Everything You Ever Wanted To Know About Computer Vision.

One of the major open questions in both Neuroscience and Machine Learning is: How exactly do our brains work, and how can we approximate that with our own algorithms? The reality is that there are very few working and comprehensive theories of brain computation; so despite the fact that Neural Nets are supposed to “mimic the way the brain works,” nobody is quite sure if that’s actually true.

The same paradox holds true for computer vision — since we’re not decided on how the brain and eyes process images, it’s difficult to say how well the algorithms used in production approximate our own internal mental processes.

On a certain level Computer vision is all about pattern recognition. So one way to train a computer how to understand visual data is to feed it images, lots of images thousands, millions if possible that have been labeled, and then subject those to various software techniques, or algorithms, that allow the computer to hunt down patterns in all the elements that relate to those labels.

So, for example, if you feed a computer a million images of cats (we all love them😄😹), it will subject them all to algorithms that let them analyze the colors in the photo, the shapes, the distances between the shapes, where objects border each other, and so on, so that it identifies a profile of what “cat” means. When it’s finished, the computer will (in theory) be able to use its experience if fed other unlabeled images to find the ones that are of cat.