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Seeing the Forest For the Trees

swframe writes "A new object recognition system developed at MIT and UCLA looks for rudimentary visual features shared by multiple examples of the same object. Then it looks for combinations of those features shared by multiple examples, and combinations of those combinations, and so on, until it has assembled a model of the object that resembles a line drawing. Popular Science has a summary of the research. I've been working on something similar and I think this accomplishment looks very promising."

4 of 64 comments (clear)

  1. Which Goal: AI or Cognitive Science? by Garrett+Fox · · Score: 1, Informative

    I've heard of various other approaches -- to two different things, and I'm not sure which one the researchers are mainly going for. Is the goal here to produce a useful vision system for AI, or to get a better understanding of how the brain works? It seems like while these are compatible goals, it's helpful to distinguish them and decide which you care more about.

    --
    Revive the Constitution.
  2. Re:This is a realization of David Marr's early wor by bezenek · · Score: 2, Informative

    You know, two people or groups can arrive at the same conclusion, because it was obvious in the first place. And why is it so appropriate? What if the work had been done elsewhere, would that be inappropriate or offensive?

    I should have been more specific in my first post.

    David Marr's vision book (published in 1982 after his early death in 1980) is considered a seminal work in understanding human visual processing.

    Marr was trying to describe how humans see. The new work at MIT is trying to allow computers to see. David Marr would be glad to see the developments, whether at MIT or elsewhere.

    -Todd

    --
    Omne ignotum pro magnifico.
  3. Re:A system can't just "learn" - does it use a GA? by Anonymous Coward · · Score: 1, Informative

    ?There's nothing particularly unique about genetic algorithms with respect to learning? All systems operate within the constraints set by their programming.

  4. And here is the link to the paper itself by S3D · · Score: 3, Informative

    Which should have been included into TFA from the start:
    http://people.csail.mit.edu/leozhu/paper/RCM10cvpr.pdf
    The main achievement claimed is that no image labeling or any additional data like viewport position was needed, the learning process was completely automated.