Bayesian Network Based Reparameterization of Haar-like Feature
Abstract
Object detection using Haar-like features is formulated as a maximum likelihood estimation. Object features are described by an arbitrary Bayesian Network (BN) of Haar-like features. We proposed variable translation techniques transform the BN into the likelihood for the object detection. The likelihood is a BN which includes a node that represents the object’s position, angle and scale. The object detection can be achieved by inference for the node.
Cite
Text
Niitsuma. "Bayesian Network Based Reparameterization of Haar-like Feature." AAAI Conference on Artificial Intelligence, 2006.Markdown
[Niitsuma. "Bayesian Network Based Reparameterization of Haar-like Feature." AAAI Conference on Artificial Intelligence, 2006.](https://mlanthology.org/aaai/2006/niitsuma2006aaai-bayesian/)BibTeX
@inproceedings{niitsuma2006aaai-bayesian,
title = {{Bayesian Network Based Reparameterization of Haar-like Feature}},
author = {Niitsuma, Hirotaka},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2006},
url = {https://mlanthology.org/aaai/2006/niitsuma2006aaai-bayesian/}
}