Edelstein's checkerboard... a non-AI perceptual illusion

David777

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checkershadow_illusion4med.jpg


Members that wish to understand where true Artificial Intelligence is heading beyond the current dominant simplistic media narrative of just a collection of digital data and processing might be surprised how our minds actually work. It is why future AI must also become more like creature machines.

Checkershadow Illusion
snippet:


The squares marked A and B are the same shade of gray.

The visual system needs to determine the color of objects in the world. In this case the problem is to determine the gray shade of the checks on the floor. Just measuring the light coming from a surface (the luminance) is not enough: a cast shadow will dim a surface, so that a white surface in shadow may be reflecting less light than a black surface in full light. The visual system uses several tricks to determine where the shadows are and how to compensate for them, in order to determine the shade of gray “paint” that belongs to the surface.

The first trick is based on local contrast. In shadow or not, a check that is lighter than its neighboring checks is probably lighter than average, and vice versa. In the figure, the light check in shadow is surrounded by darker checks. Thus, even though the check is physically dark, it is light when compared to its neighbors. The dark checks outside the shadow, conversely, are surrounded by lighter checks, so they look dark by comparison.

A second trick is based on the fact that shadows often have soft edges, while paint boundaries (like the checks) often have sharp edges. The visual system tends to ignore gradual changes in light level, so that it can determine the color of the surfaces without being misled by shadows. In this figure, the shadow looks like a shadow, both because it is fuzzy and because the shadow casting object is visible.

The “paintness” of the checks is aided by the form of the “X-junctions” formed by 4 abutting checks. This type of junction is usually a signal that all the edges should be interpreted as changes in surface color rather than in terms of shadows or lighting.


As with many so-called illusions, this effect really demonstrates the success rather than the failure of the visual system. The visual system is not very good at being a physical light meter, but that is not its purpose. The important task is to break the image information down into meaningful components, and thereby perceive the nature of the objects in view.
 

I would argue that color really is not that necessary 'to break the image information down into meaningful components.' Many natural occurring 'creature machines' (animals) are color blind but, perform quite adequately in the activities of their natural lives. However, if you are speaking about say a talking dog, indeed, maybe color perception would be important.
 
What you are relating is termed luminance. I just did a Windows Snipping Tool screen copy of the checkerboard and opened the result into Photoshop. Then changed from RGB mode, with Image...Mode...Grayscale that removed all the color hue components. The illusion affect was identical. Note with the Eyedropper Tool one can measure the pixel values as HSB levels and doing such shows everything on those 2 squares are indeed identical despite what our internal brain perception awareness model presents.
 

What you are relating is termed luminance. I just did a Windows Snipping Tool screen copy of the checkerboard and opened the result into Photoshop. Then changed from RGB mode, with Image...Mode...Grayscale that removed all the color hue components. The illusion affect was identical. Note with the Eyedropper Tool one can measure the pixel values as HSB levels and doing such shows everything on those 2 squares are indeed identical despite what our internal brain perception awareness model presents.

You could also do this as I've just done.

* Copy and paste the original image onto an MS Excel doc.

* Then copy the same image and crop it down around the A & B square. The B square is already starting to look darker.

* Then copy the same image again and put white boxes around the B square to isolate it further. B now looks the same shade as A in the original image, yet the three images of B and its grey scale are all the same.


Capture 01.JPG
 
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I'm missing something here. The placement of B presumes that it is in a lighter shade box than the dark A. If it were placed on
a dark box, it would be presumed dark. The fact that this is a checkerboard means there are built-in assumptions (light and dark boxes), whether or not it is the shade you are saying it is. The green column is there to confuse us, I think.
 

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