In several machine vision problems, a relevant issue is the estimation of homographies between two different perspectives that\nhold an extensive set of abnormal data. A method to find such estimation is the random sampling consensus (RANSAC); in this,\nthe goal is to maximize the number of matching points given a permissible error (Pe), according to a candidate model. However,\nthose objectives are in conflict: a low Pe value increases the accuracy of the model but degrades its generalization ability that refers\nto the number of matching points that tolerate noisy data, whereas a high Pe value improves the noise tolerance of the model but\nadversely drives the process to false detections. This work considers the estimation process as a multi objective optimization problem\nthat seeks to maximize the number of matching points whereas Pe is simultaneously minimized. In order to solve the multiobjective\nformulation, two different evolutionary algorithms have been explored: the Nondominated Sorting Genetic Algorithm II (NSGA-II)\nand the Nondominated Sorting Differential Evolution (NSDE). Results considering acknowledged quality measures among original\nand transformed images over a well-known image benchmark show superior performance of the proposal than Random Sample\nConsensus algorithm.
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