Piecewise Linear Segmentation of Digital Contours in O(N. Log(N)) Through a Technique Based on Effective Digital Curvature Estimation

A novel and effective approach to piecewise contour segmentation in terms of straight line segments is described. The reported approach is based on a recently developed technique for digital curvature estimation that relies extensively upon digital signal processing techniques. When combined with an energy-based curvature compensation strategy, also shown here, such a framework allows not only the fast and accurate determination of the curvature at each of the points of the original contour, but also provides an effective means for multiscale contour analysis through Gaussian lowpass filtering. Straight line segments can be straightforwardly and speedily derived from the obtained curvature diagrams simply by looking for maximum curvature points. Considering that all the processing takes place in terms of one-dimensional signals (the parametrized representation of the discrete contour in terms of its x and y coordinates), one-dimensional fast Fourier correspond to the major computational demand required by the proposed techniques, thus implying O(N.Log(N)). Application examples are provided that fully illustrate the potential of the proposed framework for fast and accurate piecewise linear segmentation. Discussion on the real-time aspects of the proposed methodology as well as the design of an effective parallel/pipelined architecture for its execution have also been included and discussed.