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It is fairly straight forward, I cannot provide code here. First create a 2D blank array with the same size as your image, then crop the pixels in each rectangle and paste (substitute) it on the array in the same coordinates as image, do the second step for next rectangle until end.


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You can't if you have nothing to start labelling them with. As you describe it, there's simply no info available (not even about statistics) about the gender of your reference images, so there's nothing you can train. What you can do is use a different technique (as you say "biometrics say..." I assume there's a non-ML method of classification) and run it ...


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A sound wave is a pressure variation in a fluid (typically air) that varies with time and position. A microphone records this pressure variation: Positive means the local pressure is slightly higher than the static pressure and negative means it's slightly lower.


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It looks like you could use a low pass filter or another noise reduction filter. Then maybe an edge detection filter.


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My guess is that the assertion "the histogram of DCT coefficients of the un-compressed natural image has the smooth histogram ... However, the histogram of DCT coefficients of the compressed natural image has peaks and gap in it." is valid when one uses a compression method that uses a DCT and quantizes (sufficiently) it coefficents. This might not ...


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I think this is a conceptual issue. Your images are not fractals, they are approximations to fractals. Lacunarity is a property of the fractal curve, not of its approximation. The Koch curve has a specific lacunarity. Your program computes a measure that is inspired by lacunarity, from a given approximation to a fractal. This measure determines what ...


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You might be able to take these steps: Use Otsu threshold cv2.threshold(img, 0, 255, cv2.THRESH_BINARY_INV|cv2.THRESH_OTSU) to get the image in only pure white and pure black. (Thanks @HKoshdel point it out) Use Hough Transformation to find the curve lines in your image. (OpenCV only has the Hough transform for straight lines, you can write your own one ...


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HSV color model is more correlated to how humans see colored objects, as compared to RGB, YUV, Lab etc. We see what color the object is (Hue), how much is it saturated (Saturation) and how much white light is falling on it (Intesity).


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If it is a color camera, then typically there is a Bayer filter mosaic in front of the sensor: Figure 1. Bayer filter (with cutout) in front of a sensor. Image credit: Colin M.L. Burnett. Perhaps the grayscale mode pixels are simply the sensor element outputs, giving the repeating pattern for a solid color area. You can first debayer the image into a color ...


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It looks like that is a color camera with the Bayer pattern, and the color camera data is directly used as grayscale data, as if it was coming from a grayscale camera. If it is a color camera, the color camera data should be de-Bayerized first (and smoothed etc processing) to get color RGB data pixels as usual, and then the RGB data can be converted to ...


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As shown in your examples, you have used an odded-sized kernel. Those are very common in image processing. Plus, they can ease the visualization of what happens. If the kernel is $(2\kappa+1)\times (2\kappa+1)$, you can think of superimosing the center of the flipped kernel over each pixel, as known on the Matlab 2D convolution: So the equation becomes: $...


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Answer to the first post I guess that is pretty much dependent on the problem that you are dealing with. Boundaries are always a problem that needs extra care. In most applications, it would be an option to set those values to zero (and handle normalization factors of the filter appropriately). Other options would be to reflect the data, so that the index ...


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Ok I solved this problem using a simple solution. After I computed my convex defects in my countours, I performed a point polygon test and found the distance between the point in the convex defect and the hull. And I gave a threshold after looking at the distance of the points and all the sharp points were in the threshold greater than approximately 30. So ...


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You might find my, or the other answers, from here helpful. After advice about detecting focus quality of objects in a photo detected using YoloV3 I would have put this in a comment, but I can't include a pic there. Perhaps you could come up with a better verbal description of how to define the edges of interest.


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You can obtain pretty good results by just thresholding the image at a high intensity (since your text appears always to be white) and do a closing operation to close the gaps: # convert to grayscale img = cv2.imread('OCR.jpg') gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # threshhold ret,bin = cv2.threshold(gray,245,255,cv2.THRESH_BINARY) # closing ...


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The problem in your implementation is that it returns the tensor of float, while an image must be a tensor of int. Because of that, your rendering library, which I assume is matplotlib, cannot correctly plot an image. To fix that you need to specify the type of final_img explicitly. That is, you need to add a parameter dtype=np.int when creating final_img, ...


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SNR is good for images where intensity is equally distributed while psnr is good for those images where it varies a lot.so depending on the situation we can use any one of these.


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On a 2-sample vector, or $2\times2$ pixel images, Haar, Walsh-Hadamard, or Slant are exactly the same. They are all separable, orthogonal transformations with fast $N\log N$ algorithms. According to Slant Transform Image Coding, 1974, it is superior in appearance to the above, only inferior to Karhunen-Loève, which has no fast enough algorithms.


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In image denoising far more important then the noise distribution is the noise spatial correlation properties and the prior about the image. Let's try building some cases and dealing with them. The model is: $ y = x + n $ Where $ x $ is the clan image, $ n $ is the Poisson Noise (With mean $ \lambda $) and $ y $ is the noisy image. Noise Is Poisson ...


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Vanilla implementation of each method for image of size m x n and kernel of size k x l will yield: Spatial Domain Convolution - O(mnkl) as for each pixel in the image we do kl multiplications (Additions are discarded). Frequency Domain Convolution - O(mn log(mn) + mn) as the complexity of the FFT is mn log(mn) and we add the multiplication (You could add ...


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My (lazy) option is to dig into the Matrix cookbook (pdf), by Kaare Brandt Petersen and Michael Syskind Pedersen, namely section 2.4 Derivatives of Matrices, Vectors and Scalar Forms. They have a whole section on complex matrices as well.


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The easiest approach would be writing each case using Matrix Form of the convolution. In this answer we assume the discrete convolution is applied only on valid support (Matching MATLAB's valid parameter for the convolution). Namely, given $ x \in \mathbb{R}^{m \times n} $ and $ h \in \mathbb{R}^{k \times l} $ then $ h \ast x \in \mathbb{R}^{ \left( m - k + ...


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HPF or LPF linear operators. It means that an operation at pixel position i, j will only be affected by neighbors within the window size of your filter. Pixels outside the filter will not play a role - in other words, the effects of any filtering operation is localized around its support. Typically, if the image size post filtering is not a concern, we 0 pad ...


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