A Framework for the Robust Estimation of Optical Flow

We conszder the problem of robustly estzmatzng optical flow f rom a paw of zinages uszng a new framework based on robust estimation whzch addresses iizolatzons of the brzghtness constancy and spatzal smoothness asbuinptzons. We also show the rrlatzonshzp between the robust estimation framework and he-process approaches for copzng wzth spatzal dascontznuities. In doang so, we generalaze the notzon of a lane proce.ss to that of an outlier process that can account for vzolatzons zn both the brzghtness and smoothness assumption>. We develop a Graduated A’on-(’onvexzty algorzthm f o r recovcrzng oplzcal flow and motzon dzscontznuztzes and demonstrate the performance of the robust formulatzon on both synthetzc data and natural zrnages.