Learning spatial context from tracking using penalized likelihood estimation

Learning spatial context from tracking using penalised likelihoods

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

Publication Date: 01/01/2004

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., Nait-Charif, H.

Publication Date: 2004

Pages: 138-141

DOI: 10.1109/ICPR.2004.1333723

Source: Web of Science

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

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

Publication Date: 2004

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., Nait-Charif, H.

Publication Date: 2004

Pages: 138-141

Publisher: IEEE Computer Society

DOI: 10.1109/ICPR.2004.1333723

https://ieeexplore.ieee.org/xpl/conhome/9258/proceeding

Source: DBLP