Camera calibration from a single night sky image

Klaus, Andreas; Bauer, Joachim; Karner, Konrad; Elbischger, Pierre; Perko, Roland; Bischof, Horst

doi:10.1109/cvpr.2004.1315026

Cited by 21 publications

(18 citation statements)

References 5 publications

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“…A planar pattern viewed from at least three different orientations is used in [23]. Other calibration objects include: spheres [2,22], circles [12], surfaces of revolution [7], shadows [5] and even fixed stars in the night sky [14]. Most of these methods are based on the constraints provided by vanishing points along perpendicular directions.…”

Section: Related Workmentioning

confidence: 99%

Simple calibration of non-overlapping cameras with a mirror

Kumar

Ilie

Frahm

et al. 2008

2008 IEEE Conference on Computer Vision and Pattern Recognition

156

111

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Calibrating a network of cameras with non-overlapping views is an important and challenging problem in computer vision. In this paper, we present a novel technique for camera calibration using a planar mirror. We overcome the need for all cameras to see a common calibration object directly by allowing them to see it through a mirror. We use the fact that the mirrored views generate a family of mirrored camera poses that uniquely describe the real camera pose. Our method consists of the following two steps: (1) using standard calibration methods to find the internal and external parameters of a set of mirrored camera poses, (2) estimating the external parameters of the real cameras from their mirrored poses by formulating constraints between them. We demonstrate our method on real and synthetic data for camera clusters with small overlap between the views and non-overlapping views.

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Section: Related Workmentioning

confidence: 99%

Simple calibration of non-overlapping cameras with a mirror

Kumar

Ilie

Frahm

et al. 2008

2008 IEEE Conference on Computer Vision and Pattern Recognition

156

111

View full text Add to dashboard Cite

show abstract

“…The mathematical algorithm which describes the transformation of image coordinates into horizontal spherical coordinates or vice versa, including corrections for distortion is called a camera model. Camera models have been used widely for astronomical observations, mainly for dark sky imaging, e.g., for meteor trace analysis (Oberst et al, 2004), also for gravity wave analysis in mesospheric airglow images (Garcia et al, 1997), noctilucent cloud observations (Baumgarten et al, 2009), cloud mapping using calibration with stars and aircraft with known positions (Seiz et al, 2007;Shields et al, 2013), or for automatic identification of stars in digital images (Klaus et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

Contrail study with ground-based cameras

et al. 2013

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Abstract. Photogrammetric methods and analysis results for contrails observed with wide-angle cameras are described. Four cameras of two different types (view angle < 90 • or whole-sky imager) at the ground at various positions are used to track contrails and to derive their altitude, width, and horizontal speed. Camera models for both types are described to derive the observation angles for given image coordinates and their inverse. The models are calibrated with sightings of the Sun, the Moon and a few bright stars. The methods are applied and tested in a case study. Four persistent contrails crossing each other, together with a short-lived one, are observed with the cameras. Vertical and horizontal positions of the contrails are determined from the camera images to an accuracy of better than 230 m and horizontal speed to 0.2 m s −1 . With this information, the aircraft causing the contrails are identified by comparison to traffic waypoint data. The observations are compared with synthetic camera pictures of contrails simulated with the contrail prediction model CoCiP, a Lagrangian model using air traffic movement data and numerical weather prediction (NWP) data as input. The results provide tests for the NWP and contrail models. The cameras show spreading and thickening contrails, suggesting ice-supersaturation in the ambient air. The ice-supersaturated layer is found thicker and more humid in this case than predicted by the NWP model used. The simulated and observed contrail positions agree up to differences caused by uncertain wind data. The contrail widths, which depend on wake vortex spreading, ambient shear and turbulence, were partly wider than simulated.

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“…Only two parallel lights in each image are, however, necessary in the proposed method, thus allowing it to be applied more easily than these previous methods. Other methods used stars as distant objects [13], [14] and estimated both the intrinsic and extrinsic parameters using parallel lights. Instead of using a distant object, yet other methods used vanishing points for calibration.…”

Section: Introductionmentioning

confidence: 99%

Accurate calibration of intrinsic camera parameters by observing parallel light pairs

Sagawa

Yagi

2008

2008 IEEE International Conference on Robotics and Automation

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Abstract-This study describes a method of estimating the intrinsic parameters of a perspective camera. In previous calibration methods for perspective cameras, the intrinsic and extrinsic parameters are estimated simultaneously during calibration. Thus, the intrinsic parameters depend on the estimation of the extrinsic parameters, which is inconsistent with the fact that intrinsic parameters are independent of extrinsic ones. Moreover, in a situation where the extrinsic parameters are not used, only the intrinsic parameters need to be estimated. In this case, an intrinsic parameter, such as focal length, is not sufficiently robust to combat the image processing noise, that is absorbed by both parameter types, during calibration. We therefore propose a new method that allows the estimation of intrinsic parameters without estimating the extrinsic parameters. In order to calibrate the intrinsic parameters, the proposed method observes parallel light pairs that are projected on different points. This is accomplished by applying the constraint that the relative angle of two parallel rays is constant irrespective of where the rays are projected. This method focuses only on intrinsic parameters and the calibrations are sufficiently robust as demonstrated in this study. Moreover, our method can visualize the error of the calibrated result and the degeneracy of the input data.

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Camera calibration from a single night sky image

Cited by 21 publications

References 5 publications

Simple calibration of non-overlapping cameras with a mirror

Simple calibration of non-overlapping cameras with a mirror

Contrail study with ground-based cameras

Accurate calibration of intrinsic camera parameters by observing parallel light pairs

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