Motion parameter constraints analysis from a single image

Motion parameter estimation is a fundamental problem in image processing and image understanding. A large number of algorithms have been proposed based on a number of different geometrical considerations, such as perspective or epipolar geometries. However, proposed motion estimation algorithms do not explicitly use the distance between feature points and angle information as rigid constraints to calibration. In this paper, we present a new geometric analysis of correspondence data and derive explicit expressions for rigid constraints that are then used to estimate motion parameters. We then present a novel, efficient motion parameter estimation algorithm based on a coarse to fine strategy from a single image data. For a comparative study of performance, we also extended to the 3D-2D case a well known 2D-2D motion estimation algorithm based on the epipolar geometry. Experimental results demonstrate that the coarse to fine strategy is appropriate for the problem and that the algorithm generally performs better than the extended epipolar geometry based algorithm