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It’s kind of a funny story. Back in 1966, Prof. Seymour Papert at MIT asked one of his graduate students to ‘plug a camera into a computer and describe what it sees’ as a summer project. I guess that graduate student must’ve had a tough time, since their project isn’t even close to completion till today - giving rise to the science of computer vision [The Summer Vision Project]. In a nutshell if we were to describe computer vision, we’d say - a interdisciplinary field centred around computer science that obtains ideas from neuroscience, physics and psychology and formulations from mathematical optimisation, artificial intelligence and signal processing to understand why the human visual system works, and ultimately replicate it. The funny part is that we’ve made leaps and bounds in replicating some tasks the human visual system does (with deep learning being the MVP here) but we’ve got very little understanding of why it works so well - and that’s an interesting problem to solve, where there is now a lot of interest from theoretical computer scientists as well as cognitive scientists. From an outsider’s POV, if you’re looking to enter the world of computer vision research, it will involve taking ideas from various domains, forming hypotheses around them, deriving conditions and proofs around their feasibility of operation, and finally programming experiments to see how good t work. If you like that sort of thing (or any sub-part of it), you’ll fit in well.
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This is what we are going for! Now let's jump into the examples: Let's start with a simple one: You have 3 screens, where each screen represents the whole picture; let's call it a “scene”. Now suppose, you want to give this picture to your computer, so you present it in our above example, to which you have given it the description “Cookie” — a picture that represents the cookie. First, your computer will need an image description. In other words, it needs an image! Now, this is the point where your computer starts to get smart, you saw that I just described it as follows: In our examples the computer will start getting familiar with its environment. It starts to map out a path that corresponds to the visual scene we gave it. This process of mapping, searching or associating the input/output with the inputs or outputs is called.
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