Vision is one of nature’s amazing creations that has been with us for hundreds of millions of years. It’s a key sense for humans, but one we often take for granted: that is, until we start losing it or we try and recreate it for a robot.
Many research labs (including our own) have been modelling aspects of the vision systems found in animals and insects for decades. We draw heavily upon studies like those done in ants, in bees and even in rodents.
To model a biological system and make it useful for robots, you typically need to understand both the behavioural and neural basis of that vision system.
The behavioural component is what you observe the animal doing and how that behaviour changes when you mess with what it can see, for example by trying different configurations of landmarks. The neural components are the circuits in the animal’s brain underlying visual learning for tasks, such as navigation.
Recognition is a fundamental visual process for all animals and robots. It’s the ability to recognise familiar people, animals, objects and landmarks in the world.
Because of its importance, facial recognition comes partly “baked in” to natural systems such as a baby. We’re able to recognise faces quite early on.
Along those lines, some artificial face recognition systems are based on how biological systemsare thought to function. For example, researchers have created sets of neural networks that mimic different levels of the visual processing hierarchy in primates to create a system that is capable of face recognition.
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