Computing Dense Displacement Fields With Confidence Measures In Scenes Containing Occlusion

Abstract

Matching successive frames of a dynamic image sequence using area correlation has been studied for many years by researchers in machine vision. Most of these efforts have gone into improving the speed and the accuracy of correlation matching algorithms. Yet, the displacement fields produced by these algorithms are often incorrect in homogeneous areas of the image and in areas which are visible in one frame, but are occluded in the succeeding frames. Further, these displacement fields are often incorrect even at non-occluded areas that border occlusion boundaries. In this paper, we present a confidence measure which indicates the reliability of each displacement vector computed by a specific hierarchical correlation matching algorithm. We also provide an improved hierarchical matching algorithm which performs particularly well near occlusion boundaries. We demonstrate these with experiments performed on real image sequences taken in our robotics labaratory. A more detailed version of this work appears in (Anan84).