jetqin

Thank you!

As you said, this VI may automatically perform some optimizations when the iterations is "0".

The main difference between the NI algorithm and most others is that it removes outliers. It calculates a "best fit" using the mean squared difference method and applies a score. If iterations is set to 0, it is finished and returns the line (as in your first image). If iterations are greater than "0" it removes an outlier and then re-calculates the MSD and re-calculates the score to see if the there is an improvement. It keeps iterating until it either reaches a certain score or until it reaches the iteration number

So in your second case, if you set the iterations to >0, the final point will be eliminated and it will decide that the first two are the "best fit". You need to have more data points to use this function effectively - the more, the better.

Actually, the first image is the fit line returned when the iterations is 1,and the second is the results when the iterations is 0 or >1.That's why I can't understand.

jetqin

Thank you, shaunR!

But I still have some confusion. For example, how does this line fitting algorithm find the initial subset of points to start, and use what method to fit a line to the subset of points. Strangely enough, if I set the iterations value to 0 or a number more than 1, this VI will return almost best fit line, but when I set this value to 1, it return a line that seems to be fitted by the standard fit method.

(the red spots represent the original edge points, and the green line is the fit line returned by this VI)

The line fitting function in NI Vision uses a robust algorithm to find a line that best fits a set of points.Although I have read the description of this VI in the NI Vision concepts manual, I am still confused about the robust algorithm it uses. So does any one know this algorithm or help to provide some references? thank you!