Tracking Rectangles

[Greg McKaskle]

I can't find the white paper on the NI site yet, but one should be posted soon that covers several approaches. One approach uses simple particle analysis to identify the ones most like hollow rectangles. Another approach is to use the line or rectangle geometric fit routines -- which are Hough implementations under the hood.

The paper actually uses NI Vision Assistant for most of the exploration, but does refer to the LV example when it comes to scoring and position/distance calculation. The LV example will also run directly on your computer, so your cRIO can run whatever, and the laptop can pull images directly from the camera that is on the switch.

Greg McKaskle

[BradAMiller]

I posted a copy of Greg's the Whitepaper here:

http://firstforge.wpi.edu/sf/docman/...ib/docman.root

This has a lot of good information about finding and tracking the 2012 vision targets.

Brad

[bhasinl]

Thank you Brad, this is perfect. Just three questions/comments for anyone:

1) On the PDF under the "Measurements" section concerning distance (page 9), it says that the blue rectangle width is 11.4 ft but half the blue rectangle width is 6.7 ft. I don't know who wrote this, but that seems like a typo.

2) Does the particle processing method only accurately find rectangles when it encounters them head on? Is the edge detection method necessary to find rectangles distorted by perspective?

• I'm assuming it's possible to use the edge detection method in Java by taking the NI Vision Assistant's generated C code and translating it (hopefully).

3) Are there any pointers you can give on how to process camera images on the laptop instead of the cRIO? We've never tried this before, but it seems worth doing.

Thank you again for your help.