This paper addresses the estimation of 2D object boundary from noisy data, using deformable contours. First, it discusses the relationship between deformable contours and other Pattern Recognition algorithms (e.g., Kohonen maps, mean shift, fuzzy c-means) and derives a unified framework which allows a joint formulation for a wide set of methods. Afterwords, the paper addresses the estimation of deformable curves in cluttered images, assuming that there is a large number of outlier features detected in the image. The paper presents two robust algorithms: the adaptive snake for static objects and a robust tracker (S-PDAF) for moving objects in video sequences. The advantages of both algorithms with respect to classic methods are illustrated by examples.
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Marques, J.S., Nascimento, J.C., Abrantes, A.J., Silveira, M. (2009). Robust Shape Estimation with Deformable Models. In: Tavares, J.M.R.S., Jorge, R.M.N. (eds) Advances in Computational Vision and Medical Image Processing. Computational Methods in Applied Sciences, vol 13. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-9086-8_3
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