A dynamic conditional random field model for object segmentation in image sequences

This paper presents a dynamic conditional random field (DCRF) model to integrate contextual constraints for object segmentation in image sequences. Spatial and temporal dependencies within the segmentation process are unified by a dynamic probabilistic framework based on the conditional random field...

Full description

Saved in:
Bibliographic Details
Published in2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) Vol. 1; pp. 264 - 270 vol. 1
Main Authors Yang Wang, Qiang Ji
Format Conference Proceeding
LanguageEnglish
Published IEEE 2005
Subjects
Filtering algorithms
Hidden Markov models
History
Image segmentation
Image sequences
Layout
Motion estimation
Object segmentation
Recursive estimation
Video sequences
Online AccessGet full text

Cover

More Information
Summary:This paper presents a dynamic conditional random field (DCRF) model to integrate contextual constraints for object segmentation in image sequences. Spatial and temporal dependencies within the segmentation process are unified by a dynamic probabilistic framework based on the conditional random field (CRF). An efficient approximate filtering algorithm is derived for the DCRF model to recursively estimate the segmentation field from the history of video frames. The segmentation method employs both intensity and motion cues, and it combines dynamic information and spatial interaction of the observed data. Experimental results show that the proposed approach effectively fuses contextual constraints in video sequences and improves the accuracy of object segmentation.
ISBN:0769523722
9780769523729
ISSN:1063-6919
1063-6919
DOI:10.1109/CVPR.2005.26