Abstract
In current active contour image segmentation methods, the number of regions is assumed to be known beforehand. It is related directly to a fixed number of active curves. How to allow it to vary is an important question which has been generally avoided. This study investigates a segmentation prior related to regions area to allow the number of regions to vary automatically during curve evolution, thereby optimizing the objective functional implicitly with respect to the number of regions. The obtained evolution equations show that the proposed prior can cause some curves to disappear while other curves expand, thereby leading to a region merging by curve evolution, although not in the sense of the traditional one-step merging of two regions. We give a statistical interpretation to the coefficient of this prior to balance its effect systematically against the other functional terms. We show the validity and efficiency of the method by testing on real images of intensity. A comparison demonstrates the advantages of the proposed method over the region-competition algorithm in regard to the optimal number of regions and computational load.
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References
Rousson, M., Paragios, N.: Prior Knowledge, Level Set Representations and Visual Grouping. Int. J. of Computer Vision 76, 231–243 (2008)
Cremers, D., Rousson, M., Deriche, R.: A Review of Statistical Approaches to Level Set Segmentation: Integrating Color, Texture, Motion and Shape. Int. J. of Computer Vision 62, 249–265 (2007)
Ben Ayed, I., Hennane, N., Mitiche, A.: Unsupervised Variational Image Segmentation/Classification using a Weibull Observation Model. IEEE Trans. on Image Processing 15, 3431–3439 (2006)
Ben Ayed, I., Mitiche, A., Belhadj, Z.: Polarimetric Image Segmentation via Maximum Likelihood Approximation and Efficient Multiphase Level Sets. IEEE Trans. on Pattern Anal. and Machine Intell. 28, 1493–1500 (2006)
Mansouri, A.-R., Mitiche, A., Vazquez, C.: Multiregion competition: A Level Set extension of Region Competition to Multiple Region Image Partitioning. Computer Vision and Image Understanding 101(3), 137–150 (2006)
Vese, L.A., Chan, T.F.: A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model. Int. J. of Computer Vision 50, 271–293 (2002)
Chan, T.F., Vese, L.A.: Active Contours without Edges. IEEE Transactions on Image Processing 10, 266–277 (2001)
Samson, C., Blanc-Féraud, L., Aubert, G., Zerubia, J.: A Level Set Model for Image Classification. Int. J. of Computer Vision 40, 187–197 (2000)
Brox, T., Weickert, J.: Level Set Segmentation With Multiple Regions. IEEE Trans. on Image Processing 15, 3213–3218 (2006)
Kadir, T., Brady, M.: Unsupervised non-parametric region segmentation using level sets. In: Proc. Int. Conf. on Computer Vision, pp. 1267–1274 (2003)
Zhu, S.C., Yuille, A.: Region Competition: Unifying Snakes, Region Growing, and Bayes /MDL for Multiband Image Segmentation. IEEE Trans. on Pattern Anal. and Machine Intell. 18, 884–900 (1996)
Martin, P., Réfrégier, P., Goudail, F., Guérault, F.: Influence of the Noise Model on Level Set Active Contour Segmentation. IEEE Trans. on Pattern Anal. and Machine Intell. 26, 799–803 (2004)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley Interscience, Hoboken (2000)
Sethian, J.: Level Set Methods and Fast Marching Methods. Cambridge University Press, Cambridge (1999)
Nock, R., Nielsen, F.: Statistical Region Merging. IEEE Trans. on Pattern Anal. and Machine Intell. 26, 1452–1458 (2004)
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Ben Ayed, I., Mitiche, A. (2008). Merging Active Contours. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2008. Lecture Notes in Computer Science, vol 5359. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89646-3_18
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DOI: https://doi.org/10.1007/978-3-540-89646-3_18
Publisher Name: Springer, Berlin, Heidelberg
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