Abstract
In this paper we show how to use blind source separation to estimate shape from polarised images. We propose a new method which does not require prior knowledge of the polariser angles. The two key ideas underpinning the approach are to use weighted Singular Value Decomposition(SVD) to estimate the polariser angles, and to use a mutual information criterion function to optimise the weights. We calculate the surface normal information using Fresnel equation, and iteratively update the values of weighting matrix and refractive index to a recover surface shape. We show that the proposed method is capable of calculating robust shape information compared with alternative approaches based on the same inputs. Moreover, the method can be applied when using uncalibrated polarisation filters. This is the case when the the subject is difficult to stabilse during image capture.
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References
Atkinson, G., Hancock, E.: Recovery of surface orientation from diffuse polarization. IEEE Transactions on Image Processing 15(6), 1653–1664 (2006)
Atkinson, G., Hancock, E.: Shape estimation using polarization and shading from two views. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(11), 2001–2017 (2007)
Bronstein, A., Bronstein, M., Zibulevsky, M., Zeevi, Y.: Sparse ICA for blind separation of transmitted and reflected images. International Journal of Imaging Systems and Technology 15(1), 84–91 (2005)
Farid, H., Adelson, E.: Separating reflections and lighting using independent components analysis. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 262–267 (1999)
Kisilev, P., Zibulevsky, M., Zeevi, Y., Pearlmutter, B.: Multiscal framework for blind source separation. Journal of Machine Learning Research 4, 1339–1363 (2004)
Miyazaki, D., Kagesawa, M., Ikeuchi, K.: Transparent surface modeling from a pair of polarization images. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(1), 73–82 (2004)
Miyazaki, D., Tan, R., Hara, K., Ikeuchi, K.: Polarization-based inverse rendering from a single view. In: International Conference on Computer Vision, vol. 2, pp. 982–987 (2003)
Nayar, S., Fang, X., Boult, T.: Separation of Reflection Components using Color and Polarization. International Journal of Computer Vision 21(3), 163–186 (1997)
Rahmann, S., Canterakis, N.: Reconstruction of specular surfaces using polarization imaging. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 149–156 (2001)
Saman, G., Hancock, E.: Robust Computation of the Polarisation Image. In: International Conference on Pattern Recognition, pp. 971–974 (2010)
Umeyama, S., Godin, G.: Separation of Diffuse and Specular Components of Surface Reflection by Use of Polarization and Statistical Analysis of Images. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(5), 639–647 (2004)
Wolff, L.: Polarization vision: a new sensory approach to image understanding. Image and Vision computing 15(2), 81–93 (1997)
Wolff, L., Boult, T.: Constraining object features using a polarization reflectance model. IEEE Transactions on Pattern Analysis and Machine Intelligence 13(7), 635–657 (2002)
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© 2011 Springer-Verlag Berlin Heidelberg
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Zhang, L., Hancock, E.R. (2011). Robust Shape and Polarisation Estimation Using Blind Source Separation. In: Real, P., Diaz-Pernil, D., Molina-Abril, H., Berciano, A., Kropatsch, W. (eds) Computer Analysis of Images and Patterns. CAIP 2011. Lecture Notes in Computer Science, vol 6854. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23672-3_22
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DOI: https://doi.org/10.1007/978-3-642-23672-3_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23671-6
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