Variational Maximum A Posteriori model similarity and dissimilarity matching

Author

Chiverton, John ; Mirmehdi, Majid ; Xie, Xianghua

Author_Institution

Dept. of Comput. Sci., Univ. of Bristol, Bristol

fYear

2008

fDate

8-11 Dec. 2008

Firstpage

1

Lastpage

4

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.

Keywords

image matching; maximum likelihood estimation; object detection; variational techniques; background distribution; foreground pixels; model distribution; object tracking; robust discrimination; variational maximum a posteriori contextual modeling; variational maximum a posteriori model dissimilarity matching; variational maximum a posteriori model similarity matching; Active shape model; Computer science; Context modeling; Image segmentation; Labeling; Maximum likelihood estimation; Photometry; Pixel; Probability; Robustness;

fLanguage

English

Publisher

ieee

Conference_Titel

Pattern Recognition, 2008. ICPR 2008. 19th International Conference on

Conference_Location

Tampa, FL

ISSN

1051-4651

Print_ISBN

978-1-4244-2174-9

Electronic_ISBN

1051-4651

Type

conf

DOI

10.1109/ICPR.2008.4761600

Filename

4761600