Title :
On critical point detection of digital shapes
Author :
Zhu, Pengfei ; Chirlian, Paul M.
Author_Institution :
James River Corp., Easton, PA, USA
fDate :
8/1/1995 12:00:00 AM
Abstract :
In this paper, we present a nonlinear algorithm for critical point detection (CPD) of 2D digital shapes. The algorithm eliminates the problems arising from curvature approximation and Gaussian filtering in the existing algorithms. Based on the definition of “critical level,” we establish a set of criteria for the design of an effective CPD algorithm for the first time. By quantifying the critical level to the modified area confined by three consecutive “pseudocritical points,” a simple but very effective algorithm is developed. The comparison of our experimental results with those of many other CPD algorithms shows that the proposed algorithm is superior in that it provides a sequence of figures at every detail level, and each has a smaller integral error than the others with the same number of critical points. The experimental results on shapes with various complexities also show the algorithm is reliable and robust with regard to noise
Keywords :
curve fitting; filtering theory; image recognition; 2D digital shapes; Gaussian filtering; consecutive pseudocritical points; critical level; critical point detection; curvature approximation; digital shapes; nonlinear algorithm; Algorithm design and analysis; Approximation algorithms; Feature extraction; Filtering algorithms; Noise robustness; Noise shaping; Nonlinear filters; Quantization; Shape; Space technology;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on