International Journal of Computer Vision
Video object segmentation by motion-based sequential feature clustering
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
CoCRF deformable model: a geometric model driven by collaborative conditional random fields
IEEE Transactions on Image Processing
Detecting object boundaries using low-, mid-, and high-level information
Computer Vision and Image Understanding
Conditional random field for text segmentation from images with complex background
Pattern Recognition Letters
Deformable probability maps: Probabilistic shape and appearance-based object segmentation
Computer Vision and Image Understanding
Inferring laser-scan matching uncertainty with conditional random fields
Robotics and Autonomous Systems
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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.