Abstract
We propose a probabilistic segmentation scheme, which is widely applicable to some extend. Besides the segmentation itself our model incorporates object specific shading. Dependent upon application, the latter is interpreted either as a perturbation or as meaningful object characteristic. We discuss the recognition task for segmentation, learning tasks for parameter estimation as well as different formulations of shading estimation tasks.
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References
Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. In: Proc. of the 7. Intl. Conf. on Computer Vision, vol. 1, pp. 377–384 (1999)
Cremers, D.: A multiphase level set framework for variational motion segmentation. In: Griffin, L.D., Lillholm, M. (eds.) Scale-Space Methods in Computer Vision (Isle of Skye), vol. 2695, pp. 599–614. Springer, Heidelberg (2003)
Cremers, D., Rousson, M., Deriche, R.: A review of statistical approaches to level set segmentation: integrating color, texture, motion and shape. International Journal of Computer Vision 72(2), 195–215 (2007)
Geman, S., Geman, D.: Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence 6(6), 721–741 (1984)
Kolmogorov, V.: Convergent tree-reweighted message passing for energy minimization. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) 28(10), 1568–1583 (2006)
Kolmogorov, V., Zabih, R.: What energy functions can be minimized via graph cuts? In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2350, pp. 65–81. Springer, Heidelberg (2002)
Micusik, B., Pajdla, T.: Multi-label image segmentation via max-sum solver. In: Proc. of Computer Vision and Pattern Recognition (2007)
Schlesinger, D., Flach, B.: Transforming an arbitrary minsum problem into a binary one, Tech. report, Dresden University of Technology, TUD-FI06-01 (April 2005), http://www.bv.inf.tu-dresden.de/~ds24/tr_kto2.pdf
Wainwright, M.J., Jaakkola, T.S., Willsky, A.S.: Tree-reweighted belief propagation algorithms and approximate ML estimation via pseudo-moment matching. In: Workshop on Artificial Intelligence and Statistics (January 2003)
Wainwright, M.J., Jaakkola, T.S., Willsky, A.S.: A new class of upper bounds on the log partition function. IEEE Transactions on Information Theory 51(7), 2313–2335 (2005)
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Schlesinger, D., Flach, B. (2008). A Probabilistic Segmentation Scheme. In: Rigoll, G. (eds) Pattern Recognition. DAGM 2008. Lecture Notes in Computer Science, vol 5096. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69321-5_19
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DOI: https://doi.org/10.1007/978-3-540-69321-5_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69320-8
Online ISBN: 978-3-540-69321-5
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