My guess is you can find all rectangles using hough transform. OpenCV python returns a structure that has all rectangles. Then sort the rectangles and find take out the largest one and using the ...

Yes, at least in the above case it is possible. Though it might not be computationally as cheap as other methods such as least squares based curve fitting. I do not think injecting NaN gonna help, ...

As far as I understood you're seeking the best similarity measuring function. There are zillions of metrics for that purpose, in fact any clustering algorithm such as SVM, K-means and neural network ...

My understanding of your question is that you have a small misunderstanding here. In the definition it is not said time and space. A signal can vary in time or space. Some signals vary with time, as ...

It is not only noise. It is noise and distortion because this scenario modulates the signal as well. Abruptly changing amplitude of a signal is equivalent to multiplying the signal with a rectangular ...

Short answer, as a general rule NO, but depending on $g(t)$, in special cases YES, e.g. when $g(t)=0$ or $g(t)=Const$ ! Maybe if you find the Fourier of $Sign(G(f))$, e.g. $G(f)$ turns out to be ...

This is a good idea, I was pondering upon awhile ago. I drop some thoughts. A Linear(no activations) MLP with a single hidden layer performs the same or sometimes better than a Multi-layer model with ...

DFT does not decompose a signal into regular sinusoids, it decompose it up into complex exponentials. Therefore, the Fourier transform of a real value signal must be conjugate symmetric (has both ...

You should remove low frequency content of the diagram. Simply use moving average filter. find the optimum filter size that produces your desired result. Here is a matlab example code. t=0:0.001:1; ...

Curve fitting is another option after Fourier transform, you can fit a sum of sine function to your signal and the fitting coefficients show the amplitude and frequency of the signal. Check this: ...

It is necessary when reconstruction is considered. Simply imagine the case when $A = \Phi \Psi$ has a high coherence, e.g. all columns are exactly the same and indistinguishable, then there is no ...

Compressed sensing does not assume any distribution of non-zero elements in the input vector (signal), so it makes no difference if your non-zero elements are near each other or located uniformly on ...

Histogram Equalization can help as well. It tries to have the same distribution of pixel values in both images.

The random does not play a role when averaging is used to remove the noise. The distribution of noise does. Averaging works when the mean of the noise is zero. The assumption is that averaging noise ...

As far as I know there are two reasons: In sensing part: For practical implementation, usage of random matrices is hard, so people try to come up with simpler matrices that are fixed, this is thought ...

A sensing matrix maps input vector to measurement vector through linear wighted summation of input. What makes a specific matrix good, is application dependent. Now, both distributions more or less ...

First I explain how compressive sensing is leveraged into imaging reconstruction and then a little bit on how CS is deployed in an imaging hardware. Compressive Sensing For the sake of simplicity ...

Checking for RIP of a matrix is an NP-Hard problem which means it is not computationally feasible to accomplish. RIP is used in matrix design mostly in theoretical aspects. Stealing @David 's comments,...

In the context of a Gaussian pyramid, why is the image down-sampled separately although the numbers of pixels are decreased through smoothing already? After filtering the image the number of pixels ...

Regarding your questions: Is there a good rule of thumb for determining the length $L$ of the sections (second variable for the function)? $L$ (or window argument) specifies a window function to ...

There are two sources of sampling jitter in ADCs namely clock jitter and sampler jitter (S/H circuit). Clock jitter or clock phase noise is due to clock imperfections. So if you have an idea sampler, ...

I'd like to point out Heisenberg Uncertainty principle, based on which theoretical achievable precision is limited. It states that one can not measure two complementary qualities (e.t. here time and ...

I think this part in article is starting point of the confusion : See how the $I(x+u, y+v)$ changed into a totally different form $I(x,y)+ uI_x + vI_y)$? The author should have write a bit more ...

Simply resize the binarized image to a larger image but do not use any high order interpolation in resizing and instead use 'nearest neighbor'interpolation technique. In MATLAB: im2 = imresize(image, ...

If by feature you mean a group of pixels (like Goofy) I suggest to try SIFT + SVD. (http://weitz.de/sift/)

The second image you find online as you mentioned is magnitude and the first image must be either real part or the imaginary part (but not both). If you calculate complex value magnitude for each ...

You can use Hough Transform to find dominant lines in the image and then based on rho & theta parameters of the Hough transform, align your text. First you need to remove unnecessary details from ...

The sharp peaks are actually due to segmentation and concatenation, you need to have overlapping segmentations. The peaks occur on the edges of segments mostly. In following figures I tried to show ...