Abstract
The paper proposes a robust estimation method which implements, in cascade, two algorithms: (i) a Random Sample and Consensus (RANSAC) algorithm and (ii) a novel nonlinear prediction error estimator minimizing a cost function inspired by the mathematical definition of Gibbs entropy. The minimization of the nonlinear cost function allows to refine the Consensus Set found with standard RANSAC in order to reach optimal estimates of geometric transformation parameters under image stitching context. The method has been experimentally tested and compared with a standard RANSAC-MSAC algorithm where noticeable improvements are recorded in terms of computational complexity and quality of the stitching process, namely of the mean squared symmetric re-projection error.
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