Display-camera calibration using eye reflections and geometry constraints

Nitschke, Christian; Nakazawa, Atsushi; Takemura, Haruo

doi:10.1016/j.cviu.2011.02.008

Cited by 43 publications

(53 citation statements)

References 66 publications

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“…Our algorithm equivalently provides a method for calibrating spherical mirror based catadioptric systems using a single photo of a checkerboard. Techniques for display-camera calibration [25] often utilize reflections from an eye by modeling them as spherical mirror reflection. We believe that our approach will be useful in several vision applications such as specular surface reconstruction, robot navigation, catadioptric imaging, display-camera calibration and multi-camera calibration without overlapping field of view.…”

Section: Discussionmentioning

confidence: 99%

“…For example, Francken et al [6] require several images of a moving sphere (≈ 10) along with the sphere boundary to be visible in all images for extrinsic calibration. Similarly, Nitschkea et al [25] used multiple eye reflections along with the cornea contour (limbus) information for display-camera calibration. In contrast, our approach does not require the sphere boundary to be visible and uses a single reflection.…”

Section: Related Workmentioning

confidence: 99%

“…In this paper, we consider the case where the camera does not view the 3D points directly. This is a natural setting when considering multiple cameras with non-overlapping field of view (FOV) [18], calibrating a camera with a display for humancomputer interaction [25] or when reconstructing specular/shiny objects requiring a calibrated camera and reference 3D points.…”

Section: Introductionmentioning

confidence: 99%

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Extrinsic Camera Calibration without a Direct View Using Spherical Mirror

Agrawal

2013

2013 IEEE International Conference on Computer Vision

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We consider the problem of estimating the extrinsic parameters (pose) of a camera with respect to a reference 3D object without a direct view. Since the camera does not view the object directly, previous approaches have utilized reflections in a planar mirror to solve this problem. However, a planar mirror based approach requires a minimum of three reflections and has degenerate configurations where estimation fails. In this paper, we show that the pose can be obtained using a single reflection in a spherical mirror of known radius. This makes our approach simpler and easier in practice. In addition, unlike planar mirrors, the spherical mirror based approach does not have any degenerate configurations, leading to a robust algorithm. While a planar mirror reflection results in a virtual perspective camera, a spherical mirror reflection results in a nonperspective axial camera. The axial nature of rays allows us to compute the axis (direction of sphere center) and few pose parameters in a linear fashion. We then derive an analytical solution to obtain the distance to the sphere center and remaining pose parameters and show that it corresponds to solving a 16th degree equation. We present comparisons with state-of-art method using planar mirrors and show that our approach recovers more accurate pose in the presence of noise. Extensive simulations and results on real data validate our algorithm. IEEE International Conference on Computer Vision (ICCV)This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Extrinsic Camera Calibration without a Direct View Using Spherical Mirror

Agrawal

2013

2013 IEEE International Conference on Computer Vision

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“…5 (b)). Although there have been several discussions as to whether the eyeball shape is more complex than the model and to whether or not its anatomic parameters vary among humans, several previous studies have shown that this simplified model is sufficiently accurate [19]. The cornea is modeled as a spherical cap that is cut off from the corneal sphere by the limbus plane.…”

Section: Tracking 3d Corneal Posementioning

confidence: 99%

Point of Gaze Estimation Using Corneal Surface Reflection and Omnidirectional Camera Image

Ogawa

Nakazawa

Nishida

2018

IEICE Trans. Inf. & Syst.

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Taishi OGAWA †a) , Atsushi NAKAZAWA †b) , Nonmembers, and Toyoaki NISHIDA †, † †c) , Member SUMMARYWe present a human point of gaze estimation system using corneal surface reflection and omnidirectional image taken by spherical panorama cameras, which becomes popular recent years. Our system enables to find where a user is looking at only from an eye image in a 360 • surrounding scene image, thus, does not need gaze mapping from partial scene images to a whole scene image that are necessary in conventional eye gaze tracking system. We first generate multiple perspective scene images from an omnidirectional (equirectangular) image and perform registration between the corneal reflection and perspective images using a corneal reflection-scene image registration technique. We then compute the point of gaze using a corneal imaging technique leveraged by a 3D eye model, and project the point to an omnidirectional image. The 3D eye pose is estimate by using the particle-filter-based tracking algorithm. In experiments, we evaluated the accuracy of the 3D eye pose estimation, robustness of registration and accuracy of PoG estimations using two indoor and five outdoor scenes, and found that gaze mapping error was 5.546 [deg] on average.

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“…), video game controllers. Therefore, implementations of face and gaze detection have received a great deal of attention in the recent literature [1][2][3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%