Three-dimensional object recognition from single two-dimensional images
Artificial Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing solid objects by alignment with an image
International Journal of Computer Vision
Invariant Descriptors for 3D Object Recognition and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part I
Space and Time Bounds on Indexing 3D Models from 2D Images
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part I
View Variation of Point-Set and Line-Segment Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Study of Affine Matching With Bounded Sensor Error
ECCV '92 Proceedings of the Second European Conference on Computer Vision
Probabilistic Indexing: Recognizing 3D Objects from 2D Images Using
Probabilistic Indexing: Recognizing 3D Objects from 2D Images Using
Probabilistic 3D Object Recognition
International Journal of Computer Vision
Structural Constraints for Pose Clustering
CAIP '99 Proceedings of the 8th International Conference on Computer Analysis of Images and Patterns
Computationally efficient and reliable fingerprint mosaicking on embedded hardware using minutiae
Machine Graphics & Vision International Journal
Object recognition using point uncertainty regions as pose uncertainty regions
Image and Vision Computing
| Hi-index | 0.14 |
Recent papers have indicated that indexing is a promising approach to fast model-based object recognition because it allows most of the possible matches between sets of image features and sets of model features to be quickly eliminated from consideration. This correspondence describes a system that is capable of indexing using sets of three points undergoing three-dimensional transformations and projection by taking advantage of the probabilistic peaking effect. To be able to index using sets of three points, we must allow false negatives. These are overcome by ensuring that we examine several correct hypotheses. The use of these techniques to speed up the alignment method is described. Results are given on real and synthetic data.