Probabilistic Fusion of Stereo with Color and Contrast for Bi-Layer Segmentation

This paper describes two algorithms for the real-time segmentation of foreground from background layers in stereo video sequences. Automatic separation of layers from colour/contrast or from stereo alone is known to be error-prone. Here, colour, contrast and stereo matching information are fused to infer layers accurately and efficiently. The first algorithm, Layered Dynamic Programming (LDP), solves stereo in an extended 6-state space that represents both foreground/background layers and occluded regions. The stereo-match likelihood is then fused with a contrast-sensitive colour model that is learned on the fly, and stereo disparities are obtained by dynamic programming. The second algorithm, Layered Graph Cut (LGC), does not directly solve stereo. Instead the stereo match likelihood is marginalised over disparities to evaluate foreground and background hypotheses, and then fused with a contrast-sensitive colour model like the one used in LDP. Segmentation is solved efficiently by te...

• Type: Journal
• Year: 2008
• Where: IJCV
• Authors: Vladimir Kolmogorov, Antonio Criminisi, Andrew Blake, Geoffrey Cross, Carsten Rother
• Keywords: Algorithms, Contrast-sensitive Colour Model, IJCV 2008, Stereo