Learning Good Features for Active Shape Models
Workshop Paper, ICCV '09 2nd IEEE International Workshop on Subspace Methods, September, 2009
Abstract
Active Shape Models (ASMs) are commonly used to model the appearance and shape variation of objects in images. This paper proposes two strategies to improve speed and accuracy in ASMs fitting. First, we define a new criterion to select landmarks that have good generalization properties. Second, for each landmark we learn a subspace with improved facial feature response effectively avoiding local minima in the ASM fitting. Experimental results show the effectiveness and robustness of the approach.
BibTeX
@workshop{Brunet-2009-120979,author = {N. Brunet and F. De la Torre and F. Perez},
title = {Learning Good Features for Active Shape Models},
booktitle = {Proceedings of ICCV '09 2nd IEEE International Workshop on Subspace Methods},
year = {2009},
month = {September},
}
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