March 2019
Intermediate to advanced
538 pages
13h 38m
English
We now understand how visual data enters the human visual system, and how our system processes it. The issue is that we still don't fully understand how our brain recognizes and organizes this visual data. In machine learning, we just extract some features from images, and ask the computers to learn them using algorithms. We still have these variations, such as shape, size, perspective, angle, illumination, occlusion, and so on.
For example, the same chair looks very different to a machine when you look at it from the profile view. Humans can easily recognize that it's a chair, regardless of how it's presented to us. So, how do we explain this to our machines?
One way to do this ...