@inproceedings{0ba9a3000edb42c7b3eb39023c7d1f60,
title = "Fundamental matrix estimation based on improved genetic algorithm",
abstract = "Fundamental matrix estimation is the key technology in 3D reconstruction and widely used in many aspects in computer vision. In this paper, a global search genetic algorithm combining with a local search hill climbing algorithm is proposed to optimize MAPSAC algorithm for estimating fundamental matrix. The average distances between points and epipolar lines under noise and outliers are investigated with synthetic and real image feature point data respectively. The simulation results show proposed method is more robust to noise and outliers and can estimate fundamental matrix more precisely.",
keywords = "Computer vision, Epipolar geometry, Fundamental matrix, Genetic algorithm, Hill climbing algorithm",
author = "Ying Zhang and Lan Zhang and Changyin Sun and Guifeng Zhang",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 8th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2016 ; Conference date: 11-09-2016 Through 12-09-2016",
year = "2016",
month = dec,
day = "13",
doi = "10.1109/IHMSC.2016.46",
language = "英语",
series = "Proceedings - 2016 8th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "326--329",
booktitle = "Proceedings - 2016 8th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2016",
}