Abstract
Disparity contours are easily computed from stereo image pairs, given a known background geometry. They facilitate the segmentation and depth calculation of multiple foreground objects even in the presence of changing lighting, complex shadows and projected video background. Not relying on stereo reconstruction or prior knowledge of foreground objects, a disparity contour based image segmentation method is fast enough for some real-time applications on commodity hardware. Experimental results demonstrate its ability to extract object contour from a complex scene and distinguish multiple objects by estimated depth even when they are partially occluded.
This research was carried out at the Centre for Intelligent Machines, McGill University, Montreal, Quebec, Canada.
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Sun, W., Spackman, S.P. (2008). Disparity Contours – An Efficient 2.5D Representation for Stereo Image Segmentation. In: Braz, J., Ranchordas, A., Araújo, H.J., Pereira, J.M. (eds) Computer Vision and Computer Graphics. Theory and Applications. VISIGRAPP 2007. Communications in Computer and Information Science, vol 21. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89682-1_16
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