“One thing that’s really exciting about studying vision is that it is so powerful that we don’t actually think about it from day to day. We just use it. It’s only when we pause and think, ‘wait, how do I actually do this?’, that we start to think about just how complex the problem is.”
Vision scientist James Elder, professor of psychology and electrical engineering and computer science at York University, is a member of York’s Vision: Science to Applications (VISTA) program. His multi-disciplinary team is working to better understand human vision and perception in order to train more intelligent artificial vision systems. He calls this biologically-inspired computer vision, building on the powerful and adaptive human visual system to teach computers how to interpret what they “see” in video input.
While human vision feels simple and automatic, the artificial intelligence (AI) community faces many challenges in replicating such a complex processing system in computers. For one, people perceive the world as being 3D, even with one eye closed. This remains something that machine vision struggles with, says Elder.
Another feature of human vision is that people can easily attend to a specific part of the scene to focus on and process. Machine vision still lacks that ability to pick out and be attentive to a narrow part of its field of view.
A third aspect that machines struggle with is being adaptive. For instance, new environments, or even changes in weather, can be enough to throw off machine vision. Human vision can readily adapt to the new context of changing locations.
Human vision is still so much more powerful than machine vision. Studying vision systems in this way gives a greater appreciation for just how complex human vision is, and how remarkable it is that these feats all feel effortless.
“Vision in particular is a really cool thing to study because we experience it every moment of our lives,” says Elder. “We can do this real diversity of tasks: recognizing people, appreciating beauty. It’s kind of automatic, and yet there are, in secret, these billions of neurons working away.”