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 contribution | Supervised Segmentation and Tracking of Non-rigid Objects using a ""Mixture of Histograms"" Model |
|---|---|
| Original language | English |
| Title of host publication | Unknown |
| Editors | - |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| Pages | 62 - 65 |
| Number of pages | 3 |
| Publication status | Published - Oct 2001 |
Bibliographical note
Conference Proceedings/Title of Journal: Proceedings of the 8th IEEE International Conference on Image Processing (ICIP 2001)Fingerprint Dive into the research topics of 'Supervised Segmentation and Tracking of Non-rigid Objects using a ""Mixture of Histograms"" Model'. Together they form a unique fingerprint.
Cite this
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