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Foreground and Shadow Detection Based on Conditional Random Field

  • Yang Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4673)

Abstract

This paper presents a conditional random field (CRF) approach to integrate spatial and temporal constraints for moving object detection and cast shadow removal in image sequences. Interactions among both detection (foreground/background/shadow) labels and observed data are unified by a probabilistic framework based on the conditional random field, where the interaction strength can be adaptively adjusted in terms of data similarity of neighboring sites. Experimental results show that the proposed approach effectively fuses contextual dependencies in video sequences and significantly improves the accuracy of object detection.

Keywords

Conditional random field contextual constraint object detection shadow removal 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Yang Wang
    • 1
  1. 1.National ICT Australia, Kensington, NSW 2032Australia

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