Learning and evaluating visual features for pose estimation (1999) [28 citations — 8 self]
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by Robert Sim, Gregory Dudek
In Proceedings of the Seventh International Conference on Computer Vision (ICCV’99), Kerkyra
http://www.cim.mcgill.ca/~simra/publications/iccv99.ps.gz
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@INPROCEEDINGS{Sim99learningand,author = {Robert Sim and Gregory Dudek},
title = {Learning and evaluating visual features for pose estimation},
booktitle = {In Proceedings of the Seventh International Conference on Computer Vision (ICCV’99), Kerkyra},
year = {1999},
pages = {1217--1222},
publisher = {IEEE Press}
}
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Abstract:
We present a method for learning a set of visual landmarks which are useful for pose estimation. The landmark learning mechanism is designed to be applicable to a wide range of environments, and generalized for di#erent approaches to computing a pose estimate. Initially, each landmark is detected as a local extremum of a measure of distinctiveness and represented by a principal components encoding which is exploited for matching. Attributes of the observed landmarks can be parameterized using a generic parameterization method and then evaluated in terms of their utility for pose estimation. We present experimental evidence that demonstrates the utility of the method.
