Estimating the Fundamental Matrix Without Point Correspondences WithApplication to Transmission Imaging
Würfl T, Aichert A, Maass N, Dennerlein F, Maier A (2019)
Publication Language: English
Publication Type: Conference contribution, Conference Contribution
Publication year: 2019
Pages Range: 1072 - 1081
Conference Proceedings Title: Proceedings of the IEEE International Conference on Computer Vision
Event location: Seoul, Südkorea
Abstract
We present a general method to estimate the fundamental matrix from a pair of images under perspective projection without the need for image point correspondences. Our method is particularly well-suited for transmission imaging, where state-of-the-art feature detection and matching approaches generally do not perform well. Estimation of the fundamental matrix plays a central role in auto-calibration methods for reflection imaging. Such methods are currently not applicable to transmission imaging. Furthermore, our method extends an existing technique proposed for reflection imaging which potentially avoids the outlier-prone feature matching step from an orthographic projection model to a perspective model.
Our method exploits the idea that under a linear attenuation model line integrals along corresponding epipolar lines are equal if we compute their derivatives in orthogonal direction to their common epipolar plane.
We use the fundamental matrix to parametrize this equality.
Our method estimates the matrix by formulating a non-convex optimization problem, minimizing an error in our measurement of this equality.
We believe this technique will enable the application of the large body of work on image-based camera pose estimation to transmission imaging leading to more accurate and more general motion compensation and auto-calibration algorithms, particularly in medical X-ray and Computed Tomography imaging.
Authors with CRIS profile
Tobias Würfl
Lehrstuhl für Informatik 5 (Mustererkennung)
André Aichert
Lehrstuhl für Informatik 14 (Bild- und Sprachverarbeitung)
Andreas Maier
Lehrstuhl für Informatik 5 (Mustererkennung)
Involved external institutions
Germany (DE)How to cite
APA:
Würfl, T., Aichert, A., Maass, N., Dennerlein, F., & Maier, A. (2019). Estimating the Fundamental Matrix Without Point Correspondences WithApplication to Transmission Imaging. In Proceedings of the IEEE International Conference on Computer Vision (pp. 1072 - 1081). Seoul, Südkorea, KR.
MLA:
Würfl, Tobias, et al. "Estimating the Fundamental Matrix Without Point Correspondences WithApplication to Transmission Imaging." Proceedings of the International Conference on Computer Vision 2019, Seoul, Südkorea 2019. 1072 - 1081.
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