In this correspondence, we present a new algorithm to determine optical flow that utilizes a correlation-feedback technique. Several experiments are presented to demonstrate that our method performs generally better than some standard correlation and gradient-based methods in terms of accuracy.

In this paper, a Kalman filter-based algorithm for 3-D motion estimation from a stereo image sequence using the unified temporal-spatial optical flow field (UOFF) has been proposed. The modeling problem is discussed first. More consideration has been given to determining the covariance matrices for system noise and sensor noise than the previous works using Kalman filtering in image sequence processing. The newly visible image areas, i.e., the disocclusion issue, which have not been considered in the most of previous works, are handled in our algorithm by using a threshold method. Two experiments are presented to demonstrate the effectiveness of our algorithm.>

The optical flow techniques have been developed for more than one decade. Once the optical flow field is computed accurately, this measurement of image velocity can be used widely in many tasks in computer vision area. Current computer vision techniques require that the relative errors in the optical flow be less than 10%. However, to reduce error in determination of the optical flow is still a difficult problem. In this paper, firstly, errors occurring in the correlation-based approaches to optical flow computation are analyzed. Through understanding how the errors arise, we developed a new approach to computation of optical flow named the correlation-feedback approach. It is based on the idea of feedback and the correlation-based approach. In this approach, a virtual continuous image is obtained by a bilinear interpolation applied to a digital image. The idea of feedback is used so that errors in determining optical flow are reduced considerably in the iterative procedure. It is proved that the algorithm is convergent generally. Several experiments working on real image sequences in the laboratory demonstrate that our correlation-feedback algorithm performs better than the gradient-based and correlation-based algorithms in terms of accuracy.>