Learning spatial context from tracking using penalised likelihoods

This source preferred by Hammadi Nait-Charif

Authors: McKenna, S.J. and Nait-Charif, H.

Editors: Kittler, J., Petrou, M. and Nixon, M.

Volume: 4

Pages: 138-141

Publisher: IEEE Computer Society

Place of Publication: Los Alamitos

This data was imported from DBLP:

Authors: McKenna, S.J. and Nait-Charif, H.

http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=9258

Pages: 138-141

Publisher: IEEE Computer Society

DOI: 10.1109/ICPR.2004.1333723

This data was imported from Scopus:

Authors: McKenna, S.J. and Nait-Charif, H.

Volume: 4

Pages: 138-141

DOI: 10.1109/icpr.2004.1333723

MAP estimation of Gaussian mixtures through maximisation of penalised likelihoods was used to learn models of spatial context. This enabled prior beliefs about the scale, orientation and elongation of semantic regions to be encoded, encouraging one-to-one correspondences between mixture components and these regions. In conjunction with minimum description length this enabled automatic learning of inactivity zones and entry zones from track data in a supportive home environment.

This data was imported from Web of Science (Lite):

Authors: McKenna, S.J. and Nait-Charif, H.

Pages: 138-141

DOI: 10.1109/ICPR.2004.1333723

The data on this page was last updated at 05:19 on October 21, 2020.