View-Independent Relational Model for 3-D Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computing the Generalized Aspect Graph for Objects with Moving Parts
IEEE Transactions on Pattern Analysis and Machine Intelligence
3-D Object Recognition with Symmetric Models: Symmetry Extraction and Encoding
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geometric Probing of Dense Range Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
DGCI '02 Proceedings of the 10th International Conference on Discrete Geometry for Computer Imagery
Strong thinning and polyhedric approximation of the surface of a voxel object
Discrete Applied Mathematics
The Sample Tree: A Sequential Hypothesis Testing Approach to 3D Object Recognition
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
International Journal of Remote Sensing
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The authors explore the connection between CAGD (computer-aided geometric design) and computer vision. A method for the automatic generation of recognition strategies based on the 3-D geometric properties of shape has been devised and implemented. It uses a novel technique to quantify the following properties of features which compose models used in computer vision: robustness, completeness, consistency, cost, and uniqueness. By utilizing this information, the automatic synthesis of a specialized recognition scheme, called a strategy tree, is accomplished. Strategy trees describe, in a systematic and robust manner, the search process used for recognition and localization of particular objects in the given scene. The consist of selected 3-D features which satisfy system constraints and corroborating evidence subtrees which are used in the formation of hypotheses. Verification techniques, used to substantiate or refute these hypotheses are explored. Experiments utilizing 3-D data are presented.