CSDL Home IEEE Transactions on Pattern Analysis & Machine Intelligence 2006 vol.28 Issue No.11 - November
Optical Flow 3D Segmentation and Interpretation: A Variational Method with Active Curve Evolution and Level Sets
Issue No.11 - November (2006 vol.28)
A. Mitiche , Institut Nat. de la Recherche Sci., INRS-EMT, Montreal, Que.
H. Sekkati , Institut Nat. de la Recherche Sci., INRS-EMT, Montreal, Que.
This study investigates a variational, active curve evolution method for dense three-dimensional (3D) segmentation and interpretation of optical flow in an image sequence of a scene containing moving rigid objects viewed by a possibly moving camera. This method jointly performs 3D motion segmentation, 3D interpretation (recovery of 3D structure and motion), and optical flow estimation. The objective functional contains two data terms for each segmentation region, one based on the motion-only equation which relates the essential parameters of 3D rigid body motion to optical flow, and the other on the Horn and Schunck optical flow constraint. It also contains two regularization terms for each region, one for optical flow, the other for the region boundary. The necessary conditions for a minimum of the functional result in concurrent 3D-motion segmentation, by active curve evolution via level sets, and linear estimation of each region essential parameters and optical flow. Subsequently, the screw of 3D motion and regularized relative depth are recovered analytically for each region from the estimated essential parameters and optical flow. Examples are provided which verify the method and its implementation
Image motion analysis, Level set, Image segmentation, Motion estimation, Image sequences, Layout, Cameras, Motion segmentation, Computer vision, Equations,image sequence analysis., Optical flow, 3D segmentation, 3D interpretation, level sets
A. Mitiche, H. Sekkati, "Optical Flow 3D Segmentation and Interpretation: A Variational Method with Active Curve Evolution and Level Sets", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.28, no. 11, pp. 1818-1829, November 2006, doi:10.1109/TPAMI.2006.232