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Those kind of algorithms are called Non Local algorithms. The most known algorithms of this family is the - Non Local Means which is a decent Noise Reduction (Denoising) algorithm. Until the Deep Learning boom, this approach has been extended and usually means working in the Patch Space of image - Patch Based Models and Algorithms for Image Denoising: A ...


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There are different color spaces. Each of them has different coefficients for calculating gamma-corrected luma from gamma-corrected RGB values. You have listed coefficients for calculating luma according to ITU-R BT.709, ITU-R BT.601 and NTSC color spaces. There are many others as well, like BT.2020 used in consumer TVs for Wide Color Gamut.


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For the 1-d case, I think that conv() is implemented in the direct domain while xcorr is implemented in the frequency domain. This indicates that conv will be faster for small kernels, while xcorr will be faster when inputs are equal size. I dont know if this still is the case, and if it extends to the 2-d case. Operations that allows for native single-...


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clearly, these are very similar images Both I and your arbitrarily picked measure for similarity would like to heartily contradict you, there. It was you who picked SSIM, Structural SIMilarity, as measure; if that measure doesn't describe your own idea of similarity well enough, well, you might want to define what similarity is and come up with a ...


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After having researched and learned a little bit more about the pinhole camera model, I think I know the difference between the camera (or image) plane and the virtual plane (or image). The virtual plane (or virtual image) doesn't physically exist, but it's only used to simplify the mathematical modeling and reasoning. How does it do that? The main ...


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They can. As Francesco mentioned, these problems can be solved with less correspondences. What makes the difference is how we formulate the problem. If we like a fast linear solution, then 8-points are required. For formulations using less number of points, the constraints are non-linear and typically involve either determinants or systems of polynomial ...


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The Viola Jones algorithm does the face detection for you using the Cascade Object Detector. %Read the image x= imread('C:\~\crowd.jpeg'); x=rgb2gray(x); %Face detection faceDetector = vision.CascadeObjectDetector; bboxes = step(faceDetector,x); The first two columns of bboxes gives the (x,y) coordinates of the faces detected If you want to label the ...


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If the camera has a shallow depth of focus and the background is mostly blurry and the foreground is only moving at one focal plane, why not use a simpler method, such as Template Matching? Since the printing head is changing its angle of view with respect to the lens as it moves in the $x,y$ dimensions, you might have to use more than one templates but ...


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Apart from the formulae, let us go back to their actual meaning, and how they are derived. Talking about convolution: this operation is inherent to Linear-Time-Invariant (LTI) systems. In other words: if you want to analyse a system that is linear, and time-invariant, or you want to apply a processing or a filter that does not vary in time or space then ...


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One of the most important classes of systems are Linear Time Invariant (or LTI) systems. These can fully described by either their transfer function or their impulse response (which are Fourier transforms of each other). If you apply an input signal to an LTI system and you want calculate the output signal, you can simply convolve the input with the ...


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When I read «bilinear interpolation kernel» in image processing, I assume that they mean bilinear image imterpolation: y(n+t) = (t-1)x(n) + tx(n+1) If you decide upon a fixed uniform upsampling factor and standard dsp «zero-fill then lowpass filter» upsampling, the convolution kernel turns out to be samples of a triangular waveform. -k


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I am not sure there are "best" image processing or computer vision books to buy. The topics covered can be very wide, and some can be better on some aspect (morphology, segmentation, denoising, etc.). Some sites recommend a hanful of such books, and link to their draft versions, as in 8 Books for Getting Started With Computer Vision. Indeed, a lot of ...


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It is indeed possible to formulate this setting in terms of matrix-vector products. First, let us re-formulate your $x$ (notice throughout that I use bold letters for vectors and matrices): $$x = \begin{bmatrix}\mathbf{x}_1 & \mathbf{x}_2 & \ldots & \mathbf{x}_8\end{bmatrix}$$ where $\mathbf x_k$ is the $k$ column of $x$. I define the vertically ...


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Let's rearrange \begin{align} x(n,m) * \left[ f_1(n,m) + f_2(n,m) + f_3(n,m)\right] &= x(n,m) + kx(n,m) - x(n,m) * kg(n,m)\\ x(n,m) * \left[ f_1(n,m) + f_2(n,m) \right] &= x(n,m) + kx(n,m)\\ &=(1+k)x(n,m) \end{align} It directly follows from the linearity of convolution that $f_1+f_2=(1+k)\delta_{n,m}^{(N \times M)}$, where $\delta^{(N\times M)}$ ...


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The solution was to take Fourier transform 3D for each slice, then to chose only the 2nd component of the Transform to transform it back to the spatial space, and that's it. The benefit of this is to detect if something is moving along the third axis(time in my case). for sl in range(img.shape[2]): #-----Fourier--H1---------------------------------------...


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You defined to compute an RMS. I don't know why you choose it, but OK. How can we probe it as a contrast measure? If you are considering images with positive pixel values, and consider that multiplying them by a scale factor $a>1$, you extend the range, hence the contrast, or if $a>1$, you shrink the range, hence the contrast again. How does the RMS ...


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