Optical flow refers to the visual motion observed between two consecutive images. Since the degree of freedom is typically much larger than the constraints imposed by the image observations, the straightforward formulation of optical flow inference is an ill-posed problem. By setting some type of additional “regularity” constraints, classical approaches formulate a well-posed optical flow infer...

In this paper we present a new method to estimate optical flow for large displacements. It is based on prediction of global flow field parameters, performs better than multiresolution estimation methods and has been verified using standard test sequences as well as real-world data. Global flow field parameters can be estimated from optical flow measurements in all flow regions. They can then be...

Many optical flow estimation techniques are based on the differential optical flow equation. These algorithms involve solving over-determined systems of optical flow equations. Least squares (LS) estimation is usually used to solve these systems even though the underlying noise does not conform to the model implied by LS estimation. To ameliorate this problem, work has been done using the total...

Robust Incremental Optical Flow Michael Julian Black Yale University 1992 This thesis addresses the problem of recovering 2D image velocity, or optical flow, robustly over long image sequences. We develop a robust estimation framework for improving the reliability of motion estimates and an incremental minimization framework for recovering flow estimates over time. Attempts to improve the robus...

Usually, optical flow computation is based on grayscale images and the brightness conservation assumption. Recently, some authors have investigated in transferring gradient-based grayscale optical flow methods to color images. These color optical flow methods are restricted to brightness and color conservation over time. In this paper, a correlation-based color optical flow method is presented ...