Multiple View Relations Using the Teaching and Learning-Based Optimization Algorithm

Abstract

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.

Bulk Drag Predictions of Riparian Arundo donax Stands through UAV-Acquired Multispectral Images

“…Sparse point cloud cleaning, removing all points with reprojection error [30] higher than 1, camera alignment optimization, and dense cloud building; 6.…”

An Efficient Point-Matching Method Based on Multiple Geometrical Hypotheses

“…By using invariant descriptors, you can resolve the point-matching problem by extending the methods commonly used in stereovision. Normally, the correspondence is determined from the points of interest (PoI) previously obtained by an algorithm able to detect the regions of interest (RoI) [2][3][4]. However, when the current saliency techniques [5][6][7][8] cannot detect the PoI, determining its matching pair or triplet into other images becomes a difficult task.…”

“…Over the last 20 years, different methods for addressing the point-matching problem have been proposed. Methods and techniques based on the analysis and optimization of the invariant descriptors [12][13][14], estimation of affine transformations/homographies/perspective transformations [15][16][17], epipolar geometry analysis [18][19][20], optical flow-based methods [21,22], and methods based on geometric and photometric constraints [4,23] are some of the approaches already explored.…”