I wish to put 2D curve fitting to different axis within a 3D plot. I am attaching an image for reference.
on this actual data set:
As you can see the curve fitting for x and y axis is at z=0 value, I need that at say z=10.
Further, when I try to do curve fitting for x and z data set or y and z data set, the fitted curve instead of appearing on the the X-Z plane or Y-Z plane, is appearing on the X-Y plane.
All help is appreciated.
I have an array of spatial data [lat,lon,intensity] on the Earth surface. Plotting the data with surf(lon,lat,intensity) shows the surface is a Gaussian shaped. I want to fit a 2D Gaussian function to the data to get the center and spread (mean and variance) of the data.
It's easy to fit a bivariate Gaussian function for data as [x,y,intensity]. But my data is sampled on the sphere. Latitude and longitude cannot be treated as for x and y in cartesian coordinates.
Given some function z = f(x,y), I'm interested in creating a (1D) line plot along an arbitrary cutting plane in x,y,z. How do I do this in Matlab? Slice, for example, provides a higher dimensional version (colormap of density data) but this is not what I'm looking for.
E.g.:
z = peaks(50);
surf(z);
%->plot z along some defined plane in x,y,z...
This has been asked before, e.g. here, but this is the answer given is for reducing 3D data to 2D data, and there is no obvious answer on googling. Thanks.
If the normal vector of the plane you want to slice your surface will always lay in the xy plane, then you can interpolate the data over your surface along the x,y coordinates that are in the slicing line, for example, let the plane be defined as going from the point (0,15) to the point (50,35)
% Create Data
z=peaks(50);
% Create x,y coordinates of the data
[x,y]=meshgrid(1:50);
% Plot Data and the slicing plane
surf(z);
hold on
patch([0,0,50,50],[15,15,35,35],[10,-10,-10,10],'w','FaceAlpha',0.7);
% Plot an arbitrary origin axis for the slicing plane, this will be relevant later
plot3([0,0],[15,15],[-10,10],'r','linewidth',3);
Since it is a plane, is relatively easy to obtain the x,y coordinates alogn the slicing plane with linspace, I'll get 100 points, and then interpolate those 100 points into the original data.
% Create x and y over the slicing plane
xq=linspace(0,50,100);
yq=linspace(15,35,100);
% Interpolate over the surface
zq=interp2(x,y,z,xq,yq);
Now that we have the values of z, we need against what to plot them against, that's where you need to define an arbitrary origin axis for your splicing plane, I defined mine at (0,15) for convenience sake, then calculate the distance of every x,y pair to this axis, and then we can plot the obtained z against this distance.
dq=sqrt((xq-0).^2 + (yq-15).^2);
plot(dq,zq)
axis([min(dq),max(dq),-10,10]) % to mantain a good perspective
I have given 3d points of a scene or a subset of these points comprising one object of the scene. I would like to create a depth image from these points, that is the pixel value in the image encodes the distance of the corresponding 3d point to the camera.
I have found the following similar question
http://www.mathworks.in/matlabcentral/newsreader/view_thread/319097
however the answers there do not help me, since I want to use MATLAB. To get the image values is not difficult (e.g. simply compute the distance of each 3d point to the camera's origin), however I do not know how to figure out the corresponding locations in the 2d image.
I could only imagine that you project all 3d points on a plane and bin their positions on the plane in discrete, well, rectangles on the plane. Then you could average the depth value for each bin.
I could however imagine that the result of such a procedure would be a very pixelated image, not being very smooth.
How would you go about this problem?
Assuming you've corrected for camera tilt (a simple matrix multiplication if you know the angle), you can probably just follow this example
X = data(:,1);
Y = data(:,1);
Z = data(:,1);
%// This bit requires you to make some choices like the start X and Z, end X and Z and resolution (X and Z) of your desired depth map
[Xi, Zi] = meshgrid(X_start:X_res:X_end, Z_start:Z_res:Z_end);
depth_map = griddata(X,Z,Y,Xi,Zi)
I am wondering if there is a way to build a random 3D surface from only one (top-down) 2D image of this surface. The fact is that the 3D surface needs the z-coordinates (the heights and the depths) and the 2D (top-down) image gives only the x and y coordinates.
I believe that the main problem is that we can't get the real ranges of the dimensions (x,y,z) of the surface from one 2D (top-down) image but we can get some kind of normalized scaling which is not the real one (it's just similar).
For example:
If we have an image with a surface (2D) and we want 3D of this surface (x,y,z) we can have easily the x and the y coordinates from the image. We can't have the real range of the amplitude (z coordinate) in each point of the surface but only the gray-tones scaling. Is there any ideas on how could we take the real sizes of the amplitudes of a surface from one 2D (top-down) image?
Left is a sample of 2D top-down image and Right is a surface which created by the 2D
http://www.sendspace.com/file/9wzx0u
p.s.
I can't post an image because of my reputation, so I uploaded one on sedspace.com.
Read in the image:
A = imread(filename)
Plot the surface plot using the magnitude of the value read in for each x and y from the file:
surf(A)