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I am implementing an image segmentation algorithm, at some point I need to implement a function named "Danielsson Function" which 'transforms a binary image into a grayscale distance map in which each particle pixel is assigned a gray-level value equal to its shortest Euclidean distance from the particle border'.
I'm not an expert in image processing and the only way I can imagine is using a BFS algorithm for every pixel but that would take ages.
I couldn't find much in web, so I want to know is there any optimized way to implement this or is there any Matlab/Octave function that can do such thing?
you can simply do that by using the "bwdist" matlab function, as the comments mentioned
and here is the link for help if you needed any :
Distance transform of binary image - MATLAB bwdist
And here is the code :
I=uint8(bwdist(some_binary_image))
Be sure to use the 'uint8' because 'bwdist' only gives you the distance matrix which has double number values.
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While solving a puzzle, I ended up having a complexity of T(n)=T(n-3)+T(n-5).
I was trying subtraction method. But I am unable to solve this. Please explain what should be the procedure.
This is a linear homogeneous difference equation with constant coeffs.. It is usually solved by transforming it to the complex plane and solving a polynomial.
Without a CS background (as you state), I'm afraid the details wouldn't fit in here. Start with the Wikipedia entry, if you're interested.
If you want to skip to the final solution, here is the Wolfram Alpha for it.
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In Matlab using the rand routine, how should I write the code to generate 500 samples from an exponential distribution, whose pdf is:
(1/mu)*exp(-x/mu); x>=0
Assuming you really have to do it using the rand function: exploit the property that the minus logarithm of a normalized uniform RV is a normalized exponential RV:
samples = -mu*log(rand(1,500));
Use random function.
For example to create a 4*6 matrix with mu=1.3 with an exponential distribution use:
random('Exponential',1.3,4,6)
or
random('exp',1.3,4,6)
If you have the Statistic toolbox you can simply use exprnd much like you use rand:
r = exprnd(mu);
where the size of r will be the size of the mean, mu, or
r = exprnd(mu,m,n);
where mu is a scalar mean, and m and n are the size of your desired output. If you type edit exprnd, you'll see that the code is virtually identical to that kindly provided by #LuisMendo. You might find the other functions related to the exponential distribution helpful to, such as exppdf and expcdf. These are simple as well and implement basic equation that you can find in your textbook or on Wikipedia.
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Closed 9 years ago.
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I am new to image processing and am trying to learn few concepts by practically implementing certain functions. I heard about creating eigen images of an image, so tried to implement the same, to actually know what they are and what properties they alter.
Thus I obtained the eigen vectors using the eig function in matlab. How can I display these eigen images using the vector? Please forgive me if the question is wrong or rudimentary. Your help is much appreciated.
Assuming you have several images of size r x c, then taken the steps described on wikipedia, you should now have eigenvectors ev1, ev2 ... of length r x c.
If this is the case, it should be fairly easy to turn these into images again:
myImage1 = reshape(ev1,r,c);
Check whether r and c are in the right order and whether you need to transpose, but this is basically it.
For showing them you may want to look into surf or image.
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I have a function, dependant on a vector k=(x,y,z) and a function f(x,y,z)=0, I would like to find the solution space for (x,y,z).
Can this even be done analytically in matlab? I imagine it can be done numerically because my initial thought was to plot the surface created by this function, however this is of no use to me as I have 9 other constants in my equation with no numerical value assigned to them. Many thanks in advance for any help.
EDIT: This is for a polynomial degree 4.
There are no generic, analytical solvers for given function f(x,y,z) neither in matlab, nor in any other language. If such solution would exist, the Riemann hypothesis (and dozens others) would be solved ;)
For simple problems you could use symbolic math toolbox and a solve function:
http://www.mathworks.com/help/symbolic/solve.html
And obbiously there are numerical solvers, which you can use like vpasolve and others
http://www.mathworks.com/help/symbolic/vpasolve.html
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I am interested in porting my Matlab implementation to C++ for speed improvement. I tried armadillo. It is perfect for porting code from Matlab because Armadillo's library functions names/syntax is very close to that of Matlab programming. However I realized that at some places Matlab out performed armadillo because my data is mainly sparse and armadillo does not give it any special treatment and simply treats it as dense matrices. Armadillo team is working on sparse matrices support but currently it is not available. So I am looking for a library which like Armadillo has syntax very close to Matlab (or is easy to use) and supports sparse matrices for speed and space optimization.
Not sure what you are trying to do but try SPAMS. it's a good and fast sparse modeling implementation.
Version 3.6.2 has more Sparse Matrices support.
As of version 3.4, Armadillo has preliminary support for sparse matrices. This is likely to get more complete in future versions.