5 votes
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Image Contrast Enhancement Threshold Selection

What you are looking for called Otsu Method / Otsu Thresholding. It solves an optimization problem to set the optimal threshold between two modal distribution.
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5 votes
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Equalization and then Sharpening vs Sharpening and then Equalization

Since the Histogram Equalization isn't Linear Operator the order does matter and the result of the doing one before the other changes the final result. Sharpening would work best when all the Dynamic ...
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  • 40.7k
4 votes

Histogram matching of two images using cdf

First you'll have to compute the histogram of one of the two images. H = hist(img(:), bins); Next find the cdf of the image: ...
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  • 270
4 votes
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Image Histogram - What Is the Interpretation of PDF

Well, If you model your image as a realization of a random variable generator then the Histogram is the best estimation (Assuming no other information like prior, etc..) you have for the PDF of the ...
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  • 40.7k
3 votes

Histogram Counts of a 2d Matrix

Are you looking for a specific function to perform this task? Otherwise this could probably be done with a simple for loop. Determine your unique rows using the function bins = unique(A, 'rows'); ...
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  • 451
3 votes
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Histogram matching (specification) in Python

If I understood right, you are stuck in matching a given histogram into a desired one and creating a new image from this matched histogram obtained by your filtering method. I would first suggest you ...
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2 votes
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Optimal image histogram for Human eye

Human eye perception is a complicated thing, the human perception tend to fail in different situations even in large changes of gray tones, also is capable of note minimal changes in the gray levels ...
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2 votes

Histogram Intersection with two different bin sizes?

I don't see any other way than to adapt one of the histograms such that both histograms have the same number of bins before performing histogram intersection. This will always involve some guesswork ...
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2 votes

float image to integer type image

As pointed out by Scott in comment, the quick'n dirty way of doing it is to just multiply by 255 and taking the integer part of the result. The actual answer is (unfortunately, it's a very common ...
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  • 3,889
2 votes

float image to integer type image

You should check the Envi documentation (a standard tool for working with multi/hyper spectral images) on the stretches it uses to fit multi/hyperspectral data into 8 bits for display. Here is the ...
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  • 1,071
2 votes
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Histogram estimation (from sub-set of image pixels)

I think random sampling approach seems to be not effective, since the statistical population (pixel intensities) distribution in images is heavily localized. There might be more scientific approaches, ...
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  • 1,906
2 votes
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Histogram Equalization

Assuming that in your question $T(r)$ denotes a map $:\mathbb{R}\to[0,1]$ and assuming that it is continuous and strictly increasing, then it is obviously an isomorphism among $r\in[0,1]$ onto its ...
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  • 738
2 votes

Why histogram equalization fail in enhance images for bimodal histogram?

Yes it is true. A simple histogram equalization may fail for certain image intensity distributions. In your case there are some bins with excessive number of pixels. For other methods, try for example ...
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2 votes

What Information Is Lost by Histogram Equalization?

Brightness. You can find more detailed information in here: Bi-Histogram Equalization with Brightness Preservation Using Contras Enhancement
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2 votes
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What Information Is Lost by Histogram Equalization?

The expectation of information is called entropy. The loss of information can hence be understood as difference in entropy between source and processed image, assuming no random effect was added. ...
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2 votes

MATLAB Image-Processing Toolbox: asking for a roadmap!

If the distinct contents/ingredients really have different colors than you just have to take a picture, always in the same location/distance, and then count the amount of pixels for each color. You ...
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2 votes

Lightweight test for bimodal distributions?

An easy way, especially one that is used in the detection and SNR estimation of PSK signals in a noisy signal, is based on stochastic moments of the received amplitude: The kurtosis can be used as ...
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2 votes

Lightweight test for bimodal distributions?

If the test is just to test the hypothesis that there is noise vs. the hypothesis that there is noise plus some bimodal signal, then I would compare the mean absolute value of the signal, or some ...
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  • 8,285
2 votes

How can I create a histogram showing the RMS amplitude for each frequency bin of an audio file

If what you want is to determine the amount of energy your signal has in particular frequency bands you can perform a FFT of your signal. For that you need to make sure that you have amplitude data ...
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1 vote

What Is a Weighted Local Histogram?

A weighted local histogram would mean filtering the image $I(x,y)$ with a localised filter $H(x,y)$ (gaussian in this example). The resultant image is the 2D convolution $Y(x,y) = I(x,y)*H(x,y)$. The ...
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  • 2,552
1 vote

From Uniform to 2D gaussian

The Gaussian distribution is separable. Apply your transformation to each coordinate separately and you will get a 2D Gaussian. If $G(x)$ is a 1D Gaussian, then $G(x) G(y)$ is a 2D Gaussian. Thus, ...
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  • 1,023
1 vote

Histogram Counts of a 2d Matrix

The consolidator function by J. d'Errico (a solid contributor to MatlabCentral, with heavily optimized and versatile code) can be quite useful here. For your ...
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1 vote

Entropy of enhanced image more than the original image

According to me, the above relation should always hold true, According to the data processing theorem (and here), this is always true. how can an enhanced image contain more information than the ...
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1 vote

How to detect whether a signal is unimodal or bimodal?

There are a few answers to a similar question over on Cross Validated.SE. One suggested answer is to use Hartigan's dip test. Another is to use the mixtools ...
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1 vote

How to gather similar objects in an image?

First, I personally wouldn't recommend you using background subtraction in general for tracking applications. There are way better methods like L1-minimzation of lasso problems, Kenalized Correlation ...
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  • 270
1 vote

Histogram Equalization Ignoring zero in Python

Actually, this is not a signal processing quesiton, and would have been better at stackoverflow. Anyway, you can use a look-up-table for this problem. The output value of a pixel does only depend on ...
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1 vote
Accepted

How to Generate a vector map for histogram equalization

Your code is halfway correct; it creates the cumulative distribution function (CDF), but the map isnt quite there yet. First you get the histogram using imhist(), but we want to convert this to ...
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  • 451
1 vote

MATLAB: get members of histogram populations EXCLUDING those inbetween

Assumption Your noise is Gaussian. Proposed solution Compute the histogram of your data. There will be two peaks, as in the histogram you've shown. Extract the approximate positions of the peaks ...
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  • 1,686
1 vote

CLAHE - Redistributing excess pixels among histogram bins

...
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1 vote

Noise in Digital image

If the noise is a constant background/remains in the same place, you could take various photos in the absence of any light to create an image for background subtraction. My image processing expertise ...
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