I need to calculate Tanimoto Coefficient. I don't know what's wrong in my code. I have 2 nearly similar images. But the value obtained using my code indicates that the two images are highly dissimilar. Kindly help me with my code.
%Tanimoto coeff
I=imread('sliver3.jpg');
J=imread('ref5.jpg');
figure,imshow(I),title('Original');
figure,imshow(J),title('Reference');
inter=intersect(I,J,'rows');
uni=union(I,J,'rows');
si=size(inter);
su=size(uni);
tc=si/su
I attach three images here. First one is the segmented output. Second is a reference image.Third is also a reference but which is highly dissimilar. So, the output must be that, first and second must be nearly similar and first and third must be highly dissimilar. But I'm getting the opposite.
For first two images, tc =0.4895
For first and third images, tc=0.5692
Kindly help me out.
I think you should use the sum() function on union and intersect instead of size() since the Tanimoto Coefficient is the "summation of the intersect"/"summation of the union"
Related
I am trying to avoid the for loops and I have been reading through all the old posts there are about it but I am not able to solve my problem. I am new in MATLAB, so apologies for my ignorance.
The thing is that I have a 300x2 cell and in each one I have a 128x128x256 matrix. Each one is an image with 128x128 pixels and 256 channels per pixel. In the first column of the 300x2 cell I have my parallel intensity values and in the second one my perpendicular intensity values.
What I want to do is to take every pixel of every image (for each component) and sum the intensity values channel by channel.
The code I have is the following:
Image_par_channels=zeros(128,128,256);
Image_per_channels=zeros(128,128,256);
Image_tot_channels=zeros(128,128,256);
for a=1:128
for b=1:128
for j=1:256
for i=1:numfiles
Image_par_channels(a,b,j)=Image_par_channels(a,b,j)+Image_cell_par_per{i,1}(a,b,j);
Image_per_channels(a,b,j)=Image_per_channels(a,b,j)+Image_cell_par_per{i,2}(a,b,j);
end
Image_tot_channels(a,b,j)=Image_par_channels(a,b,j)+2*G*Image_per_channels(a,b,j);
end
end
end
I think I could speed it up introducing (:,:,j) instead of specifying a and b. But still a for loop. I am trying to use cellfun without any success due to my lack of expertise. Could you please give me a hand?
I would really appreciate it.
Many thanks and have a nice day!
Y
I believe you could do something like
Image_par_channels=zeros(128,128,256);
Image_per_channels=zeros(128,128,256);
Image_tot_channels=zeros(128,128,256);
for i=1:numfiles
Image_par_channels = Image_par_channels + Image_cell_par_per{i,1};
Image_per_channels = Image_per_channels + Image_cell_par_per{i,2};
end
Image_tot_channels = Image_par_channels + 2*G*Image_per_channels;
I haven't work with matlab in a long time, but I seem to recall you can do something like this. g is a constant.
EDIT:
Removed the +=. Incremental assignment is not an operator available in matlab. You should also note that Image_tot_channels can be build directly in the loop, if you don't need the other two variables later.
I have a csv file which contains data like below:[1st row is header]
Element,State,Time
Water,Solid,1
Water,Solid,2
Water,Solid,3
Water,Solid,4
Water,Solid,5
Water,Solid,2
Water,Solid,3
Water,Solid,4
Water,Solid,5
Water,Solid,6
Water,Solid,7
Water,Solid,8
Water,Solid,7
Water,Solid,6
Water,Solid,5
Water,Solid,4
Water,Solid,3
The similar pattern is repeated for State: "Solid" replaced with Liquid and Gas.
And moreover the Element "Water" can be replaced by some other element too.
Time as Integer's are in seconds (to simplify) but can be any real number.
Additionally there might by some comment line starting with # in between the file.
Problem Statement: I want to eliminate the first dip in Time values and smooth out using some quadratic or cubic or polynomial interpolation [please notice the first change from 5->2 --->8. I want to replace these numbers to intermediate values giving a gradual/smooth increase from 5--->8].
And I wish this to be done for all the combinations of Elements and States.
Is this possible through some sort of coding in Matlab etc ?
Any Pointers will be helpful !!
Thanks in advance :)
You can use the interp1 function for 1D-interpolation. The syntax is
yi = interp1(x,y,xi,method)
where x are your original coordinates, y are your original values, xi are the coordinates at which you want the values to be interpolated at and yi are the interpolated values. method can be 'spline' (cubic spline interpolation), 'pchip' (piece-wise Hermite), 'cubic' (cubic polynomial) and others (see the documentation for details).
