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MIT Machine Vision System Figures Out What It's Looking At By Itself
MIT's "Dense Object Nets" or "DON" system uses machine vision to figure out what it's look at all by itself. "It generates a 'visual roadmap' -- basically, collections of visual data points arranged as coordinates," reports Engadget. "The system will also stitch each of these individual coordinate sets together into a larger coordinate set, the same way your phone can mesh numerous photos together into a single panoramic image. This enables the system to better and more intuitively understand the object's shape and how it works in the context of the environment around it." From the report: [T]he DON system will allow a robot to look at a cup of coffee, properly orient itself to the handle, and realize that the bottom of the mug needs to remain pointing down when the robot picks up the cup to avoid spilling its contents. What's more, the system will allow a robot to pick a specific object out of a pile of similar objects. The system relies on an RGB-D sensor which has a combination RGB-depth camera. Best of all, the system trains itself. There's no need to feed the AI hundreds upon thousands of images of an object to the DON in order to teach it. If you want the system to recognize a brown boot, you simply put the robot in a room with a brown boot for a little while. The system will automatically circle the boot, taking reference photos which it uses to generate the coordinate points, then trains itself based on what it's seen. The entire process takes less than an hour. MIT published a video on YouTube showing how the system works.