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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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If you’re looking for a more hands-on approach, you can start in computer vision research as early as university with an introduction to neural networks and deep learning and jump in with the rest. Now, I’ve mentioned this topic many times on this blog, and I am going to keep doing so. I think my advice is this: If you want to take part in the sub-set of research in which computer vision is applied, you need to be very careful to avoid the traps mentioned in this article. First, don’t get too involved with machine learning. This is like playing football with a mule. Secondly, if you’re just starting with research, then you must do some reading on artificial intelligence, computer science and computer vision (and other computer related topics), in order to understand what is going on and at what level it falls short. Then, go in, and build.