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Releases: PtrMan/symVision

release 0.0.5

18 Apr 16:35
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  • way lower jittering (virtually none in pong)

Functional changes:

  • the source of the points is from ProcessA(points) instead of ProcessD(lines) for each edge direction. This "decouples" it from the logic to find the lines.

release 0.0.4

14 Apr 13:22
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NarConSimpleWorld
A NAR(in this case ONA) can control a agent in a virtual world (simplified pong or a simplified shoot'em'up game) which is rendered as a virtual map.
The vision pipeline does:

  • convolution for edge detection (8 directions)
  • sampling of edges as symbolic points (by threshold) into set X
  • recognition of points as lines (not strictly necessary)
  • rendering of points of lines back to black/white image
  • cropping of rendered image for unsupervised classifier C by proposal positions (derived from X)

generation of region proposals:

  • cluster points from set X by proximity
  • clusters are proposals, used for classifier C, have center and extend. Proposals are represented as AABB's

classifier C:

  • unsupervised (as usual)
  • online learning(as usual)
  • try to find closest match by distance of stored classes
  • if not found -> create new class with new class id
  • if found -> return class id

release 0.0.3

21 Nov 21:48
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working sufficiently well:
process A
process B
process C
process D
process E
process H

  • variable tuning so it can deal a bit better with more complex scenes

Additional features

  • use of process-E
  • narsese output for line intersections
  • merge lines of edge detectors

visualization

  • visualization of line primitives and intersections
  • drawing of primitives of line-edges

release 0.0.2

21 Nov 16:52
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working sufficiently well:
process A
process B
process C
process D

process H is used but it wasn't checked if it works sufficiently well

additional features:

  • process-D reinforcement of line detector as described by foundalis
  • process-D sampling by proximity

bugfixes:

  • fixed bug which lead to wrong confidence

release 0.0.1

20 Nov 18:31
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working sufficiently well:
process A
process B
process C
process D

features

  • recognition of endoskelton (as edges)
  • recognition of edges in different orientations (as edges)
  • conversion of edge detectors to line primitives