Vanishing point detection using the teaching learning‐based optimisation algorithm. Issue 11 (6th August 2020)

• Record Type: Journal Article
• Authors: López‐Martinez, Alan; Cuevas, Francisco J.
• Is Part Of: IET image processing. Volume 14:Issue 11(2020)
• Page Start: 2487
• Page End: 2494
• Publication Date: 2020-08-06
• Subjects: image segmentation -- pattern clustering -- search problems -- optimisation -- computer vision -- edge detection; TLBO algorithm -- line segment detection -- optimal vanishing point -- nonorthogonal vanishing points -- vanishing point detection -- teaching learning‐based optimisation algorithm -- computer vision -- image vanishing points -- heuristic approach -- metaheuristic search -- population‐based method -- metaheuristic technique -- teaching–learning process -- computational cost; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2019.0516
• Languages: English
• ISSNs: 1751-9659

Abstract

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°.