You have alot of options here, it really depends on the nature of your data, but I would start of with a simple moving average (MA) filter (which replaces each data point with the average of the neighboring data points), and see were that takes me. It's easy to implement, and fine-tuning the MA-span a couple of times on some sample data is usually enough.
http://www.mathworks.se/help/curvefit/smoothing-data.html
I would not try to fit a polynomial to the entire data set unless I really needed to compress it, (but to do so you can use the polyfit function).
I have one big matrix of for example 3000X300. And I need to select each element and do several calculations with it. I looked into using the array fun function but because the output of my program is not one value this is not possible.
It works fine now with the loops but it has to preform much faster, so i want to remove the for loop.
Maybe i'll try to be more specific: Each value of the big matrix has to give me an answer of 4 different matrices with the size of 4X6020..
So i don't know if this is possible making this vectorized...
Maybe somebody has other suggestions to make it faster?
greetings,
You can use arrayfun and set uniformoutput to false. See here.
http://www.mathworks.com/help/techdoc/ref/fftshift.html
If you check that link - thats what I want to do in the first picture - swap quadrants of a matrix.
However, I cant seem to think of a good way to do this without having several loops to pull out the relevant sub-matrices.
I need it to work with MxN matrices, where M and N can be any combination of even and odd.
Thanks
The following should work
sz = ceil(size(A)/2)
A = A([sz(1)+1:end, 1:sz(1)], [sz(2)+1:end, 1:sz(2)])
That only works for 2d matrices, but can be easily generalized to the Nd case.
If you enter type fftshift.m at MATLAB's command line, you'll see the source code for MATLAB's implementation of the function (use edit fftshift.m if you want to view it in the editor with syntax highlighting). I'm not posting the code here, as it is copyrighted. However, you can try it on your machine and re-implement the same in C. Its up to you to figure out the license terms etc, if you're into any of that.
Keeping simple, take a matrix of ones i.e.
U_iso = ones(72,37)
and some parameters
ThDeg = 0:5:180;
dtheta = 5*pi/180;
dphi = 5*pi/180;
Th = ThDeg*pi/180;
Now the code is
omega_iso = 0;
for i = 1:72
for j=1:37
omega_iso = omega_iso + U_iso(i,j)*sin(Th(j))*dphi*dtheta;
end
end
and
D_iso = (4 * pi)/omega_iso
This code is fine. It take a matrix with dimension 72*37. The loop is an approximation of the integral which is further divided by 4pi to get ONE value of directivity of antenna.
Now this code gives one value which will be around 1.002.
My problem is I dont need 1 value. I need a 72*37 matrix as my answer where the above integral approximation is implemented on each cell of the 72 * 37 matrix. and thus the Directviity 'D' also results in a matrix of same size with each cell giving the same value.
So all we have to do is instead of getting 1 value, we need value at each cell.
Can anyone please help.
You talk about creating a result that is a function essentially of the elements of U. However, in no place is that code dependent on the elements of U. Look carefully at what you have written. While you do use the variable U_iso, never is any element of U employed anywhere in that code as you have written it.
So while you talk about defining this for a matrix U, that definition is meaningless. So far, it appears that a call to repmat at the very end would create a matrix of the desired size, and clearly that is not what you are looking for.
Perhaps you tried to make the problem simple for ease of explanation. But what you did was to over-simplify, not leaving us with something that even made any sense. Please explain your problem more clearly and show code that is consistent with your explanation, for a better answer than I can provide so far.
(Note: One option MIGHT be to use arrayfun. Or the answer to this question might be more trivial, using simple vectorized operations. I cannot know at this point.)
EDIT:
Your question is still unanswerable. This loop creates a single scalar result, essentially summing over the entire array. You don't say what you mean for the integral to be computed for each element of U_iso, since you are already summing over the entire array. Please learn to be accurate in your questions, otherwise we are just guessing as to what you mean.
My best guess at the moment is that you might wish to compute a cumulative integral, in two dimensions. cumtrapz can help you there, IF that is your goal. But I'm not sure it is your goal, since your explanation is so incomplete.
You say that you wish to get the same value in each cell of the result. If that is what you wish, then a call to repmat at the end will do what you wish.