Increasing the density of active appearance models
Active Appearance Models (AAMs) typically only use 50-100 mesh vertices because they are usually constructed from a set of training images with the vertices hand-labeled on them. In this paper, we propose an algorithm to increase the density of an AAM. Our algorithm operates by iteratively building the AAM, refitting the AAM to the training data, and refining the AAM.We compare our algorithm with the state of the art in optical flow algorithms and find it to be significantly more accurate. We also show that dense AAMs can be fit more robustly than sparse ones. Finally, we show how our algorithm can be used to construct AAMs automatically, starting with a single affine model that is subsequently refined to model non-planarity and non-rigidity.
Text ReferenceKannan Ramnath, Simon Baker, Ian Matthews, and Deva Ramanan. Increasing the density of active appearance models. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2008.
author = "Ramnath, Kannan and Baker, Simon and Matthews, Ian and Ramanan, Deva",
booktitle = "IEEE Conference on Computer Vision and Pattern Recognition (CVPR)",
title = "Increasing the density of active appearance models",
year = "2008",
tag = "object_recognition, people"