Probabilistic Scene Analysis for Robust Stereo Correspondence
ICIAR '09 Proceedings of the 6th International Conference on Image Analysis and Recognition
Probabilistic categorization of kitchen objects in table settings with a composite sensor
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Dynamic edge detection and analysis by multiple frame based derivative tensor
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Detecting spatiotemporal structure boundaries: beyond motion discontinuities
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
International Journal of Computer Vision
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We present a novel method for motion segmentation and depth ordering from a video sequence in general motion. We first compute motion segmentation based on differential properties of the spatio-temporal domain, and scale-space integration. Given a motion boundary, we describe two algorithms to determine depth ordering from two- and three- frame sequences. An remarkable characteristic of our method is its ability compute depth ordering from only two frames. The segmentation and depth ordering algorithms are shown to give good results on 6 real sequences taken in general motion. We use synthetic data to show robustness to high levels of noise and illumination changes; we also include cases where no intensity edge exists at the location of the motion boundary, or when no parametric motion model can describe the data. Finally, we describe human experiments showing that people, like our algorithm, can compute depth ordering from only two frames, even when the boundary between the layers is not visible in a single frame.