Optimal estimation of contour properties by Cross-Validated regularization.

This paper presents a method for the estimation of contour properties based on smoothing spline approximation. Generalized Cross-Validation is employed in order 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 resented 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 regularizations.