The detection of dominant points on digital curves by scale-space filtering

Abstract

The detection of dominant points is an important preprocessing step for shape recognition. An effective method of scale-space filtering with a Gaussian kernel is introduced to detect dominant points on digital curves. The conventional polygonal approximation algorithms are time-consuming and need input parameter tuning for Gaussian smoothing the noise and quantization error, also they are sensitive to scaling and rotation of the object curve. The above difficulty can be overcome by finding out the dominant points at each scale by scale-space filtering. By tracing back the dominant point contours in the scale-space image, the stable cardinal curvature points can be detected very accurately. This new method requires no input parameters, and the resultant dominant points do not change under translation, rotation and scaling. Meanwhile a fast convolution algorithm is proposed to detect the dominant points at each scale.