Optimal estimation of contour properties by cross-validatedregularization
Shahraray, B.
Anderson, D.J.
AT&T Bell Lab., Holmdel, NJ;
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: Jun 1989
Volume: 11,
Issue: 6
On page(s): 600-610
ISSN: 0162-8828
References Cited: 44
CODEN: ITPIDJ
INSPEC Accession Number: 3433498
Digital Object Identifier: 10.1109/34.24794
Current Version Published: 2002-08-06
Abstract
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
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