Register
A password will be e-mailed to you.

How does the stalking lioness sneak up on her prey so effectively? How does the gazelle detect the lioness and escape with his life before it is too late? Camouflage has evolved as a way of hiding in plain sight, but along with camouflage has come the ability to overcome it, to see the truth behind the visual deception. As better camouflage is discovered through evolution, so are more acute visual systems — a perceptual co-evolutionary arms race.

One of the ways an organism can detect another despite being camouflaged is through movement. When a camouflaged animal moves, it moves together as a coherent pattern, which may reveal its form against the background.

This study explores the mechanisms behind the ability to use coherent motion information to separate an object or organism form its background, even when it is otherwise perfectly camouflaged. Using mathematical and computational models, we show how the predictions of old models break down and fail to capture the nature of the human visual system. We instead present a model that responds like human vision, lending insight into how we might achieve this capability.

The advancement was made possible through a visual phenomenon called reverse-phi. In a reverse-phi stimulus, objects are seen to move in the opposite direction than they are actually moving. This happens when the dark parts of the image are made light, and the light parts of the image are made dark. Previous models (e.g. the Reichardt-detector), although they perceived reverse-phi, cannot detect any qualitative difference between a reverse-phi and a regular motion stimulus. However, humans are able to tell the difference — the two kinds of motion are perceptually distinct. We used this finding to generate an alternative model for which reverse-phi and regular motion have different signatures, and don’t lead to the same qualitative percepts.

In addition to being of scientific interest in understanding perceptual processes in general, this work could lead to new ideas about recovering sight to the blind, through the use of neuro-prosthetics, as well as developing automated computer-vision systems that can ‘see’ effectively in complex environments.

Original publication source

Leave a Reply

Your email address will not be published.