文章基本信息

标题：Multiple View Relations Using the Teaching and Learning-Based Optimization Algorithm
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作者：Alan López-Martínez ; Francisco Javier Cuevas
期刊名称：Computers
电子版ISSN：2073-431X
出版年度：2020
卷号：9
期号：4
页码：101-120
DOI：10.3390/computers9040101
出版社：MDPI Publishing
摘要：In computer vision, estimating geometric relations between two different views of the same scene has great importance due to its applications in 3D reconstruction, object recognition and digitization, image registration, pose retrieval, visual tracking and more. The Random Sample Consensus (RANSAC) is the most popular heuristic technique to tackle this problem. However, RANSAC-like algorithms present a drawback regarding either the tuning of the number of samples and the threshold error or the computational burden. To relief this problem, we propose an estimator based on a metaheuristic, the Teaching–Learning-Based Optimization algorithm (TLBO) that is motivated by the teaching–learning process. We use the TLBO algorithm in the problem of computing multiple view relations given by the homography and the fundamental matrix. To improve the method, candidate models are better evaluated with a more precise objective function. To validate the efficacy of the proposed approach, several tests, and comparisons with two RANSAC-based algorithms and other metaheuristic-based estimators were executed.
关键词：epipolar geometry; fundamental matrix; homography; RANSAC; metaheuristics; TLBO epipolar geometry ; fundamental matrix ; homography ; RANSAC ; metaheuristics ; TLBO