针对线激光测量系统对金属表面进行测量时强反射光影响光条提取的问题,提出了一种基于双高斯拟合的光条提取算法。首先对光条截面的灰度进行分析,发现了光条灰度的多峰分布规律;然后推导光的反射模型,对金属表面强反射光的产生原理和能量分布模型进行研究;之后根据分布模型建立双高斯拟合模型,设计光条提取算法,并用样例验证了算法的可行性;最后进行对比实验,分析了双高斯拟合法与传统光条提取算法的提取效果,并对结果进行了置信度评价。结果表明:双高斯拟合法可以有效抑制光条图像中强反射光的影响,准确提取光条中心;双高斯拟合法的置信度评价优于传统算法。

% This folder contains a collection of "fitting" functions. % (Some has demo options - the third section) % The GENERAL input to the functions should be samples of the distribution. % % for example, if we are to fit a normal distribution ('gaussian') with a mean "u" and varaince "sig"^2 % then the samples will distribute like: % samples = randn(1,10000)*sig + u % %fitting with Least-Squares is done on the histogram of the samples. % fitting with Maximum likelihood is done directly on the samples. % % % Contents of this folder % ======================= % 1) Maximum likelihood estimators % 2) Least squares estimators % 3) EM algorithm for estimation of multivariant gaussian distribution (mixed gaussians) % 4) added folders: Create - which create samples for the EM algorithm test % Plot - used to plot each of the distributions (parametric plot) % % % % % % Maximum likelihood estimators % ============================= % fit_ML_maxwell - fit maxwellian distribution % fit_ML_rayleigh - fit rayleigh distribution % (which is for example: sqrt(abs(randn)^2+abs(randn)^2)) % fit_ML_laplace - fit laplace distribution % fit_ML_log_normal- fit log-normal distribution % fit_ML_normal - fit normal (gaussian) distribution % % NOTE: all estimators are efficient estimators. for this reason, the distribution % might be written in a different way, for example, the "Rayleigh" distribution % is given with a parameter "s" and not "s^2". % % % least squares estimators % ========================= % fit_maxwell_pdf - fits a given curve of a maxwellian distribution % fit_rayleigh_pdf - fits a given curve of a rayleigh distribution % % NOTE: these fit function are used on a histogram output which is like a sampled % distribution function. the given curve MUST be normalized, since the estimator % is trying to fit a normalized distribution function. % % % % % Multivariant Gaussian distribution % ================================== % for demo of 1