Abstract
Well known estimation techniques in computational geometry usually deal only with single geometric entities as unknown parameters and do not account for constrained observations within the estimation.
The estimation model proposed in this paper is much more general, as it can handle multiple homogeneous vectors as well as multiple constraints. Furthermore, it allows the consistent handling of arbitrary covariance matrices for the observed and the estimated entities. The major novelty is the proper handling of singular observation covariance matrices made possible by additional constraints within the estimation. These properties are of special interest for instance in the calculus of algebraic projective geometry, where singular covariance matrices arise naturally from the non-minimal parameterizations of the entities.
The validity of the proposed adjustment model will be demonstrated by the estimation of a fundamental matrix from synthetic data and compared to heteroscedastic regression [1], which is considered as state-of-the-art estimator for this task. As the latter is unable to simultaneously estimate multiple entities, we will also demonstrate the usefulness and the feasibility of our approach by the constrained estimation of three vanishing points from observed uncertain image line segments.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Matei, B., Meer, P.: A General Method for Errors–in–Variables Problems in Computer Vision. In: Computer Vision and Pattern Recognition Conference, vol. II, pp. 18–25. IEEE, Los Alamitos (2000)
Heuel, S.: Uncertain Projective Geometry. LNCS, vol. 3008. Springer, Heidelberg (2004)
Utcke, S.: Grouping based on Projective Geometry Constraints and Uncertainty. In: Ahuja, N., Desai, U. (eds.) Proceedings of the Sixth International Conference on Computer Vision, Bombay, India, January 4-7, 1998, pp. 739–746. Narosa Publishing House (1998)
Criminisi, A.: Accurate Visual Metrology from Single and Multiple Uncalibrated Images. Distinguished Dissertations. Springer, Heidelberg (2001)
Clarke, J.C.: Modelling Uncertainty: A Primer. Technical Report 2161/98, Department of Engineering Science, University of Oxford (1998)
Chojnacki, W., Brooks, M.J., van den Hengel, A.: Rationalising the Renormalisation Method of Kanatani. Journal of Mathematical Imaging and Vision 14, 21–38 (2001)
Kanatani, K.: Statistical Analysis of Geometric Computation. CVGIP: Image Understanding 59(3), 286–306 (1994)
Meidow, J., Beder, C., Förstner, W.: Reasoning with Uncertain Points, Straight Lines, and Straight Line Segments in 2D. ISPRS Journal of Photogrammetry and Remote Sensing 64(2), 125–139 (2009)
Koch, K.R.: Parameter Estimation and Hypothesis Testing in Linear Models, 2nd edn. Springer, Berlin (1999)
Huber, P.J.: Robust Statistics. J. Wiley, New York (1981)
Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press, Cambridge (2000)
Georgescu, B.: Software for HEIV based estimation, binary version. Center of Advanced Information Processing, Robust Image Understanding Laboratory, Rutgers University, http://www.caip.rutgers.edu/riul/research/code/heiv/ (2002)
Fischler, M.A., Bolles, R.C.: Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography. Communications of the Association for Computing Machinery 24(6), 381–395 (1981)
Förstner, W., Gülch, E.: A Fast Operator for Detection and Precise Location of Distinct Points, Corners and Circular Features. In: Proceedings of the ISPRS Intercommission Conference on Fast Processing of Photogrammetric Data, Interlaken, June 1987, pp. 281–305 (1987)
Collins, R.T., Weiss, R.S.: Vanishing Point Calculation as a Statistical Inference on the Unit Sphere. In: International Conference on Computer Vision, December 1990, pp. 400–403 (1990)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Meidow, J., Förstner, W., Beder, C. (2009). Optimal Parameter Estimation with Homogeneous Entities and Arbitrary Constraints. In: Denzler, J., Notni, G., Süße, H. (eds) Pattern Recognition. DAGM 2009. Lecture Notes in Computer Science, vol 5748. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03798-6_30
Download citation
DOI: https://doi.org/10.1007/978-3-642-03798-6_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03797-9
Online ISBN: 978-3-642-03798-6
eBook Packages: Computer ScienceComputer Science (R0)