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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:
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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.
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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.
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I'm trying to open a .bin file in Matlab. The file is a xcat phantom of human body.
dims: 320,128,666
vxsize: 2.5,2.5,2.5
data type: 32-bit float, littleendian
And what I want is to open/see image of a specific slice of this file.
The XCAT phantom comes in raw format. Just use fopen, fread (with the precision flag set to the correct type) and fclose. Then you will likely need to reshape what fread returns.
Once you have the 3D image in MATLAB, then just slice it with indexes img(:,100,:)
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I want to enhance my image by using pso based gray level image enhacment.I send the algorithm but i dont understand how I get particle of my image.pso paper
You only need to carefully read the section B. Proposed methodology. It says something like this:
Now our aim is to find the best set of values
for these four parameters which can produce the optimal result
and to perform this work PSO is used. P number of particles
are initialized, each with four parameters a, b, c, and k by the
random values within their range and corresponding random
velocities.
So there you have your particle generation. Each particle is a set of 4 random values.
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I want to use bag-of-word feature on image classification, and how to visualize the codebook?
I use keypoint-sift then kmeans to do clustering.
e.g., http://fias.uni-frankfurt.de/~triesch/courses/260object/papers/Fei-Fei_CVPR2005.pdf (figure 4)
The 174-word codebook is visualized by a patch. The paper mentioned they used two types of representation, one is 11*11 pixel patch and the other is SIFT descriptors. Fig 4 is a result based on the former presentation after a k-means clustering. They cannot visualize a codebook based on SIFT (a wired image as 174*128 ). Of course, we can get the closest SIFT in query and visualize a patch around corresponding keypoint. Hope it helps.