I have the following scanned image of an area which I want to digitize in terms of coordinates and depths, where depths are shown in terms of contour lines. The depths are shown in the above image by the number over the contour line. I would like to divide this image in terms of grids/pixels and get two data - representing it's (x, y) coordinates and depth - for every grid/pixel.
Is there some Matlab function or File Exchange in Matlab that can be used to perform this task than doing manually? I would like to perform this task faster than if it were to be done manually. Thanks!
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I am trying to erode objects in a binary image such that they do not become smaller than some fixed size. Consider, for instance, a binary map composed of connected components (blobs), wherein one defines blob size by either the minimal or maximal antipolar (anti-perimetric) distance (i.e., the distance between two points that are as far from one another as they can be on the perimeter or contour of the blob; if the contour consists of N consecutively numbered points, then the distances evaluated would be those between points 1 and N/2+1, points 2 and N/2+2, etc.). Given such an arrangement, I seek to erode these blobs until the distance metric reaches a specified limit. If the blobs were simple circles, then the effect could be realized by ultimate erosion followed by dilation to a fixed size; however, the contour of an irregular object would be lost by such a procedure. Is there a way to achieve such an effect for connected, irregular components using built-in functions in MATLAB?
With no image and already tried code presented I can understand you wrong, but may be iterative using bwmorph with 'thin','skel' or 'shrink' will help you.
while(cond < cond_threshold)
bw=bwmorph(bw,...,1); %one of the options above
cond = calc_cond(bw);
end
I am trying to program a matlab code in R2012a that will allow a color representation of temperature change on a surface based on different corresponding height and temperature measurements. Then end goal would be to combine multiple images together to get some sort of a jpeg. The temperatures I recorded were time specific which is why it would be beneficial in the end to have a visual depicting this temperature change over time.
It has been a little bit of time since I have used matlab, and I have never created something This complicated. It has me a little over whelmed and wondering where to start.
Thanks for any and all advice!
You might want to look at the avifile documentation for details on how to create a movie from a series of frames, there is a nice example that should make it clear enough.
Now to generate the frames needed for the avi file. Here, the meshgrid and surf functions come in handy. meshgrid generates two coordinate matrices with x and y coordinates for every point on your plate, surf then allows you to plot a surface with colors given by the temperatures at these positions. For example:
[x,y] = meshgrid(1:5,1:10) % generate a 10*5 plate with step size 1
z = rand(10,5) % height of the plate for a given position
c = rand(10,5) % color values, these should be your (time-dependent) temperatures
surf(x,y,z,c)
It is not clear to me whether you actually have a curved surface (requiring three coordinates to define each position), or just a simple plate. If it is just a simple plate, you can just set z = ones(size(x)) to obtain an uniform height.
I am currently doing some image segmentation on a bone qCT picture, see for instance images below.
I am trying to find the different borders in the picture for instance the outer border separating the bone to the noisy background. In this analysis I am getting a list of points (vec(1,:) containing x values and vex(2,:) containing the y values) in random order.
To get them into order I am using using a block of code which effectively takes the first point vec(1,1),vec(1,2) and then finds the closest point among the rest of the points in the vector. And then repeats.
Now my problem is that I want to smooth the data but how do I do that as the points lie in a circular formation? (I do have the Curve Fitting Toolbox)
Not exactly a smoothing procedure, but a way to simplify your data would be to compute the boundary of the convex hull of the data.
K = convhull(O(1,:), O(2,:));
plot(O(1,K), O(2,K));
You could also consider using alpha shapes if you want more control.
I have two images(both are exactly same images) and I am trying to calculate the disparity between them using sum of squared distances and reconstruct disparity in 3D space.
Do I need to rectify the image before calculating disparity?
The following are the steps that I have done so far for disparity map computation(I have tried with rectification and without rectification but both are returning all zeroes disparity matrix).
For each pixel in the left image X,
Take the pixels in the same row in the right image.
Separate the row in right image to windows.
For each window,
Calculate the disparity for each pixel in that window with X
Select the pixel in the window which gives minimum SSD with X
Find the pixel with minimum disparity among all windows as the best match to X
Am I doing it correctly?
How can I visualise the 3D reconstruction of the disparity as scatter plot in matlab?
Rectification guarantees that matches are to be found in the same row (for horizontally separated cameras). If you have doubts about rectification of your images you can try to compare rows by drawing horizontal lines between horizontally separated images. If the lines hit the same features you are fine, see the picture below where images are NOT rectified. The fact that they are distorted means there was a lens distortion correction as well as attempted (but not actually performed correctly) rectification.
Now, let’s see what you meant by the same images. Did you mean the images of the same object that were taken from different viewpoints? Note that if the images are literally the same (the same viewpoints) the disparity will be zero as was noted in another answer. The definition of disparity (for horizontally separated cameras) is a value of shift (in the same row) between matching features. The disparity is related to depth (if optical axes of cameras are parallel) as disparity d=f*B/z, where z - depth, B - baseline or separation between cameras and f is a focal length. You can transform the formula above into disparity/B=f/z which basically says that disparity related to camera separation as focal length is related to distance. In other words, the ratios of horizontal and distance measures are equal.
If your images are taken with the cameras shifted horizontally the disparity (in a simple correlation algorithm) is typically calculated in 5-embedded loops:
loop over image1 y
loop over image1 x
loop over disparity d
loop over correlation window y
loop over correlation window x
Disparity, or D_best, gives you the best matching window between image1 and image2 across all possible values of d. Finally, scatterplots are for 3D point clouds while disparity can be rather visualized as a heat color map. If you need to visualize 3D reconstruction or simply saying a 3D point cloud calculate X, Y, Z as:
Z=fB/D, X=uZ/f, Y=v*Z/f, where u and v are related to column and row of wxh image as
u=col-w/2 and v=h/2-row, that is u, v form an image centered coordinate system.
If your two images are exactly the same, then the disparity would be 0 for every pixel. You either have to use two separate cameras to take the images, or take them with a single camera from two different locations. The best way to do 3D reconstruction is to use a calibrated stereo pair of cameras. Here is an example of how to do that using the Computer Vision System Toolbox for MATLAB.
I am using matlab to process data to get a radius distribution function. Now I get data(an array) of different distances of other atoms to 1 specific atom.
I used the "hist" command (hist(radius1,400)) and get a histogram:
But what I want is a curve, like this:
http://upload.wikimedia.org/wikipedia/commons/3/31/Lennard-Jones_Radial_Distribution_Function.svg
I tried some fit command, but it would give me a normal-distribution-like curve, which is not what I want. actually no fit is fine, I only want a curve to show its varying.
The raw data was a 4000*1 array of radius, is there any other way to get a curve of the top of each bar of the histogram?
Thanks so much.
Instead of automatically plotting a histogram using hist, you can get it to output the values:
[x, c] = hist(radius1,400);
x is the data in each bin, c the centre of each bin, so this replicates a histogram and then overplots a line on it (which will just connect the top of each bar so it may not look as smooth as you hoped):
bar(c,x);
hold on
plot(c,x,'r');
It is possible to use fit with an anonymous function as a custom model, but that may be overkill in this situation.