Supervised Segmentation and Tracking of Non-rigid Objects using a ""Mixture of Histograms"" Model

M Everingham, B Thomas

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

6 Citations (Scopus)

Abstract

Segmentation and tracking of objects in video sequences is important for a number of applications. In the supervised variant, segmentation can be achieved by modelling the probability density of image observations taken from an object for use in a Bayesian classifier, and Gaussian mixture models have been applied to this task by several researchers. Motivated by practical difficulties we have experienced with these models we propose a novel and simple alternative approach which combines a strong shape model with histograms of image features and gives good empirical results on test sequences requiring flexible models.
Translated title of the contributionSupervised Segmentation and Tracking of Non-rigid Objects using a ""Mixture of Histograms"" Model
Original languageEnglish
Title of host publicationUnknown
Editors-
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages62 - 65
Number of pages3
Publication statusPublished - Oct 2001

Bibliographical note

Conference Proceedings/Title of Journal: Proceedings of the 8th IEEE International Conference on Image Processing (ICIP 2001)

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    Everingham, M., & Thomas, B. (2001). Supervised Segmentation and Tracking of Non-rigid Objects using a ""Mixture of Histograms"" Model. In . (Ed.), Unknown (pp. 62 - 65). Institute of Electrical and Electronics Engineers (IEEE). http://www.cs.bris.ac.uk/Publications/pub_info.jsp?id=1000569