The design and analysis of spatial data structures
The design and analysis of spatial data structures
The non-existence of general-case view-invariants
Geometric invariance in computer vision
Geometric invariants and object recognition
International Journal of Computer Vision
Local Invariants For Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Incremental distance join algorithms for spatial databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Model-Based Recognition of 3D Objects from One View
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
SSD '95 Proceedings of the 4th International Symposium on Advances in Spatial Databases
On affine registration of planar point sets using complex numbers
Computer Vision and Image Understanding
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A new method has been developed for overcoming some of the major obstacles of object recognition. It represents both 2D images and 3D models from a database by viewpoint invariant descriptors in the same invariant space. As there is a large number of possible matches between the image and model invariants, there is a crucial need to perform this match efficiently. We answer this need by using an adaptation of spatial sorting based on the octree representation of the model points and image lines in three-dimensional space. We show the advantage of the octree method over the traditional" brute-force" approach and we compare the merits of the two methods. This makes it possible for the recognition task to be performed in a reasonable time for a large number of models.