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A Dynamic Conditional Random Field Model for Object Segmentation in Image Sequences

10 years 2 months ago
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 (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.
Qiang Ji, Yang Wang 0002
Added 12 Oct 2009
Updated 29 Oct 2009
Type Conference
Year 2005
Where CVPR
Authors Qiang Ji, Yang Wang 0002
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