Regularization of inverse visual problems involving discontinuities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Corner detection and curve representation using cubic B-spline
Computer Vision, Graphics, and Image Processing
The Computation of Visible-Surface Representations
IEEE Transactions on Pattern Analysis and Machine Intelligence
One-Dimensional Regularization with Discontinuities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimal Estimation of Contour Properties by Cross-Validated Regularization
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Detection of Dominant Points on Digital Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimal Local Weighted Averaging Methods in Contour Smoothing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Inverse Quantization of Digital Binary Images for Resolution Conversion
Scale-Space '01 Proceedings of the Third International Conference on Scale-Space and Morphology in Computer Vision
Inverse quantization for resolution conversion
Proceedings of the 11th international conference on Theoretical foundations of computer vision
Edge Detection by Adaptive Splitting
Journal of Scientific Computing
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Investigates the estimation of 2-D boundary functions from sampled data sets where both noise and corners are present. The approach is based on the partial smoothing spline in which the estimated boundary function consists of an ordinary smoothing spline and a parametric function that describes the discontinuities (i.e., corners of the boundary). Prior knowledge about the boundary, such as the number of corners, their locations, noise levels, and the amount of smoothness, is not required for the boundary estimate. The smoothing parameter and the corner locations of the spline, which are parts of the estimate, are determined by the generalized cross-validation method whereby statistical properties are gathered from the input sampled data rather than specified a priori. This approach enables the smoothing of a noisy boundary while retaining an accurate description of the boundary corners. Extensive experiments were conducted to verify its ability to smooth noise while retaining a good representation of boundary corners, and do not rely on any prior information.