Abstract
This paper describes a novel method for shape detection and image segmentation. The proposed method combines statistical shape models and active contours implemented in a level set framework. The shape detection is achieved by minimizing the Gibbs energy of the posterior probability function. The statistical shape model is built as a result of a learning process based on nonparametric probability estimation in a PCA reduced feature space formed by the Legendre moments of training silhouette images. The proposed energy is minimized by iteratively evolving an implicit active contour in the image space and subsequent constrained optimization of the evolved shape in the reduced shape feature space. Experimental results are also presented to show that the proposed method has very robust performances for images with a large amount of noise.
This work was supported by the Engineering and Physical Sciences Research Council [grant number EP/D077540/1].
This is a preview of subscription content, log in via an institution to check access.
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
Houhou, N., Lemkaddem, A., Duay, V., Allal, Abdelkarim, Thiran, J.-P.: Shape prior based on statistical MAP for active contour segmentation. In: ICIP (2008)
Kim, J., Çetin, M., Willsky, A.S.: Nonparametric shape priors for active contour-based image segmentation. Signal Process. 87(12), 3021–3044 (2007)
Lecellier, F., Jehan-Besson, S., Fadili, J., Aubert, G., Revenu, M., Saloux, E.: Region-based active contour with noise and shape priors. In: ICIP, pp. 1649–1652 (2006)
Erdem, E., Tari, S., Vese, L.A.: Segmentation using the edge strength function as a shape prior within a local deformation mode. In: ICIP, pp. 2989–2992 (2009)
Tsai, A., Yezzi, A., Wells, W.M., Tempany, C., Tucker, D., Fan, A., Grimson, W., Willsky, A.: A shape-based approach to the segmentation of medical imagery using level sets. IEEE Trans. Med. Imaging 22(2), 137–154 (2003)
Fussenegger, M., Roth, P., Bischof, H., Deriche, R., Pinz, A.: A level set framework using a new incremental, robust Active Shape Model for object segmentation and tracking. Image Vision Comput. 27(8), 1157–1168 (2009)
Cremers, D., Osher, S.J., Soatto, S.: Kernel density estimation and intrinsic alignment for shape priors in level set segmentation. Int. J. Comput. Vision 69(3), 335–351 (2006)
Rousson, M., Cremers, D.: Efficient kernel density estimation of shape and intensity priors for level set segmentation. In: Duncan, J.S., Gerig, G. (eds.) MICCAI 2005. LNCS, vol. 3750, pp. 757–764. Springer, Heidelberg (2005)
Rousson, M., Paragios, N.: Prior knowledge, level set representation and visual grouping. Int. J. Comput. Vision 76(3), 231–243 (2008)
Foulonneau, A., Charbonnier, P., Heitz, F.: Multi-reference shape priors for active contours. Int. J. Comput. Vision 81(1), 68–81 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhang, Y., Matuszewski, B.J., Histace, A., Precioso, F. (2011). Statistical Shape Model of Legendre Moments with Active Contour Evolution for Shape Detection and Segmentation. 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_7
Download citation
DOI: https://doi.org/10.1007/978-3-642-23672-3_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23671-6
Online ISBN: 978-3-642-23672-3
eBook Packages: Computer ScienceComputer Science (R0)