Your interpretation is correct: directional derivation operators highlight variation in a given direction. Here, you use the $2$-point discrete derivative in the $x$-direction (along image rows). It may emphasize vertical features.
First, such operators indeed extend the initial image range. However, one often uses the absolute value of the derivative to ...
Figures that the same day I post a bounty I answer my own question.
The answer to this question is dead/speckle like pixels. For a fixed number of dead pixels of a given brightness, the smaller the relative shift between the two images, the fewer dead pixels that are needed to cause the (0,0) pixel to spike. It can take fewer brighter dead pixels to have ...
A fundamental book on image processing for electrical engineers is
Two-Dimensional Signal and Image Proccesing_Jae S. Lim
A highly recommended one, again, for electrical engineers is
Fundamentals of Digital Image Processing_Anil Jain
A hands-on book on basic practical image procesing is
Principles of Digital Image Processing_Wilhelm Burger
So the best discussion I can point to is in this PDF on Charles Poynton's site.
The PDF goes on to much more detailed explanations of each color space, and how to convert between them. It's a bit longer than I want to type out (or cut-and-paste images of). :-)
The goal is to predict a genetic distance of species from the spectra. For instance if the genetic distance of species 1 and species 2 is very height I would expect a different spectra. If the distance is 0 or very low,m the spectra are the same /very similar.
In that case, defining the distance between two spectra based on the spectra makes no sense – you ...
Looking at Intel - An Investigation of Fast Real Time GPU Based Image Blur Algorithms By Filip Strugar it looks like the Kawase kernel is just a way of implementing a linear kernel quickly, but in a way that constrains the kernel somewhat.
This means that you could make such an algorithm. Either choose a set of spreads and adjust their weights (if that is ...
Define a level function curve (L) like this:
$$ L(x,y) = C_0 \cdot x + C_1 \cdot y + C_2 \cdot x^2 + C_3 \cdot xy + C_4 \cdot y^2 .... $$
It has no constanst term. Now, find the best fit to the level curve $L(x,y)=1$. This will yield the values for the coefficients.
Normalize the coefficients so the gradient is of a magnitude you like.
Find the level ...
Detail in images require higher frequency basis functions. The frequency in this case is measuring fluctuations in intensity as a distance is traversed. With a lot of detail, a lot of fluctuations, thus higher frequency.
Tamp down the higher frequency and you lose detail, i.e. the image blurs.
The dampening is measured (described) best on a log scale.
Not sure how you do it in python, but the idea can be as simple as the following:
After you select a random point on the edge pixel, calculate the horizontal gradient and the vertical gradient with this pixel at the centre of the gradient filter.
The width of the kernel for the gradient filters will depend on the maximum width of the edges. For example if ...
I'd try a very "tinkery" approach here:
Erode the image, so that the black area is shrunk by a fixed radius of pixels from its border (say, 5px).
Dilate the resulting image by the same amount
measure the amount of difference between original and processed image.
The idea is that something that is a locally convex border doesn't suffer through erosion (it's ...
Frequencies in this context just means how fast the intensities change in the greyscale images as we move in the plane of the 2-D image.
As you might be knowing the edges or boundaries or outlines of objects in the images are composed of high frequency elements. Why? Because in order to show the outline of an object in the image, the intensities at the ...
"Need" or "Want"?
Q1: The second derivative avoids the problem of a gradually changing color being "greyish".
Here is one:
After advice about detecting focus quality of objects in a photo detected using YoloV3
It is based on how planar the color. Very planar means no edge.
There are many, many other variations of these techniques possible.
I find that pretty well-written; just as you can have time signal that has frequency properties, you can have a spatial signal that has frequencies. That's a pretty important concept in image descriptions and processing!
You can iterate for each channel:
data = double(imread('lena.jpg'));
data = data/255; % Potentially optional
dataFilt = zeros(size(data));
nChan = size(data,3);
kernelFilter = ones(11,11)/121;
for iChan = 1:nChan
dataFilt(:,:,iChan) = filter2(kernelFilter,data(:,:,iChan));
A signal is just a varying quantity over an axis.
