A Multiobjective Approach to Homography Estimation

• Published in: Computational Intelligence and Neuroscience Vol. 2016; no. 2016; pp. 695 - 706-059
• Main Authors: Pérez-Cisneros, Marco, Zúñiga, Virgilio, Oliva, Diego, Cuevas, Erik, Osuna-Enciso, Valentín, Zaldivar, Daniel
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Limiteds 01.01.2016; Hindawi Publishing Corporation; John Wiley & Sons, Inc
• Subjects: Algorithms; Artificial Intelligence; Cameras; Computer Simulation; Decision Support Techniques; Evolutionary algorithms; Genetic algorithms; Humans; Image quality; Intelligence; Machine learning; Machine vision; Matching; Mathematical research; Models, Theoretical; Optimization; Pattern Recognition, Automated - methods; Proposals; Sorting; Technology application; Canada; Mexico
• ISSN: 1687-5265; 1687-5273; 1687-5273
• DOI: 10.1155/2016/3629174

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