Fastsmooth is a function of the form s=fastsmooth(a,w, type, edge). The argument "a" is the input signal vector; "w" is the smooth width; "type" determines the smooth type: type=1 gives a rectangular (sliding-average or boxcar); type=2 gives a triangular (equivalent to 2 passes of a sliding average); type=3 gives a pseudo-Gaussian (equivalent to 3 passes of a sliding average). The argument "edge" controls how the "edges" of the signal (the first w/2 points and the last w/2 points) are handled. If edge=0, the edges are zero. (In this mode the elapsed time is independent of the smooth width. This gives the fastest execution time). If edge=1, the edges are smoothed with progressively smaller smooths the closer to the end. (In this mode the execution time increases with increasing smooth widths). The smoothed signal is returned as the vector "s". (You can leave off the last two input arguments: fastsmooth(Y,w,type) smooths with edge=0 and fastsmooth(Y,w) smooths with type=1 and edge=0). Compared to convolution-based smooth algorithms, fastsmooth typically gives much faster execution times, especially for large smooth widths; it can smooth a 1,000,000 point signal with a 1,000 point sliding average in less than 0.1 second.
Straightforward and user-friendly. Good codes & Thx.
Thank you for this! I found it irritating that matlab has a smooth function but it is in one of their specialized toolboxes. This seems like a basic enough function to have available in the main program. Anyways, thanks for uploading!
How can I apply fastsmooth to an image?
I am new to image processing in Matlab.
thanks a lot,its a grt help to me
Just as advertised, fast, efficient and does what it says.
Very good job, useful and quite straightforward.
Usefull and practical thanks.
thank you for your help, it is great!
Added SegmentedSmooth.m and other functions that involve smoothing.
Added SegmentedSmooth.m and other functions related to smoothing.