Abstract
A new variational Maximum A Posteriori (MAP) contextual modeling approach is presented that minimizes the product of two ratios: (a) the ratio of the model distribution to the distribution of currently estimated foreground pixels; (b) the ratio of the background distribution to the model distribution for all estimated background pixels. This approach provides robust discrimination to identify the division between foreground and background pixels, which is useful for applications such as object tracking.
| Translated title of the contribution | Variational Maximum A Posteriori Model Similarity and Dissimilarity Matching |
|---|---|
| Original language | English |
| Title of host publication | International Conference on Pattern Recognition |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| Publication status | Published - 2008 |
Bibliographical note
Other page information: -Conference Proceedings/Title of Journal: International Conference on Pattern Recognition
Other identifier: 2000931