Nonlinear inverse model for velocity estimation from an image sequence

Abstract

[1] Velocity estimation from an image sequence is one of the most challenging inverse problems in computer vision, geosciences, and remote sensing applications. In this paper a nonlinear model has been created for estimating motion field under the constraint of conservation of intensity. A linear differential form of heat or optical flow equation is replaced by a nonlinear temporal integral form of the intensity conservation constraint equation. Iterative equations with Gauss-Newton and Levenberg-Marguardt algorithms are formulated based on the nonlinear equations, velocity field modeling, and a nonlinear least squares model. An algorithm with progressive relaxation of the overconstraint to improve the performance of the velocity estimation is also proposed. The new estimator is benchmarked using a numerical simulation model. Both angular and magnitude error measurements based on the synthetic surface heat flow from the numerical model demonstrate that the performance of the new approach with the nonlinear model is much better than the results of using a linear model of heat or optical flow equation. Four sequences of NOAA Advanced Very High Resolution Radiometer (AVHRR) images taken in the New York Bight fields is also used to demonstrate the performance of the nonlinear inverse model, and the estimated velocity fields are compared with those measured with the Coastal Ocean Dynamics Radar array. The experimental results indicate that the nonlinear inverse model provides significant improvement over the linear inverse model for real AVHRR data sets.