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I have a signal in energy. But it's not from zero to E, but from A to B. It has N data points. If I add 2.5N zeros to the left of A, I will have a good enough signal in time after Fourier Transform. However, is it too much zeros to add? How much zeros can be added without producing too much artifacts in the signal?
Zero-padding before an FFT or DFT just results in the interpolation of more points on the same spectral result curve. You can zero-pad by a very large number if you just want a lot of smoothly interpolated points between the non-zero-padded FFT result.
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Identification of brain areas after language stimuli
Hi guys, I'm trying to solve a problem with my code in matlab. My goal is to identificate brain areas associated to language stimuli. After I performed some tasks, i arrived to plot the convolution between the BOLD signal and square signal (stimuli). Now, my goal is to perform a regression using regress between the convolution result (1x481 matrix) and a matrix containing the number of voxels of my brain images (92614x39) where the rows are the voxels and 39 are the MRI scans. So, the two matrices have different size and I have no idea how to perform a regression. Anyone could help me?
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I asked a question earlier that might have been too specific so I'll ask again in more general terms. How does error get propagated backwards through a pooling layer when there are no weights to train? In the tensorflow video at 6:36 https://www.youtube.com/watch?v=Y_hzMnRXjhI there's a GlobalAveragePooling1D after Embedding, How does the error go backwards?
A layer doesn't need to have weights in order to back-prop.
You can compute the gradients of a global avg pool w.r.t the inputs - it's simply dividing by the number of elements pooled.
It is a bit more tricky when it comes to max pooling: in that case, you propagate gradients through the pooled indices. That is, during back-prop, the gradients are "routed" to the input elements that contributed the maximal elements, no gradient is propagated to the other elements.
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I am reading in Feature Extraction in Medical Imaging field, especially in brain tumor detection, and I had found the above feature extraction and I don't understand the difference between them.
The various features that can be calculated from the co-occurrence matrices (C) are inertia (contrast), absolute value, inverse difference, energy, and entropy. Contrast is the element difference moment of order 2, which has a relatively low value when the high values of C are near the main diagonal. Energy value is highest when all values in the co-occurrence matrix are all equal. (Sigma(i) Sigma (j) cij^2). Entropy of the image is the measure of randomness of the image gray levels. ( -1*Sigma(i) Sigma (j) cij^2 * log cij^2 (with base 2))
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i have a picture with salt and pepper noise added to it. my task is to remove it using these functions :
You may use matlab functions ginput, to find specific points in the image (of course in the script you should hard code the coordinates you need).
You may use conv2, fft2, ifft2, fftshift etc.
You may also use median, mean, max, min, sort, etc.
As far as i can tell. i can use MinMax filter or Median Filter.
my task is to get the best results! so far i think Median filter will get me that.
is median filter really better than doing MinMaxMinMax.... ? is there a better way to get better results?
A median filter is a great way and text book approach to address salt an pepper noise. Matlab offers a function medfilt2 for this purpose, but of course you can also code your own by calculating the center intensity of a 3x3 window w using median(w(:)).
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I'm trying to minimize this function (by A):
argmin_A (||L(A)||^2 + a||A-B||^2*)
where:
A is a MxN image L is the Laplacian Operator
||.|| is the usual norm (Frobrenius)
a is a weight parameter
B is a matrix of size (M+2*k)xN
where k is an integer parameter.
(*) indicates that we just consider the pixels in the boundary (we want to preserve in A the pixels in the boundary of B).
Maybe the problem has a trivial solution, but I'm absolutely blocked.
If you need more details, it's (4) equation in this paper.
I will be very grateful for any help provided.
Without looking carefully at that paper, gridfit does essentially that, although it does not employ the boundary conditions you ask for. You could certainly write what you want however, or you could simply apply a weight to the boundary points. This is not a difficult problem, but in order to solve it efficiently, you would want to employ the sparse matrix capabilities of MATLAB.