Three-dimensional object recognition from single two-dimensional images
Artificial Intelligence
On Recognizing and Positioning Curved 3-D Objects from Image Contours
IEEE Transactions on Pattern Analysis and Machine Intelligence
Developing the aspect graph representation for use in image understanding
Proceedings of a workshop on Image understanding workshop
Fitting Parameterized Three-Dimensional Models to Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Surface and volumetric segmentation of complex 3-D objects using parametric shape models
Surface and volumetric segmentation of complex 3-D objects using parametric shape models
Describing Complicated Objects by Implicit Polynomials
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Approach to Object Recognition: Aligning Pictorial Descriptions
An Approach to Object Recognition: Aligning Pictorial Descriptions
COSMOS-A Representation Scheme for 3D Free-Form Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Reliable and Efficient Pattern Matching Using an Affine Invariant Metric
International Journal of Computer Vision
3D object recognition: Representation and matching
Statistics and Computing
3D Object recognition in cluttered environments by segment-based stereo vision
International Journal of Computer Vision
Hierarchical Organization of Appearance-Based Parts and Relations for Object Recognition
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Relative scale method to locate an object in cluttered environment
Image and Vision Computing
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A method is presented for computing the pose of rigid 3D objects with arbitrary curved surfaces. Given an input image and a candidate object model and aspect, the method will verify whether or not the object is present and if so, report pose parameters. The curvature method of Basri and Ullman is used to model points on the object rim, while stereo matching is used for internal edge points. The model allows an object edgemap to be predicted from pose parameters. Pose is computed via an iterative search for the best pose parameters. Heuristics are used so that matching can succeed in the presence of occlusion and artifact and without resorting to use of corresponding salient feature points. Bench tests and simulations show that the method almost always converges to ground truth pose parameters for a variety of objects and for a broad set of starting parameters in the same aspect.