MimSaad
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Can a linear reconstruction in compressive sensing perform well?
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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 ...

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Average value of power spectral density
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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; ...

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Sub nyquist sampling, required number of samples for time sparse grouped signals
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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 ...

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How to unify the color and brightness measure of two given cameras?
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Histogram Equalization can help as well. It tries to have the same distribution of pixel values in both images.

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Is it possible to remove the non-random noise in the signal by averaging?
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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 ...

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PSD using MATLAB's $\tt pwelch$ algorithm window length and measure units
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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 ...

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linear-expansion filter for image (2D array)
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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, ...

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Find common features across images
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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 Convolution neutral network train(learn) separately (train different times)?
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This is called curriculum learning , and not only this approach is possible but also it is shown to be quite effective. A few years ago I also came up with similar idea. Basically the idea is, instead ...

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Understanding of Histogram-of-Oriented-Gradients
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Each histogram has 9 orientation bins and 4 histograms are concatenated hence 36 bins. I am not sure about HOG, but I assume alike SIFT algorithm, the original authors come up with trial and error for ...

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What information does the abs(normalized_image - normalized_quantised_image) carry?
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Assuming your image is a $K$ bits depth image, and the quantized version is a $3$ bits version, pixels of the difference image, represent $(K-3)$ least significant bits of pixels of the original image....

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Evaluate image symmetry
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One suggestion could be using image derivative. To get rid of effects of different shading and light, instead of BW, use derivative of the image in step 1.

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Comparison between 2 images in Frequency domain
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Fourier Transom of these two surely would be different, but remember Fourier gives a global representation(features) of the images and you need local features to compare those, it is like that you ...

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Why is scale space (DoG) needed to detect scale invariant features?
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You are asking how scale invariant property is achieved with DoG. In fact, it is not! Scale invariance is not achieved by DoG but only through G part of it, through Gaussian filtering and it has ...

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How should someone start to learn machine learning and computer vision for mobile applications?
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I'd like to suggest Professor Hoff and Professor Mubarak's great video lectures which are freely accessible here and here.

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What are some good signal and image processing blogs worth following?
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To complete the list I'd like to add a couple of weblogs that I personally follow: 1.Aishack (covers basic to advanced algorithms, intuitively and practically) 2.Py Image Search A blog, specialized ...

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Beginner's question on LPF, Need for Sinc and windowing
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Interesting question. So you are asking why we do not use FFT instead of commonly used filters (in your case Since function). In terms of computational complexity, FFT wins over digital filtering. ...

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How can I properly detect a ball moving in a film capture?
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First, for removing shadows try to normilize your RGB image prior to converting it to HSV color model. For more information on this regard see : http://aishack.in/tutorials/normalized-rgb/ In case ...

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What algorithms can automatically determine a 3D scene from one or a few 2D images?
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Since you are having a sequence of images from a scene or an object on your phone, I suggest to use a fairly simple method called Structure from Motion1.

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Create a color image in Matlab with sinusoidal patterns
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Simply do it for each color channel, I mean for Red,Green and blue, for each form a separate matrix and then concatenate those, to form a 3D color image. fs = 0.08; //FOR RED CHANNEL W = 256; H = ...

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Random Noise Removal In Images
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There are various methods for denoising image signals. With a preface about image sparsity I'm going to provide some matlab code. Images are sparse in nature, not in spatial domain, but in some ...

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DSP processor: some beginner's questions
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I'v have some experience with TI C55 and C6000 series DSPs, I'll give you some points, hope to be helpful. Programming each chip that came from a different company requires different softwares or ...

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Training and testing set for speech recognition
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There is a huge academic data set repository in http://academictorrents.com/ give it try, you might find something. Also, there is commeresial data base of Penn state university,here: https://catalog....

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Signal sparsity: with noise or without noise?
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Actually, the beauty of compressive-sensing lies in fact that it works for signals which are approximately sparse. Recovery algorithms in CS are based on L1 norm minimization and have moderately ...

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