smoothing

问题 I have done image blurring using Gaussian Blur in opencv (smoothing). I came across in search in Google about histogram blurring. Now my question here is, do I apply blurring to the histogram or to the image and than reconstruct the histogram. What I feel is that I apply to the image and than make the histogram because applying blurring to the histogram does not change any parameters and context about the image. Well I am not sure that is my reason but it will be really grateful if I can get

问题 I have a measurment of which should fit an hysteresis. For visualisation purpose I would like to plot a line approximating the hysteresis to help explain this pattern. I created an example in the following image using the code below. I would like to have an output similar to the green curve - however I don't have this data directly available, and I don't care whether it is pointy. However most smoothing functions such as smooth.spline which I plotted in blue - allow no loops. The closest I

问题 So I need a write method to create a curve between two points, with each point having a normalized vector pointing in an arbitrary direction. I have been trying to devise such a method but haven't been able to wrap my head around the math. Here, since a picture is worth a thousand words this is what I need: In the picture, the vectors are perpendicular to the red lines. I believe the vectors need to be weighted the same with a weight equivalent to the distance between the points. It needs to

问题 For a project I am working on, I have successfully performed the SFM procedure on road image data, and have been able to generate a .ply file containing point cloud coordinates (X, Y, Z), RGB values, and normals (nx, ny, nz). Now, I am interested in calculating curvature values for each point from the data I have. I have come across Surface Curvature MATLAB Equivalent in Python, but the implementation is said to work only when X, Y, and Z are 2D arrays. Is Gaussian and Mean curvatures

问题 UPDATE Thanks to @user20650 and @李哲源 Zheyuan Li, here is the solution I came up with: # Example data set: df # 3600 observations/points # Create a vector of the cumulative distances between all of the points require(Momocs) cumdist <- coo_perimcum(df) # Apply splines parametrically - define a spline interpolated mapping R --> R^2 of some curve c # c(t) = (x(t), y(t)) # 't' is the set of cumulative distances (as defined above) # Set the number of points to some fraction of the number of

问题 I've got a 2D closed polyline, which is reasonably smooth. The vertices that define the polyline however are not spaced equally. Sometimes two will be very close, sometimes as many as four will be very close together. I'd like to smooth the polyline, but a regular averaging algorithm tends to shrink the area: for (int i = 0; i < (V.Length-1); i++) { PointF prev = V[i-1]; //I have code that wraps the index around. PointF next = V[i+1]; PointF pt = V[i]; float ave_x = one_third * (prev.X + next

问题 I have two data-frame with unequal number of rows. But i need to smooth the data in both the data frames and plot them together. I can smooth each dataframe with lowess/loess. However, when i try to plot the lines for both the data-frames together, i usually get error "unequal number of rows". I found a way around this by using spline . I want to know if the following would be valid: tmp1 <- spline( lowess( df1[,1], df[,2] ), n = 20 ) tmp2 <- spline( lowess( df2[,1], df2[,2] ), n = 20 ) plot(

问题 I'd like to smooth a scatter plot shown below (the points are very dense), and the data is here. There is large noise in the middle of the curve, and I'd like to smooth the curve, also the y value should monotonically increase . Since there are lots of curves like this, it is kind of hard to know where the noise is in the curve. I tried scipy.signal.savgol_filter , but it didn't work. The code I used is: from scipy.signal import savgol_filter from scipy import interpolate import numpy as np

问题 I have a 3D mesh which represents a surface with some rough boundaries which I would like to smooth: I am using a half edge data structure for storing the geometry so I can easily iterate over the boundary edges, vertices and faces. I can also quite easily determine whether a given pair of edges is a convex/concave using a dot and cross product. What would be the best approach for smoothing the edges out, so they form a continuous, curvy line, rather then the sharp pattern seen in the

问题 I have been working on video stabilization for quite a few weeks now. The algorithm I'm following basically involves 3 steps :- 1. FAST feature detection and Matching 2. Calculating affine transformation (scale + rotation + translation x + translation y ) from matched keypoints 3. Smooth motion parameters using cubic spline or b-spline. I have been able to calculate affine transform. But I am stuck at smoothing motion parameters. I have been unable to evaluate spline function to smooth the