Non-uniform image coordinate system definition for panoramic imaging using a hybrid AI algorithm

Abstract

A panoramic image provides a wide-angle field of view representation of a scene up to 360-degree. 360-degree images do not conform to the central projection camera model. One of the important research areas is the calculation of the three-dimensional coordinates of objects from a single 360-degree image more accurately. It is possible to calculate three-dimensional coordinates from a panoramic image using the direct linear transformation. However, distortions in panoramic images created by combining images from multiple fisheye lenses, such as the Ladybug2 camera, are not uniform across the image. In this study, a method for defining a non-uniform image coordinate system is proposed to address this issue. To generate the elliptical coordinate system the singular value decomposition algorithm was applied to the cylindrical coordinate system. The adaptive neuro-fuzzy inference system (ANFIS) method was applied to the elliptical coordinates to obtain a gridded non-uniform coordinate system. Then, three-dimensional coordinates of the control points in the calibration room were calculated from non-uniform coordinates by applying the direct linear transformation method. Finally, the three-dimensional coordinate values calculated from the proposed method were compared with the obtained conventional direct linear transformation method. When the proposed method was compared with the conventional method, it was found that the L<inf>4</inf> parameter (488.82%) was calculated more accurately among the eleven direct linear transformation parameters. The results revealed that more accurate three-dimensional coordinate values were calculated with the proposed non-uniform coordinate system. © 2025 Elsevier B.V., All rights reserved.