Abstract
It is well known that it is very difficult to estimate accurate optical flow or correspondence at locations in an image, which correspond to scene discontinuities. What is less well known, however, is that even at the locations corresponding to smooth scene surfaces, the optical flow field often cannot be estimated accurately. Noise in the data causes many optical flow estimation techniques to give biased flow estimates. Very often, there is consistent bias: the estimate tends to be an underestimate in length and to be in a direction closer to the majority of the gradients in the patch. This paper studies all three major categories of flow estimation methods-gradient-based, energy-based, and correlation methods, and it analyzes different ways of compounding one-dimensional motion estimates (image gradients, spatiotemporal frequency triplets, local correlation estimates) into two-dimensional (2D) velocity estimates, including linear and nonlinear methods.Correcting for the bias would require knowledge of the noise parameters. In many situations, however, these are difficult to estimate accurately, as they change with the dynamic imagery in unpredictable and complex ways. Thus, the bias really is a problem inherent to optical flow estimation. We argue that the bias is also integral to the human visual system. It is the cause of the illusory perception of motion in the Ouchi pattern (Figure 1) and explains various psychophysical studies of the perception of moving plaids. Finally, the implication of the analysis is that flow or correspondence can be estimated very accurately only when feedback is utilized. Estimates of normal flow are used for obtaining 3D motion and structure, which are then utilized in a feedback scheme to better estimate correspondence.