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16

You can use a standard inpainting algorithm. These algorithms replace marked pixels in an image with the pixel values that surround these marked pixels. The challenge here is to detect the grid (my tests seem to show that it is not a completely regular grid). So, I came up with this solution: from PIL import Image import requests from io import BytesIO ...


12

You'll need to understand the sampling theorem. In short, each signal has what we call a spectrum¹, which is the Fourier transform of the signal as it comes in time domain (if it is a time signal), or spatial domain (if it is a picture. Since the Fourier transform is bijective, a signal and its transform are equivalent; in fact, one can often interpret the ...


3

Overview: Detecting fake images is part of the research field of media forensics. This is an ongoing research niche. There already exists a fairly mature set of computational tools that can identify various types of image manipulations. They can also indicate the region in the image that has been altered. Concurrently, the improvements in photo-editing ...


3

I tried a really simple algorithm of running a 3x3 median filter on the R and G channels of that image and it works quite well. The python code is really simple: import scipy.signal as sp from scipy import ndimage image = ndimage.imread('Ahrnl.jpg', flatten=False) image_filtered = np.array(image) for i in range(2) : image_filtered[:,:,i] = sp.medfilt2d(...


2

If it is a true bw-image, i.e. it contains binary values only, you can use the Hough transform for circles (or a special one for ellipses). In the Hough space you will find maxima for the coordinates of the center and the radius or radii, respectively. If necessary, you can use a statistical test to proof the maxima since there are offen multiple local ...


2

Would these be limited to detecting periodicities that are aligned with one of the basis vectors of the n-D space? Well, if you say "sampling is sufficient", then it follows that the whole space is the span of the base vectors (in fact, you usually have them orthogonal). From that follows that a non-base-vector aligned oscillation can be represented by a ...


2

Is it possible ONLY by choosing the triggering sequence appropriately to increase noise-performance? Yes. The trick is to ensure that your transmitted sequences are as distant apart as possible from each other. Distant, here, is in terms of Hamming distance. This is the best you can do without more complex forward error correction codes and without any ...


1

Finding repeating but not periodically repeating patterns of an unknown template which you expect the algorithm to identify is a hard problem. Nature typically doesn't shut off all the other patterns that may be present for our convienance either. Signals have sources or generating mechanisms. It would help if you had some idea how your patterns are ...


1

This looks like an interesting problem. First, if your timing is evenly spaced, your idea of having two "pin" (aka calibration) points should give you an adequate linear relation between your two time scales. If you have more than two points then you can use linear regression to find a best fit. Second, I think you will want to use the raw values and ...


1

...a neural network that can decide wether a pattern produced by the movement of a hand near capacitive sensors is as expected, or random. The neural network is supposed to learn himself how the different channels react, in wich order, so i don't have to tell anything to the programm concerning the physical distance between two electrodes or whatever. ...


1

Yes, if applied properly, HOG is a good feature extractor even for OCR. I could point you towards an OpenCV sample and a MATLAB digit classification tutorial which do exactly that. However, if you like to get state-of-the-art performance, I would suggest deep learning methods, more specifically convolutional neural networks. Many works demonstrated the power ...


1

You need to be sure about: Noise power is bounded The power of the signal step is higher than 4 times that noise power. If the noise power is bounded and the change in the signal level is higher than 4 times that noise power (from the pictures it seems to be so, I mean, the step is more than twice the max value of noise), why not take the derivative of the ...


1

The simplest algorithm that is mathematically tractable is the matched filter. It is designed to find both the presence of (yes/no) and the location on a "target" in a "scene". However, the matched filter does not work incredibly well if there are rotations or scaling of the "target" in the scene relative to the target's image. Assuming the "target" is in ...


1

There's a lot of red flags in that paper that shouldn't have passed review. In fact, I'm a bit surprised – Mr. Ortiz-Catalan is a well-published researcher in a research group called "signals and systems" at a very prestigious university, and yet there's sentences like: Altering the dimension of the unit used for sampling (ADC resolution) to a high ...


1

As mentioned in another answer, image erosion is quite efficient to fill the gaps between the wall hatched lines. The key point is to choose a structure element that is big enough to allow connection to be done. You should use a structure element with both horizontal and vertical dimensions of at least half the distance between hatched lines (try different ...


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