steganography image im image by using k-mean [closed] - image

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When I hide an image within a picture using the ( k-mean clustering) technique, I have a problem when scaling ( entropy major ) appears. I have the measurement ( 5.6) even though the size of the original image is ( 7.6) it appears to be very little and I do not know where the problem is

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Is there a way to apply Mahalanobis and Euclidean algorithms to this code? [closed]

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Excerpts can be found on https://www.kimbalite.com
Screenshot
I've tried applying Kondu's algorithmic formulas, but it's just not working.

How can I detect skewed lines in images using Go? [closed]

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I would like to use lines in captured photos to detect and correct image skew. How would I do this in Go? Most of the packages I've seen on github don't seem to support edge detection or some way to figure out a contiguous line or perhaps a couple markers like QR codes use.

is possible to "hash" a ssim from original image so i dont have to read the image again? [closed]

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Currently I started working on a personal project where you will constantly receive images, similar or not. Every new image received do I need to loop through absolutely every other image already saved to compare ssim?
The answer is No you can't recover the image from SSIM and Yes you'd have to loop over all images for comparison.
However you may look at image compression to have faster evaluation of the SSIM.
Some example images would be welcome for a better answer.

How is automatic image optimization performed? [closed]

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How is automatic image optimization performed? I'm looking for a general understanding or areas where I can read more into this, for example with the top image the bottom was automatically optimized using an image optimizing service within a few seconds.
In this example the saturation, brightness, hue and various other parameters are changed.
Original:
Optimized:
One possible method to produce a similar result is to convert to LAB colorspace, stretch the hisogram to full dynamic range separately for the L channel and the A and B channels (the same), then convert back to (s)RGB colorspace.
Input:
Output:

Render large point cloud set (More than 1 GB) in web browser [closed]

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Now I am struggling in rendering point cloud in WebGL. Once the file size get larger than 200 MB, the browser will crash. I am now using Points in Three.js to render my data.
Is there any way to optimise it or good library I can use to do this case?
This is very broad topic where you can do many ridiculous optimizations.
Take a look on this http://www.potree.org/

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