Title :
A nonparametric method for fitting a straight line to a noisy image
Author :
Kamgar-Parsi, B. ; Netanyahu, Nathan S.
Author_Institution :
Center for Autom. Res., Maryland Univ., College Park, MD
fDate :
9/1/1989 12:00:00 AM
Abstract :
In fitting a straight line to a noisy image, the least-squares method becomes highly unreliable either when the noise distribution is nonnormal or when it is contaminated by outliers. The authors propose a nonparametric method, the median of the intercepts, to overcome these difficulties. This method is free of assumptions about the noise distribution and insensitive to outliers, and it does not require quantization of the parameter space. Thus, unlike the Hough transform, its outcome does not depend on the bin size. The method is efficient and its implementation does not involve practical difficulties such as local minima or poor convergence of iterative procedures
Keywords :
least squares approximations; pattern recognition; picture processing; noise distribution; noisy image; nonparametric method; pattern recognition; picture processing; straight line fitting; Convergence; Gaussian distribution; Gaussian noise; Image analysis; Iterative methods; Laboratories; Least squares methods; Maximum likelihood estimation; Noise level; Quantization;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on