smoothing

问题 I have a cloud point obtained from photogrammetry from a person's back. I'm trying to interpolate it to get a regular grid, and for that I'm using scipy.interpolate with good results so far. The problem is: the function I'm using ( scipy.interpolate.griddata ) uses the convex hull of the cloudpoint in the plane x,y, thus giving as result some values that don't exist in the original surface, which has a concave perimeter. The following illustration shows the original cloudpoint at the left

问题 I have a cloud point obtained from photogrammetry from a person's back. I'm trying to interpolate it to get a regular grid, and for that I'm using scipy.interpolate with good results so far. The problem is: the function I'm using ( scipy.interpolate.griddata ) uses the convex hull of the cloudpoint in the plane x,y, thus giving as result some values that don't exist in the original surface, which has a concave perimeter. The following illustration shows the original cloudpoint at the left

问题 Game: In a simple 2D Portrait Game made in Unity, I have a GameObject (Player) that has a fixed location and which is moving upwards. The Camera follows the Player and animated Obstacles are spawning from time to time moving left to right. The attached Screenshot shows the Scene. The Problem: The Movement is not smooth, as it seems like the Player is jittering. I think I already identified one of the causes: Big variation of Time.deltaTime. Average value is 0.0167, but I had variations.

来源： https://stackoverflow.com/questions/61552935/how-to-crop-an-elliptical-region-of-an-image-with-smooth-borders

来源： https://stackoverflow.com/questions/61552935/how-to-crop-an-elliptical-region-of-an-image-with-smooth-borders

问题 I have some data that were recoreded yearly as follows. mydata = [0.6619346141815186, 0.7170140147209167, 0.692265510559082, 0.6394098401069641, 0.6030995845794678, 0.6500746607780457, 0.6013327240943909, 0.6273292303085327, 0.5865356922149658, 0.6477396488189697, 0.5827181339263916, 0.6496025323867798, 0.6589270234107971, 0.5498126149177551, 0.48638370633125305, 0.5367399454116821, 0.517595648765564, 0.5171639919281006, 0.47503289580345154, 0.6081966757774353, 0.5808742046356201, 0

问题 If I smooth a data vector with a smoothing cubic spline my understanding is that each ‘segment’ between knots should be representable as a cubic polynomial. Is it possible to infer the equation of each segment from the spline coefficients after e.g fitting by the smooth.spline function in R? This is straightforward for an interpolating spine as the array of polynomial coefficients is generated explicitly. However, I’ve not been able to find an answer as to whether this is possible with a

问题 If I smooth a data vector with a smoothing cubic spline my understanding is that each ‘segment’ between knots should be representable as a cubic polynomial. Is it possible to infer the equation of each segment from the spline coefficients after e.g fitting by the smooth.spline function in R? This is straightforward for an interpolating spine as the array of polynomial coefficients is generated explicitly. However, I’ve not been able to find an answer as to whether this is possible with a

问题 I'm coding a mouse macro. It needs to meet certain points on the screen in a set delay between each point. For exammple, it must move (x 14, y 30) in 132ms. The issue I'm having is mouse_event jumps to that exact position so I need to include some sort of smoothing method so that it moves smoothly to each point. (the smoother the movement is the better the macro). Currently I am using this method of smoothing each movement. This works well but it has its limitations for example if it needs to

问题 My time-series data includes date-time and temperature columns as follows: rn25_29_o: ambtemp dt 1 -1.96 2007-09-28 23:55:00 2 -2.02 2007-09-28 23:57:00 3 -1.92 2007-09-28 23:59:00 4 -1.64 2007-09-29 00:01:00 5 -1.76 2007-09-29 00:03:00 6 -1.83 2007-09-29 00:05:00 I am using median smoothing function to enhance small fluctuations that are caused because of imprecise measurements. unique_timeStamp <- make.time.unique(rn25_29_o$dt) temp.zoo<-zoo(rn25_29_o$ambtemp,unique_timeStamp) m.av<