Refereed full papers (journals, book chapters, international conferences)

1994

@inproceedings{BeL1994,
	vgclass =	{refpap},
	author =	{Jeffrey S. Beis and David G. Lowe},
	title =	{Learning indexing functions for {3-D} model-based object
	recognition},
	booktitle =	{Proceedings of the 1994 IEEE Conference on Computer Vision and Pattern Recognition (CVPR'94)},
	address =	{Seattle, U.S.A.},
	month =	{June},
	year =	{1994},
	url =	{http://www.cs.ubc.ca/spider/lowe/papers/cvpr94-abs.html},
	url1 =	{http://www.cs.ubc.ca/spider/lowe/papers/cvpr94.ps},
	abstract =	{Indexing is an efficient method of recovering match
	hypotheses in model-based object recognition. Unlike other methods,
	which search for viewpoint-invariant shape descriptors to use as
	indices, we use a learning method to model the smooth variation in
	appearance of local feature sets (LFS). Indexing from LFS effectively
	deals with the problems of occlusion and missing features. The indexing
	functions generated by the learning method are probability
	distributions describing the possible interpretations of each index
	value. During recognition, this information can be used to select the
	least ambiguous features for matching. A verification stage follows so
	that the final reliability and accuracy of the match is greater than
	that from indexing alone. This approach has the potential to work with
	a wide range of image features and model types.},
}