Corner point detection using nest moving average

摘要

Corners are rich in high frequency components. A low-pass filter suppresses corners and results in a smooth curve. Therefore, corner points have a larger amount of shift than other points and can easily be detected by a low-pass filter. Experiments suggest that a moving average can do this job suitably with a low computational load and good locality in spatial domain. A moving average with a smaller size window is used to perprocess the digital boundary with quantization error or noise.