Mixture of General Gaussians (MOGG)

Note: This list of publications comes from my research. Please cite my following paper:

T. Bouwmans, F. El Baf, B. Vachon, “Statistical Background Modeling for Foreground Detection: A Survey”, Handbook of Pattern Recognition and Computer Vision, World Scientific Publishing, Volume 4, Part 2, Chapter 3, pages 181-199, January 2010.

List of Publications on Background Modeling using Mixture of General Gaussians for Foreground Detection

M. Allili, N. Bouguila, D. Ziou, “A Robust Video Foreground Segmentation by Using Generalized Gaussian Mixture Modeling”, Fourth Canadian Conference on Computer and Robot Vision, CRV 2007, pages 503-509, 2007.

M. Allili, N. Bouguila, D.Ziou, “Finite Generalized Gaussian Mixture Modeling and Applications to Image and Video Foreground Segmentation”, Fourth Canadian Conference on Computer and Robot Vision, CRV 2007, pages 183-190, 2007.

M. Allili, N. Bouguila, D. Ziou,”Finite Generalized Gaussian Mixture Modelling and Application to Image and Video Foreground Segmentation”, Journal of Electronic Imaging, 2008.

A. Boulmerka, M. Allili, "Background modeling in videos revisited using finite mixtures of generalized {Gaussians} and spatial information", IEEE International Conference on Image Processing, ICIP 2015, September 2015.

A. Boulmerka, M. Allili, "Foreground Segmentation in Videos Combining General Gaussian Mixture Modeling and Spatial Information", IEEE Transactions on Circuits and Systems for Video Technology, 2017.

S. Amudala, S. Ali, N. Bouguila, "Background Subtraction with a Hierarchical Pitman-Yor Process Mixture Model of Generalized Gaussian Distributions", IEEE nternational Conference on Information Reuse and Integration for Data Science, IRI 2020, pages 112-119 Las Vegas, USA, 2020.