Summary:
The paper dealt with a system that combines strokes in a spatial way. It takes in a group of single strokes and calculates their general placement in the drawing area. Using this spatial data, it can identify relativity with other strokes and generalize the data into sets of known shapes. An example was a drawn face. It was required to have two eyes (a right and a left), two pupils, a stroke for the head's shape, a nose and a mouth. After those were drawn, anything could be added, such as eyelashes, an eyepatch, ears, or hair and the shape would still be recognized as a face. It accomplishes this by labeling things and searching through them with different search bounds. This speeds things up.
Discussion:
Biggest problem with this paper - no results. Honestly, I don't blame them, because the results have to be pretty low. I like the concept of the system, but the loose style of recognition really doesn't seem to mesh with me. It's almost like we're taking one step back in recognition and saying "are there 5 strokes there? OK, there are 10 strokes so it must be a face". It would be interesting to find some way to integrate the design for multiple domains, such as recognizing a face OR a boat, because right now it seems this idea is really uninspiring.
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1 comment:
The paper does a bit more than just counting strokes, mainly by focusing on the interactions between strokes as features. Yet, the features for interactions and the strokes themselves are a bit lacking. And I would have liked to see quantitative results rather than just some well-recognized sketches.
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