Curvilinear Feature Extraction for Noisy Point Pattern Images

A frequently encountered task in many imaging problems is the detection of curvilinear features hidden in noisy spatial point patterns. This paper investigates the use of principal curves to fulfill this task. The minimum description length principle is applied simultaneously to select the number and to control the smoothness of the principal curves that are required to represent the real features. Practical performance of the proposed approach is demonstrated via numerical experiments.