BAYESIAN SEPARATION OF DOCUMENT IMAGES WITH HIDDEN MARKOV MODEL

Feng Su, Ali Mohammad-Djafari

2007

Abstract

this paper we consider the problem of separating noisy instantaneous linear mixtures of document images in the Bayesian framework. The source image is modeled hierarchically by a latent labeling process representing the common classifications of document objects among different color channels and the intensity process of pixels given the class labels. A Potts Markov random field is used to model regional regularity of the classification labels inside object regions. Local dependency between neighboring pixels can also be accounted by smoothness constraint on their intensities. Within the Bayesian approach, all unknowns including the source, the classification, the mixing coefficients and the distribution parameters of these variables are estimated from their posterior laws. The corresponding Bayesian computations are done by MCMC sampling algorithm. Results from experiments on synthetic and real image mixtures are presented to illustrate the performance of the proposed method.

References

  1. Almeida, L. B. (2005). Separating a real-life nonlinear image mixture. Journal of Machine Learning Research, 6:1199-1232.
  2. Bronstein, A. M., Bronstein, M. M., Zibulevsky, M., and Zeevi, Y. Y. (2005). Sparse ICA for blind separation of transmitted and reflected images. Intl. Journal of Imaging Science and Technology (IJIST), 15:84-91.
  3. Calhoun, V. D. and Adali, T. (2006). Unmixing fMRI with independent component analysis. IEEE Engineering in Medicine and Biology Magazine, 25(2):79-90.
  4. Castella, M. and Pesquet, J.-C. (2004). An iterative blind source separation method for convolutive mixtures of images. Lecture Notes in Computer Science, 3195/2004:922-929.
  5. Drira, F. (2006). Towards restoring historic documents degraded over time. In Second International Conference on Document Image Analysis for Libraries, pages 350-357.
  6. Feron, O. and Mohammad-Djafari, A. (2005). Image fusion and unsupervised joint segmentation using HMM and MCMC algorithms. Journal of Electronic Imaging, 14(2).
  7. Harmeling, S. (2003). Kernel-based nonlinear blind source separation. Neural Computation, 15(5):1089-1124.
  8. Hyvarinen, A. (1999). Fast and robust fixed-point algorithms for independent component analysis. IEEE Transactions on Neural Networks, 10(3):626-634.
  9. Hyvarinen, A., Karhunen, J., and Oja, E. (2001). Independent Component Analysis. John Wiley & Sons, Inc., New York.
  10. Macias-Macias, M., Garcia-Orellana, C. J., GonzalezVelasco, H., and Gallardo-Caballero, R. (2003). Independent component analysis for cloud screening of meteosat images. In International Work-conference on Artificial and Natural Neural Networks (LNCS 2687/2003), volume 2687, pages 551-558.
  11. Parra, L., Spence, C., Ziehe, A., Muller, K.-R., and Sajda, P. (2000). Unmixing hyperspectral data. In Advances in Neural Information Processing Systems, volume 12, pages 942-948.
  12. Sharma, G. (2001). Show-through cancellation in scans of duplex printed documents. IEEE Transactions on Image Processing, 10(5):736-754.
  13. Snoussi, H. and Calhoun, V. D. (2005). Bayesian blind source separation for brain imaging. In IEEE International Conference on Image Processing (ICIP) 2005, volume 3, pages 581-584.
  14. Snoussi, H. and Mohammad-Djafari, A. (2004). Fast joint separation and segmentation of mixed images. Journal of Electronic Imaging, 13:349-361.
  15. Tonazzini, A., Bedini, L., and Salerno, E. (2006). A markov model for blind image separation by a mean-field EM algorithm. IEEE Transactions on Image Processing, 15(2):473-482.
  16. Tonazzini, A., Salerno, E., Mochi, M., and Bedini, L. (2004). Blind source separation techniques for detecting hidden texts and textures in document images. In ICIAR 2004, LNCS 3212, pages 241-248, Berlin. Springer-Verlag.
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Paper Citation


in Harvard Style

Su F. and Mohammad-Djafari A. (2007). BAYESIAN SEPARATION OF DOCUMENT IMAGES WITH HIDDEN MARKOV MODEL . In Proceedings of the Second International Conference on Computer Vision Theory and Applications - Volume 3: Bayesian Approach for Inverse Problems in Computer Vision, (VISAPP 2007) ISBN 978-972-8865-75-7, pages 151-156. DOI: 10.5220/0002064601510156


in Bibtex Style

@conference{bayesian approach for inverse problems in computer vision07,
author={Feng Su and Ali Mohammad-Djafari},
title={BAYESIAN SEPARATION OF DOCUMENT IMAGES WITH HIDDEN MARKOV MODEL},
booktitle={Proceedings of the Second International Conference on Computer Vision Theory and Applications - Volume 3: Bayesian Approach for Inverse Problems in Computer Vision, (VISAPP 2007)},
year={2007},
pages={151-156},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002064601510156},
isbn={978-972-8865-75-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Second International Conference on Computer Vision Theory and Applications - Volume 3: Bayesian Approach for Inverse Problems in Computer Vision, (VISAPP 2007)
TI - BAYESIAN SEPARATION OF DOCUMENT IMAGES WITH HIDDEN MARKOV MODEL
SN - 978-972-8865-75-7
AU - Su F.
AU - Mohammad-Djafari A.
PY - 2007
SP - 151
EP - 156
DO - 10.5220/0002064601510156