Abstract
Accurate and efficient correspondence matching between two rectified images is critical for stereo reconstruction. Essentially, correspondence matching co-registers the two rectified images subject to an epipolar constraint (i.e., registration is performed along the horizontal direction). Most algorithms are based on windowed block matching that optimizes cross-correlation or its variants (e.g., sum of squared differences, SSD) between two sub-images to generate a sparse disparity map. In this work, we utilize unrestricted optical flow for a full-field correspondence matching. Relative to surface point measurements sampled with a tracked stylus as ground-truth, we show that the point-to-surface distance from the flow-based method is comparable and often superior to that from the SSD algorithm (e.g., 1.0 mm vs. 1.2 mm, respectively) but with a substantial increase in computational efficiency (5–6 sec for a full field of 41 K vs. 20–30 sec for a sparse subset of 1 K sampling points, respectively). In addition, the flow-based stereovision offers ability for feature identification based on the full-field horizontal disparity map that is directly related to reconstruction pixel depth values, whereas the vertical disparity provides an assessment of the accuracy confidence level in stereo reconstruction, which are not available with SSD methods.
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
Roma, N., Santos-Victor, J., Tome, J.: A comparative analysis of cross-correlation matching algorithms using a pyramidal resolution approach. In: Christensen, H.I., Phillips, P.J. (eds.) Empirical Evaluation Methods in Computer Vision, pp. 117–142. World Scientific Press, Singapore (2002) ISBN 981-02-4953-5
Hu, X., Mordohai, P.: A Quantitative Evaluation of Confidence Measures for Stereo Vision. IEEE Transactions on Pattern Analysis and Machine Intelligence 34(11), 2121–2133 (2012), doi:10.1109/TPAMI.2012.46
Hatzitheodorou, M., Karabassi, E.A., Papaioannou, G., Boehm, A., Theoharis, T.: Stereo Matching Using Optic Flow. Real-Time Imaging 6, 251–266 (2000)
Sun, H., Lunn, K.E., Farid, H., Wu, Z., Roberts, D.W., Hartov, A., Paulsen, K.D.: Stereopsis-guided brain shift compensation. IEEE Trans. Med. Imag. 24(8), 1039–1052 (2005)
Brox, T., Bruhn, A., Papenberg, N., Weickert, J.: High accuracy optical flow estimation based on a theory for warping. In: Pajdla, T., Matas, J. (eds.) ECCV 2004. LNCS, vol. 3024, pp. 25–36. Springer, Heidelberg (2004)
Black, M.J., Anandan, P.: The robust estimation of multiple motions: parametric and piecewise-smooth flow fields. Comput. Vision and Image Understanding 63(1), 75–104 (1996)
Liu, C.: Beyond Pixels: Exploring New Representations and Applications for Motion Analysis. Doctoral Thesis. Massachusetts Institute of Technology (May 2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer International Publishing Switzerland
About this paper
Cite this paper
Ji, S., Fan, X., Roberts, D.W., Hartov, A., Paulsen, K.D. (2013). Flow-Based Correspondence Matching in Stereovision. In: Wu, G., Zhang, D., Shen, D., Yan, P., Suzuki, K., Wang, F. (eds) Machine Learning in Medical Imaging. MLMI 2013. Lecture Notes in Computer Science, vol 8184. Springer, Cham. https://doi.org/10.1007/978-3-319-02267-3_14
Download citation
DOI: https://doi.org/10.1007/978-3-319-02267-3_14
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-02266-6
Online ISBN: 978-3-319-02267-3
eBook Packages: Computer ScienceComputer Science (R0)