Convergence analysis of active contours
Image and Vision Computing
Image Segmentation and Selective Smoothing Based on Variational Framework
Journal of Signal Processing Systems
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Active contours, or so-called snakes, require some parameters to determine the form of the external force or to adjust the tradeoff between the internal forces and the external forces acting on the active contour. However, the optimal values of these parameters cannot be easily identified in a general sense. The usual way to find these required parameters is to run the algorithm several times for a different set of parameters, until a satisfactory performance is obtained. Our nonparametric formulation translates the problem of seeking these unknown parameters into the problem of seeking a good edge probability density estimate. Density estimation is a well-researched field, and our nonparametric formulation allows using well-known concepts of density estimation to get rid of the exhaustive parameter search. Indeed, with the use of kernel density estimation these parameters can be defined locally, whereas, in the original snake approach, all the shape parameters are defined globally. We tested the proposed method on synthetic and real images and obtained comparatively better results.