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 Yβριδικές Εικόνες

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ΔημοσίευσηΘέμα: Yβριδικές Εικόνες   29/6/2012, 14:57

http://www.cs.brown.edu/courses/cs143/results/proj1/nmalkin/


Project 1: Hybrid Images


Nathan Malkin, September 2011
CS 143: Computer Vision

This project as a Twitter post


In this project, we create hybrid images: images that look differently depending on how far away from them you are. #bieber




Introduction and explanation


The effect


Look closely at the image above. What do you see?
Why, it's Justin Bieber -- the outline of his face is clearly visible.
Now move back from the screen. You can no longer make out the lines that defined Mr Bieber.
In their place, only the abstract shapes are visible.
Who is it? Why, it's a baby!

What's happening here?


This illusion exploits the "multiscale perceptual mechanisms of
human vision".
When viewing an image from close up, we are able to identify the
high-frequency parts of it (those with prominent and well-defined
edges), and they dominate our perception.
However, when viewing an image from a distance (or for very short
bursts of time -- on the order of 30 ms), the high frequencies of the
image cannot be perceived. We therefore perceive the image on the basis
of low frequencies alone.

Hybrid images use this by incorporating the high frequencies from one image and the low frequencies of another.
Then, when looking at the image from up close, the high frequencies dominate, and you see the first image.
But when you move away, only the low frequencies are visible -- and they form the second image.

Generating hybrid images


As explained above, hybrid images are obtained by combining the
high frequency values of one image with the low frequency values from
another.
It follows, then, that the first step in creating hybrid images is
separating the frequencies in each of the images.
This is done by creating Gaussian and Laplacian pyramids.

Building image pyramids


The Gaussian and Laplacian pyramids can be built by following these simple steps:


  • Take your source image and apply a Gaussian filter to you
  • Subtract the blurred image from the original. The result is the next level of the Laplacian pyramid.
  • Take the blurred image and scale it down (in our case, by a factor of 2). The result is the next level of the Gaussian pyramid.
  • Repeat as necessary, with the current level of the Gaussian pyramid serving as the source image.


Gaussian pyramid


Laplacian pyramid

Why did I just do that?


Applying a Gaussian filter
to an image achieves a blurring or smoothing effect.
The effect also corresponds to a reduction of the frequencies in the
image, since high frequencies occur in regions of contrast, and the
blurring reduces that contrast.
The successive application of the Gaussian filter therefore results
in an image that is dominated by lower frequencies (and is progressively
blurrier).

In contrast, images in the Laplacian pyramid were created by
subtracting the Gaussian-blurred images from the original (not blurred
or less-blurred) versions. This means that they encode the high(er)
frequencies that were lost due to blurring.

Reconstructing the image


It's an important observation to the Laplacian pyramid can be
used to reconstruct the original image from any level of the Gaussian
pyramid.

Suppose we had the final image in the Gaussian pyramid -- the
smallest and most blurry version of the original image. To get the
previous level of the Gaussian pyramid, we could upscale it (perhaps
using upsampling). This would produce the Gaussian-filtered version of
the previous level. How do we undo the blur?

Luckily, the information we need is encoded in the corresponding
level of the Laplacian pyramid. (Recall that we found it by subtracting
the filtered version from the unfiltered.) By adding it to the upscaled
image, we get the previous pyramid level.

This process can then be repeated until we reach the bottom level of the Gaussian pyramid. But that is just our original image.
Voilà! We have reconstructed our source image.

Creating the hybrid image


And now, the final step: making the hybrid image. To do it, we
will combine the low frequencies from one image with the high
frequencies of another.
We do this by taking the final (topmost) level of the Gaussian
pyramid and adding successive levels of the Laplacian pyramids to it.

At some point (this is our cutoff frequency), we switch over to the second image and add the remainder of its Laplacian pyramid levels to the result.
This means that it is only contributing to the high frequencies of the final image.

The result (to reiterate one last time) contains the low
frequencies from the first image and the high frequencies from the
second image. This is our hybrid image.

Adding color


All the operations above can be easily extended into the third
dimension, color. So, for example, the Gaussian filter is applied
separately to the red, green, and blue components of the image; each ise
downscaled and upscaled separately; and each pyramid level actually
consists of three images: the red, green, and blue intensity values as
separate two-dimensional images.

Results


Here are some images that have been generated using the method above.



John Quincy Adams, in the shadow of his father




A cat and a dog




The same image, in grayscale.




A donkey and a horse




Albert Einstein and Marilyn Monroe




The same pair of images, but now Marilyn Monroe is in the foreground.




A hidden message




Catman (Derek and Nutmeg)




The same image, but with color




Natalie Portman, as the Black Swan




Rhino/Car




Tank/Rhino




Tiger/Cat




Tiger/Cat (grayscale)




_________________
Cogito ergo sum.
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