RANSAC-LEL: An optimized version with Least Entropy Like Estimators
Authors: Distante C., Indiveri G.
Autors Affiliation: CNR – Istituto Nazionale di Ottica, Arnesano (LE):
Dipartimento ingegneria Innovazione, Università del Salento, via Monteroni, 73100 Lecce, Italy
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.
Conference title: IEEE International Conference on Image Processing
KeyWords: homography estimation; Ransac; image processing; mosaicking; DOI: 10.1109/ICIP.2011.6115709