Optimal estimation of contour properties by cross-validated regularization

The problem of estimating the properties of smooth, continuous contours from discrete, noisy samples is used as vehicle to demonstrate the robustness of cross-validated regularization applied to a vision problem. A method for estimation of contour properties based on smoothing spline approximations is presented. Generalized cross-validation is to devise an automatic algorithm for finding the optimal value of the smoothing (regularization) parameter from the data. The cross-validated smoothing splines are then used to obtain optimal estimates of the derivatives of quantized contours. Experimental results are presented which demonstrate the robustness of the method applied to the estimation of curvature of quantized contours under variable scale, rotation, and partial occlusion. These results suggest the application of generalized cross-validation to other computer-vision algorithms involving regularization