# Tag Info

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Your model is exactly a Convolution with Uniform Kernel where the output is what is called the Valid Part of the Convolution. In MATLAB lingo it will be using conv2(mA, mK, 'valid'). So the way to solve it will be using a matrix form of the convolution and solving the linear system of equations. Let's use the Lenna Image as input: We have a uniform ...

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A run-length encoder is generally going to do worse on two images than one, and it's already bad on one. As the other answers have elucidated, there's a spectrum of fidelity (or bandwidth) to transmit the clandestine data, but there's also a obscurity/security spectrum which depends on your intended/perceived adversary. As two opposite ends of this ...

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Below is an attempt to do what you're asking in Python. First, the dashed item: Then the sensor. It's uniform,so just comes out as black. Then the output of the sensor (convolve the thing to be measured with the sensor). Finally, the output of the deconvolution. Note that the output is not precisely the same as the input, but it's pretty close. Code ...

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

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Can we consider these noises as salt and pepper noise.? Is there something else that I am missing? several pixels getting erased to either zero or one -> yeah, that fits my definition of salt and pepper noise. However, there might be other definitions around, including assumptions of noise autocorrelation, and whether or not your noise fulfills these is ...

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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 point, i.e. $Mag_{i,j} = R_{i,j}^2 + I_{i,j}^2$ you get something similar to the following image. I = imread('cameraman.tif'); F = fft2(I); F_Centered = fftshift(F)...

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It’s helpful here to understand that there are two things involved with digital sampling: Of foremost importance is the sampling; secondarily, subsequent digitization allows us a number of conveniences. That is, sampling theory does not require digitizing—you can store the analog levels obtained by sampling a signal whose spectrum is below half the sample ...

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Many people, including Andrew Ng in his Deep Learning Specialization, emphasize the importance of domain knowledge and developing hand crafted features. Only then one can achieve significant improvements in performance. A. Ng clearly talks about how hand crafted features are nowadays looked down upon but in fact, are important. Fundamental concepts in signal/...

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Sampling is the process of making the x-axis (time) discrete and quantization is the process of making the y-axis (magnitude) discrete. You can sample without quantization (such as done with an analog sample and hold circuit). Quantization is introduced through rounding or truncation when the sampled analog signal is mapped to a digital representation. ...

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Quantization and sampling are different things and they are treated mathematically very differently. However they usually happen both at the same time. Anything represented "digitally" (i.e. as a series of numbers in a computer or a digital storage device needs to be both sampled and digitized. This is typically done with a process called Analog-to-Digital ...

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Here's something that should work: 1) Find the center of mass of all your points. 2) Sweep a radius around this center. 3) Measure a few metrics along each radius, generating a set of "periodic signals" 4) Take the DFT of the signals to determine magnification and rotation (as phase shifts). Off the top of my head and similar to this: Auto Detection of ...

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A Hough transform can certainly work. However, with position, scale, and orientation, you would have to use a 5-D Hough transform (2 DOF for position, 1 for scale, 1 for orientation), which might be computationally quite demanding. As an alternative, you might want to look at SIFT or one of its extensions like SURF. It sounds like they can be adapted to do ...

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Is there a way to convert or somewhat replicate RADAR data from a set of given high speed videos taken from multiple angles. Yes. But it is not going to be a walk in the park. The problem is quite ill defined but the task I have in hand is to reproduce RADAR data for a set of videos of soccer shootouts. I also have a corresponding file with the ...

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I do not have expertise or experience in image processing but find this interesting, so I created the graphic below to assist in finding the solution or establishing why this can't be done. If we knew the precise location and orientation of the cameras in 3d space, as well as their focal length and lens distortions (which could be calibrated against ...

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You'd basically need to do two things: find a 3D-model of your environment, and track the ball in that. This model needs to have knowledge of the materials involved. Since a video gives you no information on the involved object's radar reflectivity, scattering coefficients and other RF parameters, that's something that you'll have to enhance your model with ...

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So we have 2 neighborhoods (Which are Matrices) $v \left( {\mathcal{N}}_{i} \right)$ and $v \left( {\mathcal{N}}_{j} \right)$. How could we calculate the distance between them? One option would be the Euclidean Distance:  {\left\| v \left( {\mathcal{N}}_{i} \right) - v \left( {\mathcal{N}}_{j} \right) \right\|}_{2}^{2} = \sum_{k} {\left( {v \left( {\...

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My sugestions are based on simple approaches (and since I don't know the resolution of the images or if the watermark is always in the same position): Option 1. use a median filter with a "big" kernel. Option 2. extract the (near) black pixels (for example p<5) and attribute them a new value based on the nearby colors. (if you want you can only consider ...

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If you consider deep Convolutional Neural Networks to be a type of non-linear filter, and you have plenty of training data, then that seems to be the currently trendy identification/inference algorithm to try.

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In different applications, this parameter has different value that results optimum output. How I can find optimum smf value? What do the docs say the smooth function does? Write that down mathematically. As for any mathematical problem: you'll set up a formula for the "error" you're making (or a formula for the "goodness" you're achieving), and then you ...

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Here are a few tools you might find helpful for annotating and generating Ground Truth segmentation masks: Xyonix Vannot - Video Annotation Tool for Object Segmentation Best image annotation platforms for computer vision (+ an honest review of each) Playment.io's Video Annotation tool

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