I realize that the general problem of object recognition can be arbitrarily difficult, but so is the general problem of text recognition: How can a computer det

I realize that the general problem of object recognition can be arbitrarily difficult, but so is the general problem of text recognition: How can a computer determine if a downward stroke is a one, or a lowercase L, or an uppercase I? And yet the text-recognition captchas have been broken -- not because the problem is easy but because captcha breakers have exploited artifacts of individual captchas to get a correct answer a modest percentage of the time. The 3D captcha (as the article author described it) is highly constrained -- a small library of objects in static poses -- so it has similarly exploitable artifacts. The captcha-breaking computer has no need to infer what an object would look like from another view if someone has already manually reproduced the library of models; in that case the problem reduces to identifying which models from the library are in the picture and what angle they are being viewed from. Although the problem is no doubt difficult, the silhouette strategy I described is similar to other published object recognition approaches known to work, e.g.: http://citeseer.ist.psu.edu/bandlow98recognising.html http://citeseer.ist.psu.edu/bandlow98recognising.html And the approach doesn't need to work perfectly: the captcha breaker is only interested in improving his chances of guessing correctly. If an automated approach only guesses correctly even 20% of the time, the captcha is effectively broken. In the case where two images of objects are very similar -- like your CRT vs. LCD example -- even a human would have difficulty differentiating. By definition that makes these objects bad for the captcha, so the captcha author would either leave them out of the library of objects, or he would need to make the captcha more tolerant of human error, which makes things easier for the captcha-breaker.