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Robust Active Appearance Model Matching

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 3565))

Abstract

A novel robust active appearance model (AAM) matching algorithm is presented. The method consists of two main stages. First, initial residuals are clustered by a non parametric mean shift mode detection step. Second, modes without gross outliers are selected using an objective function. Robustness of the matching procedure is demonstrated on a variety of examples with different noise conditions. The proposed algorithm outperformed the conventional AAM matching on images with gross disturbances and can tolerate up to 40% of disturbed data.

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References

  1. Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active appearance models. In: Burkhardt, H., Neumann, B. (eds.) Proc. European Conference on Computer Vi- sion, vol. 2, pp. 484–498. Springer, Heidelberg (1998)

    Google Scholar 

  2. Cootes, T.F., Taylor, C.J.: Statistical models of appearance for com- puter vision. Technical report, Division of Imaging Science and Biomedical Engineering, University of Manchester (2004), available at http://www.isbe.man.ac.uk/~bim/refs.html

  3. Mitchell, S.C., Lelieveldt, B.P.F., van der Geest, R., Schaap, J., Reiber, J.H.C., Sonka, M.: Segmentation of cardiac MR images: An active appearance model approach. In: Proceedings SPIE, Medical Imaging - Image Processing, vol. 3979, pp. 224–234. SPIE, Bellingham (2000)

    Google Scholar 

  4. Stegmann, M.B.: Analysis of 4D cardiac magnetic resonance images. Journal of The Danish Optical Society, DOPS-NYT, 38–39 (2001)

    Google Scholar 

  5. Mitchell, S.C., Bosch, J.G., Lelieveldt, B.P.F., van der Geest, R.J., Reiber, J.H.C., Sonka, M.: 3-D Active Appearance Models: Segmentation of cardiac MR and ultrasound images.  21, 1167–1178 (2002)

    Google Scholar 

  6. Bosch, H.G., Mitchell, S.C., Lelieveldt, B.P.F., Sonka, M., Nijland, F., Reiber, J.H.C.: Feasibility of fully automated border detection on stress echocardiograms by active appearance models (abstract). Circulation 102(18)(Suppl II), II–633 (2000)

    Google Scholar 

  7. Cootes, T.F., Beeston, C., Edwards, G.J., Taylor, C.J.: A unified framework for atlas matching using active appearance models. In: Proc. Int. Conf. on Image Processing in Medical Imaging, pp. 322–333. Springer, Heidelberg (1999)

    Google Scholar 

  8. Beichel, R., Gotschuli, G., Sorantin, E., Leberl, F., Sonka, M.: Diaphragm dome surface segmentation in CT data sets: A 3D active appearance model approach. In: Sonka, M., Fitzpatrick, J.M. (eds.) SPIE: Medical Imaging: Image Processing, vol. 4684, pp. 475–484 (2002)

    Google Scholar 

  9. Edwards, G.J., Cootes, T.F., Taylor, C.J.: Advances in active appearance models. In: Proc. International Conference on Computer Vision, pp. 137–142 (1999)

    Google Scholar 

  10. Stegmann, M.B., Fisker, R., Ersbøll, B.K.: Extending and applying active ap- pearance models for automated, high precision segmentation in different image modalities. In: Austvoll, I. (ed.) Proc. 12th Scandinavian Conference on Image Analysis - SCIA 2001, Bergen, Norway, Stavanger, Norway, NOBIM, pp. 90–97 (2001)

    Google Scholar 

  11. Gross, R., Matthews, I., Baker, S.: Constructing and fitting active appearance models with occlusion. In: Proceedings of the IEEE Workshop on Face Processing in Video (2004)

    Google Scholar 

  12. Fukunaga, K., Hostetler, L.: The estimation of the gradient of a density function, with applications. pattern recognition 21, 32–40 (1975)

    MATH  MathSciNet  Google Scholar 

  13. Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis.  24, 603–619 (2002)

    Google Scholar 

  14. Thodberg, H.H.: Shape modelling and analysis minimum description length shape and appearance models. In: Information Processing in Medical Imaging - IPMI 2003, pp. 51–62. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  15. Tukey, J.W.: Exploratory Data Analysis. Addison-Wesley, Reading (1977)

    MATH  Google Scholar 

  16. Langs, G., Peloschek, P., Bischof, H.: ASM driven snakes in rheumatoid arthritis assessment. In: Bigun, J., Gustavsson, T. (eds.) SCIA 2003. LNCS, vol. 2749, pp. 454–461. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

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© 2005 Springer-Verlag Berlin Heidelberg

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Beichel, R., Bischof, H., Leberl, F., Sonka, M. (2005). Robust Active Appearance Model Matching. In: Christensen, G.E., Sonka, M. (eds) Information Processing in Medical Imaging. IPMI 2005. Lecture Notes in Computer Science, vol 3565. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11505730_10

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  • DOI: https://doi.org/10.1007/11505730_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-26545-0

  • Online ISBN: 978-3-540-31676-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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