Lucas-kanade inverse compositional using multiple brightness and gradient constraints

A recently proposed fast image alignment algorithm is the inverse compositional algorithm based on Lucas-Kanade. In this paper, we present an overview of different brightness and gradient constraints used with the inverse compositional algorithm. We also propose an efficient and robust data constraint for the estimation of global motion from image sequences. The constraint combines brightness and gradient constraints under multiple quadratic errors. The method can accommodate various motion models. We concentrate on the global efficiency of the constraint in capturing the global motion for image alignment. We have applied the algorithm to various test sequences with ground truth. From the experimental results we conclude that the new constraint provides reduced motion error at the expense of extra computations.