Vanishing point detection using the teaching learning‐based optimisation algorithm

Abstract

Within the computer vision field, estimating image vanishing points has many applications regarding robotic navigation, camera calibration, image understanding, visual measurement, 3D reconstruction, among others. Different methods for detecting vanishing points relies on accumulator space techniques, while others employ a heuristic approach such as RANSAC. Nevertheless, these types of methods suffer from low accuracy or high computational cost. To explore a different technique, this paper focuses on improving the efficiency of the metaheuristic search for vanishing points by using a recently proposed population‐based method: The Teaching Learning Based Optimisation algorithm (TLBO). The TLBO algorithm is a metaheuristic technique inspired by the teaching–learning process. In our method, the TLBO algorithm is used after a line segment detection, to cluster line segments according to their more optimal vanishing point. Thus, our algorithm detects both orthogonal and nonorthogonal vanishing points in real images. To corroborate the performance of our proposed algorithm, different comparison and tests with other approaches were carried out. The results validate the accuracy and efficiency of our proposed method. Our approach had an average computational time of1.42 seconds and obtained a cumulative focal length error of 1 pixel, and cumulative angular error of 0.1°.

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

“…Therefore, several engineering and scientific applications using this metaheuristic have been published [40][41][42][43]. Differently from the previous work in [44][45][46], this paper proposes a novel application for the TLBO algorithm. Instead of searching for image patterns like the work in [44,45] or detecting vanishing points [46], in this work the TLBO algorithm is implemented for the novel task of estimating the fundamental matrix and homography.…”

“…Differently from the previous work in [44][45][46], this paper proposes a novel application for the TLBO algorithm. Instead of searching for image patterns like the work in [44,45] or detecting vanishing points [46], in this work the TLBO algorithm is implemented for the novel task of estimating the fundamental matrix and homography. The work in [46] deals with the problem of vanishing points detection.…”

“…Instead of searching for image patterns like the work in [44,45] or detecting vanishing points [46], in this work the TLBO algorithm is implemented for the novel task of estimating the fundamental matrix and homography. The work in [46] deals with the problem of vanishing points detection. While this work tackles the problem of geometric relations encoded in homographies or the fundamental matrix.…”