Detection and segmentation of moving objects in complex scenes
Computer Vision and Image Understanding
3D motion segmentation from straight-line optical flow
MCAM'07 Proceedings of the 2007 international conference on Multimedia content analysis and mining
Direct segmentation of multiple 2-D motion models of different types
WDV'05/WDV'06/ICCV'05/ECCV'06 Proceedings of the 2005/2006 international conference on Dynamical vision
SAMT'10 Proceedings of the 5th international conference on Semantic and digital media technologies
A bottom up algebraic approach to motion segmentation
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
Dynamic texture analysis and segmentation using deterministic partially self-avoiding walks
Expert Systems with Applications: An International Journal
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We present a closed form solution to the problem of segmenting multiple 2-D motion models of the same type directly from the partial derivatives of an image sequence. We introduce the multibody brightness constancy constraint (MBCC), a polynomial equation relating motion models, image derivatives and pixel coordinates that is independent of the segmentation of the image measurements. We first show that the optical flow at a pixel can be obtained analytically as the derivative of the MBCC at the corresponding image measurement, without knowing the motion model associated with that pixel. We then show that the parameters of the multiple motion models can be obtained from the cross products of the derivatives of the MBCC at a set of image measurements that minimize a suitable distance function. Our approach requires no feature tracking, point correspondences or optical flow, and provides a global non-iterative solution that can be used to initialize more expensive iterative approaches to motion segmentation. Experiments on real and synthetic sequences are also presented.