MimSaad
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Why calculate negative frequencies of DFT?
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7 votes

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 ...

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Can compressed sensing be used instead of intepolation for missing values?
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5 votes

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, ...

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Signal and Space/Time
4 votes

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 ...

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Is it theoretically possible to perfectly quantize a continuous signal?
4 votes

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 ...

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Do we really need two cameras for 3D vision?
4 votes

First of all, our brain does not only rely on our stereo visionary system to estimate the depth. There are many cues in a image scence for depth estimation, of which stereo, vision belongs to a sub-...

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Computer exercises and solutions in signal processing
4 votes

"Digital Signal Processing: A Computer-Based Approach" by Sanjit Mitra is what you need I guess, especially the exercises at the end of each chapter. There is a booklet on the Internet again by Mitra,...

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Removing Noise from Dental Radiography
4 votes

As far as I understood, by image derivation you mean extracting edges. I would recommend to filter the image by a relatively large Gaussian filter. If computational cost of image derivation is ...

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Why do we need deterministic measurement matrices in compressed sensing?
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3 votes

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 ...

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Sensing matrix for compressed sensing
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3 votes

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 ...

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What are the practical constraints on designing Sensing matrix in compressed Sensing?
3 votes

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,...

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How can I track detected objects from frame to frame
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3 votes

Object detection is relatively a heavy task as you've notice. Detecting the object (in your case human face) in every and each frame would be cumbersome and computationally immense. Therefore, you ...

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Compare feature vectors of an image in MATLAB
2 votes

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 ...

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Single Pixel Camera - Compressive Sensing
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2 votes

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 ...

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Detecting the rotation of digits in an image
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2 votes

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 ...

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Differences between low-pass, band-pass, notch filters
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2 votes

First,note that your drawn filter responses are one dimensional filters. Notch filter, selectively suppresses some frequency bands that are not of interest. One of the well known applications of ...

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Is the basis of the sparse signal assumed known in compressed sensing?
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2 votes

Compressive Sensing is an approach to reconstruct sparse signals from incomplete set of measurements. In doing so, we need to know $Ψ$ to recover $x$, don't we? yes, we do. But if we know $Ψ$,...

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OpenCV - What type of a result does cv2.goodFeaturesToTrack() return?
2 votes

Since computing the optical flow for the whole image pixels is computationally immense, it it preferred to compute optical flow only around feature points. This method is called sparse optical flow. ...

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What is the physical meaning of the convolution of two signals?
2 votes

The physical meaning is a signal passes through an LTI system! Convolution is defined as flip (one of the signals), shift, multiply and sum. I am going to explain my intuition about each. 1. Why we ...

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Does the use of a sparse basis in Compressed Sensing imply the need to have access to all the information beforehand?
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2 votes

It seems to me you have a little misassumption here. During the sampling only $\Phi$ matrix is applied to the signal $x$, which is resulted in measurements vector $y$, $y=Φx$ . Later, in ...

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What algorithm does Google use for its "Search By Image" site?
2 votes

The other interesting approach which seems to be neglected in above answers is Deep Convolutional Neural Networks. It seems Google is using it right now for its image search engine and its translation ...

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How to Enhance this finger print?
2 votes

Two steps, do a simple histogram equalization to make the brightness a little bit more even. Then use canny edge detector (as suggested by Marcus Muller). matlab code: I=imread('Your Image'); G1=...

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Histogram estimation (from sub-set of image pixels)
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2 votes

I think random sampling approach seems to be not effective, since the statistical population (pixel intensities) distribution in images is heavily localized. There might be more scientific approaches, ...

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Compressive sensing: numerical generation of RIP matrices
2 votes

You can't prove RIP through numerical exploration of all possible cases. If you are interested in numerical analysis I suggest to use Coherence instead, however Coherence is not as strong condition ...

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Applying Lowpass (LPF) and Highpass (HPF) Filters to an Image in Frequency Domain in MATLAB
2 votes

I spatial domain, simply convolve masks like averaging, or guassian with image to get low pass filtering: LowpassMask=(1/9)*ones(3,3); % Averaging mask of size 3*3 Filtered=conv2(image,...

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OpenCV: Can't find large rectangle contour
1 votes

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 ...

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Sudden loudness changes producing unwanted noises?
1 votes

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 ...

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$\int_{-\infty}^{+\infty} |G(f)| \,e^{j2\pi ft}df=|g(t)|$?
1 votes

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 ...

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Can I decompose a sine signal into its component sine waves?
1 votes

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: ...

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Why is incoherence important for compressive sensing?
1 votes

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 ...

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Gaussian pyramid: why needs the image to be downsampled
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1 votes

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 ...

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