Think of a greyscale image.
Think of a single row of that image. Can you see how the intensity evolves over the distance on the horizontal axis?
In an image, you get two axes (x and y), whereas in sound, you just get one.
In (mono) sound, your instantaneous intensity is the only quantity varying. In an ...
The Monet image appears to have a much higher entropy. Whereas the Mondrian image looks like it could very likely be compressed losslessly into a lower number of bits (depending on the compression method) due to a lower informational entropy.
Some references on image sharpness metrics:
Encoding Visual Sensitivity by MaxPol Convolution Filters for Image Sharpness Assessment, IEEE Transactions on Image Processing, 2019
A Fast Approach for No-Reference Image Sharpness Assessment Based on Maximum Local Variation, IEEE Signal Processing Letters, 2014
Image Sharpness Assessment Based on Local Phase ...
Basically each pixel is a realization so all you need is to work in the 3rd dimension (Though you can also get better by using the Spatial Data).
So the trick here is to use the multiple images to estimate the Mean (True value) of each pixel and then calculate the STD on all samples (numRows * numCols * numRealizations).
Assuming we have single ...
What you are looking at for this problem is a combination of "Image Segmentation" and "Spatial Analysis".
The image segmentation part of this particular task is very simple because each pixel "class" is already labeled by a colour. You might need an additional labeling step because (for example) the blue class contains two disjoint areas, but this is easily ...
If you don't want to use the PyPI package for bm3d, you can use ffmpeg and run the bm3d filter as an OS command-
command="ffmpeg -i "+input_image_path+" -filter_complex bm3d=sigma=30/255:block=4:bstep=2:group=1:hdthr=10000:estim=basic /path/to/output/directory/output.png"
This takes lesser computation time.
What I resorted to was using the PyPI package, which is advertised here: http://www.cs.tut.fi/~foi/GCF-BM3D/index.html#ref_software.
I dug a bit in the source code, and found that I could perform BM3D, in the following fashion:
denoised_image = bm3d.bm3d(image_noisy, sigma_psd=30/255, stage_arg=bm3d.BM3DStages.HARD_THRESHOLDING)
There are ...
Using scipy you can easily:
bandpass signals, for instance with scipy.signal.butter
obtain a spectrogram, using for instance scipy.signal.stft (or LTFAT, The Large Time-Frequency Toolbox (LTFAT) in Python)
scale the values of this 2D array so that each channel has values between $0$ and $1$
reshape the array into a $560\times 420$ grid, eg with scipy....
The spatial resolving capability of an optical system refers to its ability to distingusih between closest (and possibly tiniest) details. The distance between a pair of lines, yields a measure of how small the distance between them can be while they are still sperated from each other. Resolution depends on several factors such as brightness level, color, ...
YUV, YPbPr and YCbCr are all basically same, but just with different scale factors for the signals. YUV signals are used when encoding/decoding analog composite video, YPbPr signals exists at analog component video interface and YCbCr signals are digital numbers. There is brightness (luma) channel Y and two color difference signals (U and V, Pb and Pr, Cb ...
YUV is basically a member of the television industry video-image color formats and was created during the transition of black & white broadcast into color. A compatible color broadcast was needed that could still be received by the existing B&W receivers, yet could also enable color reception with (then) new color TV receivers. The broadcast systems ...
If your image is modeled as an image which is noisy, blurry and heavily decimated the optimal thing to do is estimate the image given that model.
The model is well defined in @Laurent Duval's answer.
I'd remark that in most real world cases the blurring is spatially variant hence it can't be modeled by convolution (Well, it is a generalized convolution).
This is the list I'd recommend:
Rafael C. Gonzalez, Richard E. Woods - Digital Image Processing
Great introductory book. Well written, a lot of examples. Though it is not deep in any of the fields.
Alan C. Bovik - The Essential Guide to Image Processing
A comprehensive book on many image processing related subjects. Gret book to skim through.
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 ...