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.
展开▼
