CURVATURE IN DIGITAL 2D IMAGES
Vladimir Kovalevsky · International Journal of Pattern Recognition and Artificial Intelligence · 2001
The paper presents an analysis of sources of errors when estimating derivatives of numerical or noisy functions. A method of minimizing the errors is suggested. When being applied to the estimation of the curvature of digital curves, the analysis shows that under the conditions typical for digital image processing the curvature can rarely be estimated with a precision higher than 50%. Ways of overcoming the difficulties are discussed and a new method for estimating the curvature is suggested and investigated as to its precision. The method is based on specifying boundaries of regions in gray value images with subpixel precision. The method has an essentially higher precision than the known methods.