二项分布——拉普拉斯和离散的高斯——拉普拉斯边缘检测算子的性能

Performances of the Laplacian of Binomial Distribution and the Discrete Laplacian of Gaussian Edge Detection Operators

本文从空域及频域两方面对二项分布——拉普拉斯(LOB)及离散的高斯——拉普拉斯(DLOG)边缘检测算子进行了分析比较.在两种算子的尺度空间常数很大时,它们的时域和频域特性及对图象进行边缘检测的性能是基本相同的.但当尺度空间常数较小时,从它们在频域中带通特性的中心频率、3分贝带宽及截止频率处的高频衰减性能进行比较可知,LOB算子稍优于离散的LOG算子.故LOB算子可看成是LOG算子的一种离散实现.文中给出了实验结果,证明了分析结果的正确性.

This paper presents a performance analysis and comparison between the Laplacian of binomial distribution (LOB) and the discrete Laplacian of Guassian (DLOG) edge detection operators in the space domain and frequency domain. When the scale space constants of the two edge detection operators are large enough, the characteristics in space domain and frequency domain and performances in image edge detection are almost the same. But when the scale space constants are smaller, the conclusion can be made that the performances of LOB operator are little better than that of discrete LOG operator after comparisons of central frequency, 3dB bandwidth and high frequency attenuation rate at cut off frequency in frequency domain. LOB operator may be considered as a discrete realization of LOG operator. The experiments are given to verify the correctness of analysis.