Confidence measures for image motion estimation
Avantika Dev, Ben Kröse, F.C.A. Groen · 1997
Estimation of image motion, also known as the optic flow, from a sequence of images is known to be difficult. This is due to: the sensitivity of the image motion model to noise (the derivative property), the limited observability of the image motion from the luminance (the aperture problem) , and, the non-validity of the optic flow constraint (the assumption of intensity conservation). In this paper we analyze measures that assign a confidence value to the estimated image motion: the sensitivity of the model to noise, the validity of the model and the estimated variance of the image motion. Experiments show that selection of image motion vectors based on these measures dramatically improve the estimates of the image motion while keeping as much image motion vectors as possible. We conclude that the proposed estimated variance of the image motion optimizes this trade-off. 2. Introduction The estimation of image motion v(x; t) from a sequence of images I (x; t) is a well addressed [8] ...