Understanding our object recognition task

Having a computer or robot recognize an image of a toy is not as simple as taking two pictures and then saying if picture A = picture B, then toy. We are going to have to do quite a bit of work to be able to recognize a variety of objects that are randomly rotated, strewn about, and at various distances. We could write a program to recognize simple shapes – hexagons, for instance, or simple color blobs – but nothing as complex as a toy stuffed dog. Writing a program that did some sort of analysis of an image and computed the pixels, colors, distributions, and ranges of every possible permutation would be extremely difficult, and the result would be very fragile – it would fail at the slightest change in lighting or color.

Speaking from experience, I had a recent misadventure with a large robot that used a traditional computer vision system to find its battery charger station. That robot mistook an old, faded soft drink...