Motion Estimation And Segmentation
Abbreviated Journal Title
Mach. Vis. Appl.
optical flow; motion boundaries; line process; Markov random fields; mean field techniques; OPTICAL-FLOW; VISUAL-MOTION; FIELDS; PARALLEL; VISION; Computer Science, Artificial Intelligence; Computer Science, ; Cybernetics; Engineering, Electrical & Electronic
In the general structure-from-motion (SFM) problem involving several moving objects in a scene, the essential first step is to segment moving objects independently. We attempt to deal with the problem of optical flow estimation and motion segmentation over a pair of images. We apply a mean field technique to determine optical flow and motion boundaries and present a deterministic algorithm. Since motion discontinuities represented by line process are embedded in the estimation of the optical flow, our algorithm provides accurate estimates of optical flow especially along motion boundaries and handles occlusion and multiple motions. We show that the proposed algorithm outperforms other well-known algorithms in terms of estimation accuracy and timing.
Machine Vision and Applications
"Motion Estimation And Segmentation" (1996). Faculty Bibliography 1990s. 1775.