A new method for detecting dominant points is presented. It does not require any input parameter, and the dominant points obtained by this method remain relatively the same even when the object curve is scaled or rotated. In this method, for each boundary point, a support region is assigned to the point based on its local properties. Each point is then smoothed by a Gaussian filter with a width proportional to its determined support region. A significance measure for each point is then computed. Dominant points are finally obtained through nonmaximum suppression. Unlike other dominant point detection algorithms which are sensitive to scaling and rotation of the object curve, the new method will overcome this difficulty. Furthermore, it is robust in the presence of noise. The proposed new method is compared to the Teh-Chin algorithm (see C. Teh and R. T. Chin, IEEE Trans. Pattern Anal. Mach. Intell. 11, 859-872 (1989)) in terms of the computational complexity, the approximation errors and the number of detected dominant points of an object curve.
All Science Journal Classification (ASJC) codes
- Signal Processing
- Computer Vision and Pattern Recognition
- Artificial Intelligence
- Dominant points
- Gaussian smoothing
- Support region