Image sequence processing using spatiotemporal segmentation

We investigate the improvements that can be obtained in several conventional video-processing algorithms through the incorporation of three-dimensional (3-D) (spatiotemporal) segmentation information. Four classes of image sequence professing techniques are considered: low-pass' filtering, high-pass filtering, high-frequency emphasis, and 3-D Sobel filtering, It is demonstrated that segmentation information can improve the performance of these techniques substantially so that this approach may be promising for other applications (e.g., deinterlacing and resolution conversion) as well.