Abstract
In this paper, we introduce an unsupervised contour estimation strategy based on parametrically deformable models. The problem is formulated in a (statistical) parameter estimation framework with the parameters of both the contour the and observation model (the likelihood function) being considered unknown. Although other choices could fit in our formulation, we focus on Fourier and B-spline contour descriptors. To estimate the optimal parametrization order (e.g., the number of Fourier coefficients) we adopt the minimum description length (MDL) principle. The result is a parametrically deformable contour with an adaptive degree of smoothness and which also autonomously estimates the observation model parameters.
The work described in this paper was partially supported by the NATO Collaborative Research Grant #CRG 960010.
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Figueiredo, M.A.T., Leitão, J.M.N., Jain, A.K. (1997). Adaptive parametrically deformable contours. In: Pelillo, M., Hancock, E.R. (eds) Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 1997. Lecture Notes in Computer Science, vol 1223. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-62909-2_71
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DOI: https://doi.org/10.1007/3-540-62909-2_71
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