Spatial and Fourier Error Minimization for Motion Estimation and Segmentation
We present a new approach to motion estimation by minimizing the squared error in both the spatial and frequency domains and we show that the spatially global nature of FT leads to a motion estimation error that is much lower than that obtained via spatial motion estimation. On the other hand, spatial analysis is useful for accurate segmentation. We describe a novel, hybrid approach combining the above two estimates of motion and segmentation. We examine the robustness of minimizing the error terms in both domains, both theoretically and experimentally. Experiments with real and synthetic sequences demonstrate the capabilities of the proposed algorithm