Vanishing point detection using the teaching learning-based optimisation algorithm

• Published in: IET image processing Vol. 14; no. 11; pp. 2487 - 2494
• Main Authors: López-Martinez, Alan, Cuevas, Francisco J
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 18.09.2020; Wiley
• Subjects: computational cost; computer vision; edge detection; heuristic approach; image segmentation; image vanishing points; line segment detection; metaheuristic search; metaheuristic technique; nonorthogonal vanishing points; optimal vanishing point; optimisation; pattern clustering; population‐based method; Research Article; search problems; teaching learning‐based optimisation algorithm; teaching–learning process; TLBO algorithm; vanishing point detection; teaching–learning process; metaheuristic technique; computational cost; population-based method; metaheuristic search; vanishing point detection; teaching learning-based optimisation algorithm; optimisation; pattern clustering; image segmentation; image vanishing points; computer vision; optimal vanishing point; line segment detection; nonorthogonal vanishing points; edge detection; search problems; TLBO algorithm; heuristic approach
• ISSN: 1751-9659; 1751-9667
• DOI: 10.1049/iet-ipr.2019.0516

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°.