smoothing

The smooth.spline function in R allows a tradeoff between roughness (as defined by the integrated square of the second derivative) and fitting the points (as defined by summing the squares of the residuals). This tradeoff is accomplished by the spar or df parameter. At one extreme you get the least squares line, and the other you get a very wiggly curve which intersects all of the data points (or the mean if you have duplicated x values with different y values) I have looked at scipy.interpolate.UnivariateSpline and other spline variants in Python, however, they seem to only tradeoff by

I am trying to tan human skin with different intensity with help of opencv. I have already identified human skin and changing color tone of those pixels. But it is not smooth. Top left - original image Top right - saturation channel of original image Bottom left - Gray scale mask identifying locations of skin on original image Bottom right - result image with color tone changed of pixels located in mask. Now my problem is that, in mask image some gap is left because of variation on color tone or brightness in original image. And that is why those portion is missed in result image. Can anyone

Imagine that I have a set of measurements of x that are taken by many processes x 0 ... x N at times t 0 ... t N . Let's assume that at time t I want to make an estimate of the current value of x , based on the assumption that there is no long term trend I know about and that x can be predicted from an algorithm such as exponential smoothing. As we have many processes, and N can get very large, I can't store more than a few values (e.g. the previous state). One approach here would be to adapt the normal exponential smoothing algorithm. If samples are taken regularly, I would maintain an

I have some 9000 points that are plotted on a graph: [ Full resolution ] Actually, the plot is not as smooth as I wanted it to be. Is there some way I can smoothen the graph to a required degree? Or some form of thresholding so that I can selectively smoothen out the parts that is too bumpy? I am not sure but can fast-fourier-transform help? A simple (ad hoc) way is to just take a weighted average (tunable by alpha ) at each point with its neighbors: data(2:n-1) = alpha*data(2:n-1) + (1-alpha)*0.5*(data(1:n-2)+data(3:n)) or some variation thereof. Yes, to be more sophisticated you can Fourier

The following code runs a loops but the problem is the speed; it takes several hours to finish and I am looking for an alternative so that I don´t have to wait so long. Basically what the code does the follolling calculations: 1.-It calculates the mean of the values of the 60 days. 2.-It gets the standard deviation of the values of the 60 days. 3.-It gets the Max of the values of the 60 days. 4.-It gets the Min of the values of the 60 days. 5.-Then with the previous calculations the code "smooths" the peaks up and down. 6.-Then the code simply get the means from 60, 30, 15 and 7 Days. So the