Adaptive Spatiotemporal Similarity Measure for Consistent Depth Maps
– Published Date : August, 2017
– Place of publication : The 4th International Conference on Computer Applications and Information Processing Technology (CAIPT 2017) Bali, Indonesia, August 8-10 (http://www.caipt.org).
Abstract
When computing a depth map sequence of a stereo image sequence, the temporal consistency of computed depth maps is a very important factor along with the accuracy. In this paper, we propose a new similarity measure for spatiotemporal stereo matching aiming at producing temporally consistent depth maps from a stereo image sequence. To enforce the temporal consistency in a spatiotemporal similarity measure, we assign adaptive support weights to pixels in a spatiotemporal window and define the four-dimensional support region in consideration
of the motion and depth variation along the time. In addition, we model the support weight to be less sensitive to illumination variation. The similarity is computed simply by comparing two support regions with computed support weights. The proposed similarity measure truly improves the performance of stereo matching both in the accuracy and in the consistency aspects.