A: I don't know of any that works too well (and invariantly, including recognizing mirrored images & different orientation etc... This is fairly important for a system functioning in a dynamic world, and it is obviously a tough nut to crack)
Well, "too well" is ambiguous... There are plenty of image classification algorithms with published precision and recall results on test databases. The Poggio et al software seems to work about as well as humans do on the task of classifying images based on very rapid viewing (e.g. recognizing if there is a cat in an image flashed very rapidly in front of you). This makes sense because they have simulated only feedforward connections. Question is whether, when they add feedback connections, they will get a system that works about as well as humans do on the task of classifying images based on more leisurely viewing. No existing software system, including Poggio's or Hawkins', can equal humans on this task. But anyway, in terms of practical performance on real images, Hawkins' is nowhere near the best image classification system out there. Its ultimate potential when further developed is a whole other, and deeper, question, of course. I find Hawkins' approach lies in a funny middle-area between computer science and brain science. Poggio's work actually tries to simulate visual cortex. Hawkins' incorporates Bayes net learning which is known NOT to be how cortex works, but in an architecture inspired closely by cortex ... tries in this way to emulate the essence of what visual cortex does. Whereas my inclination tends to be toward approaches that stray even further from brain science... -- Ben G ----- This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/?member_id=231415&user_secret=fabd7936
