Abstract
We present a Kalman filter framework for recovering depth from the time-varying optical flow fields generated by a camera translating over a scene by a known amount. Synthetic data made from ray traced cubical, cylinderal and spherical primitives are used in the optical flow calculation and allow a quantitative error analysis of the recovered depth.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Barron J. L., Jepson A.D. and Tsotsos J.K. (1990), “The Feasibility of Motion and Structure From Noisy Time-Varying Image Velocity Information”, Int. Journal of Computer Vision, 5:3, pp. 239–269.
Barron J. L. and Eagleson R. (1996), “Recursive Estimation of Time-Varying Motion and Structure Parameters”, Pattern Recognition, Vol. 29, No. 5, May, pp. 797–818.
Barron J. L. and Khurana M. (1997), Determining optical flow for large motions Using Parametric Models in a Hierarchical Framework, Vision Interface, Kelowna, B. C., May, pp 47–56.
Black M. J. and Anandan P. (1996), “The Robust Estimation of Multiple Motions: Parametric and Piecewise-Smooth Flow fields”, Computer Vision and Image Understanding, 63:1, pp. 75–104.
Faugeras O.D. and Lustman F. (1985), “Let Us Suppose that the World is Piecewise Planar”, 3 rd Intl. Symposium on Robotics, France, pp. 33–39
Gelb A. (1974), “Applied Optimal Estimation”, MIT Press.
Kalman, R. E. (1960), “A new approach to linear filtering and prediction problems”, Trans. of the ASME-Journal of Basic Engineering, pp 35–45.
Lucas, B. and Kanade, T. (1981), “An iterative image registration technique with an application to stereo vision”, Proc. DARPA IU Workshop, pp. 121–130.
Heel J. (1990), “Direct Dynamic Motion Vision”, Proc. IEEE Conf. Robotics and Automation, pp. 1142–1147.
Hung Y. S. and Ho H.T. (1999), “A Kalman Filter Approach to Direct Depth Estimation Incorporating Surface Structure”, IEEE PAMI, June, pp. 570–576.
Longuet-Higgins H.-C. and Prazdny K, (1980), “The Interpretation of a Moving Retinal Image”, Proc. R. Soc. Lond B 208, pp. 385–397.
Matthies L., Szeliski R. and Kanade T. (1989), “Kalman Filter-Based Algorithms for Estimating Depth from Image Sequences”, Int. J. Computer Vision 3:3, pp. 209–238.
Simoncelli E.P. (1994), “Design of multi-dimensional derivative filters”, IEEE Int. Conf. Image Processing, Vol. 1, pp 790–793.
Press W.H., Flannery B.P., Teukolsky S.A., and Vetterling W.T. (1992), “Numerical Recipes in C (2nd edition)”, Cambridge University Press, pp 671–675.
Xiong Y. and Shafer S. (1995), “Dense Structure from a Dense Optical Flow Sequence”, Int. Symposium on Computer Vision, Coral Gables, Florida, pp 1–6.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Barron, J., Ngai, W.K.J., Spies, H. (2003). Quantitative Depth Recovery from Time-Varying Optical Flow in a Kalman Filter Framework. In: Asano, T., Klette, R., Ronse, C. (eds) Geometry, Morphology, and Computational Imaging. Lecture Notes in Computer Science, vol 2616. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36586-9_22
Download citation
DOI: https://doi.org/10.1007/3-540-36586-9_22
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-00916-0
Online ISBN: 978-3-540-36586-0
eBook Packages: Springer Book Archive