Learning spatial context from tracking using penalised likelihoods
Authors: McKenna, S.J. and Nait-Charif, H.
Volume: 4
Pages: 138-141
DOI: 10.1109/icpr.2004.1333723
Abstract: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.
Source: Scopus
Learning spatial context from tracking using penalised likelihoods
Authors: McKenna, S.J. and Nait-Charif, H.
Pages: 138-141
DOI: 10.1109/ICPR.2004.1333723
Source: Web of Science (Lite)
Learning spatial context from tracking using penalized likelihood estimation
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
Source: Manual
Preferred by: Hammadi Nait-Charif
Learning Spatial Context from Tracking using Penalised Likelihoods.
Authors: McKenna, S.J. and Nait-Charif, H.
Pages: 138-141
Publisher: IEEE Computer Society
DOI: 10.1109/ICPR.2004.1333723
https://ieeexplore.ieee.org/xpl/conhome/9258/proceeding
Source: DBLP