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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Compared with prime lenses, zoom lenses have inherent advantages in terms of operationalflexibility. Zoom lens camera systems have therefore been extensively adopted in computer visionwhere precise measurement is not the primary objective. However, the variation of intrinsiccamera parameters with respect to zoom lens settings poses a series of calibration challenges thathave inhibited widespread use in close-range photogrammetry. A flexible zoom lens calibrationmethodology is therefore proposed in this study, developed with the aim of simplifying the calibrationprocess and promoting practical photogrammetric application. A zoom-dependent camera modelthat incorporates empirical zoom-related intrinsic parameters into the collinearity condition equationsis developed. Coefficients of intrinsic parameters are solved in a single adjustment based on thiszoom lens camera model. To validate the approach, experiments on both optical- and digital-zoomlens cameras were conducted using a planar board with evenly distributed circular targets. Zoom lenscalibration was performed with images taken at four different zoom settings spread throughout thezoom range of a lens. Photogrammetric accuracies achieved through both mono-focal and multi-focaltriangulations were evaluated after calibration. The relative accuracies for mono-focal triangulationsranged from 1: 6300 to 1: 18,400 for the two cameras studied, whereas the multi-focal triangulationaccuracies ranged from 1: 11,300 to 1: 16,200. In order to demonstrate the applicability of the approach,calibrated zoom lens imagery was used to render a laser-scanned point cloud of a building façade.Considered alongside experimental results, the successful application demonstrates the feasibilityof the proposed calibration method, thereby facilitating the adoption of zoom lens cameras in closerange photogrammetry for a wide range of scientific and practical applications.
Author(s): Wang Z, Mills J, Xiao W, Huang R, Zheng S, Li Z
Publication type: Article
Publication status: Published
Journal: Remote Sensing
Online publication date: 06/03/2017
Acceptance date: 02/03/2017
Date deposited: 08/03/2017
ISSN (electronic): 2072-4292
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