USING SIMPLE DECOMPOSITION FOR SMOOTHING AND FEATURE POINT DETECTION OF NOISY DIGITAL CURVES

This correspondence presents an algorithm for smoothed polygonal appro ximation of noisy digital planar curves, and feature point detection. The, resulting smoothed polygonal representation preserves the signs o f the curvature function of the curve. The algorithm is based on a sim ple decomposition of noisy digital curves into a minimal number of con vex and concave sections. The location of each separation point is opt imized, yielding the minimal possible distance between the smoothed ap proximation and the original curve. Curve points within a convex (conc ave) section are discarded if their angle signs do not agree with the section sign, and if the resulted deviations from the curve are less t han a threshold epsilon which is derived automatically. Inflection poi nts are curve points between pairs of convex-concave sections, and cus ps are curve points between pairs of convex-convex or concave-concave sections. Corners and points of local minimal curvature are detected b y applying the algorithm to respective total curvature graphs. The det ection of the feature points is based on properties of pairs of sectio ns that are determined in an adaptive manner, rather than on propertie s of single points that are based on a fixed-size neighborhood. The de tection is therefore reliable and robust. Complexity analysis and expe rimental results are presented.