RANSAC

Given:
    data – a set of observed data points
    model – a model that can be fitted to data points
    n – minimum number of data points required to fit the model
    k – maximum number of iterations allowed in the algorithm
    t – threshold value to determine when a data point fits a model
    d – number of close data points required to assert that a model fits well to data

Return:
    bestfit – model parameters which best fit the data (or nul if no good model is found)

iterations = 0
bestfit = nul
besterr = something really large
while iterations < k {
    maybeinliers = n randomly selected values from data
    maybemodel = model parameters fitted to maybeinliers
    alsoinliers = empty set
    for every point in data not in maybeinliers {
        if point fits maybemodel with an error smaller than t
             add point to alsoinliers
    }
    if the number of elements in alsoinliers is > d {
        % this implies that we may have found a good model
        % now test how good it is
        bettermodel = model parameters fitted to all points in maybeinliers and alsoinliers
        thiserr = a measure of how well model fits these points
        if thiserr < besterr {
            bestfit = bettermodel
            besterr = thiserr
        }
    }
    increment iterations
}
return bestfit

e = probability that a point is an outlier
s = number of points in a sample
N = number of samples (we want to compute this,iteration num)
p = desired probability that we get a good sample