Computer vision: how a robot sees its environment

For a robot, a camera is not a pretty picture: it is a matrix of numbers that must be turned into a decision. Computer vision is the bridge.

The typical pipeline has layers. First, preprocessing: adjust lighting, reduce noise, sometimes convert to grayscale. A clean image makes the rest of the work more reliable.

Then, detection: find edges, shapes or colors of interest. Classic techniques (thresholding, contours) solve a surprising number of industrial problems without neural networks.

When the problem warrants it, learning models: object detection to recognize what is there and where. Here Python and its libraries make accessible what years ago required entire teams.

And finally, the most important part: the decision. Detecting an object is useless if the robot does not act on it: turn, stop, pick the part. The value is in closing the loop between what it sees and what it does.

What is fascinating about vision is that it combines my two worlds, data analysis and robotics, in service of a concrete action.