Difference between revisions of "Visual Systems for Robots"

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* change directory to directory with this training by cd
 
* change directory to directory with this training by cd
 
* launch tr1
 
* launch tr1
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 +
 +
 +
== Part 2: Training on 2D processing ==
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 +
* ''start'' ImageJ
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* ''open'' pic1.jpg
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* ''turn it grayscale:'' Image / Type / 8bit
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* ''descrease noise by threshold:'' Image / Adjust / Threshold
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**  ''select'' Black & White ''from menu and move with low and high end of range to emphasize the seen objects. Then'' Apply ''and close the threshold window''
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* ''make the image binary:'' Process / Binary / Make binary
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* ''if the picture contains now black object on white background, change it to white object on black background:'' Image / Lookup Tables / Invert LUT
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* ''try to eliminate holes in object by'' Process / Binary / Dilate ''and'' Process / Binary / Erode
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* ''try'' Process / Binary / Skeletonize
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* ''restart'' ImageJ
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* ''open'' pic1.jpg
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* ''start segmentation to 2 colors by'' Plugins / Segmentation / k-means clustering ''(select two colors)''
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* ''return to the opened'' pic1.jpg ''and three times apply'' Process /Smooth
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''then again parform the same segmentation''
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* ''compare the two segmented images and select better''
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restart ImageJ
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open pic2.jpg
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apply Process / Filters / Gaussian Blur / 1.0
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apply Process / Find edges
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apply Process / Binary / Make Binary
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apply thinning by Process / Binary / Skeletonize
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start Octave or Matlab
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change directory to directory with this training by cd
 +
launch tr2

Revision as of 06:58, 6 July 2010

by RNDr. Andrej Lúčny, PhD. (MicroStep-MIS, Ltd., Slovakia) presented at the Robotic Summer School 2010

Part 1: Training on input image mainpulation

  • start ImageJ
  • open pic1.jpg
  • Image / Color / RGB Split Split Channels
  • apply Image / Lookup Tables / Red to pic1.jpg (red)
  • apply Image / Lookup Tables / Green to pic1.jpg (green)
  • apply Image / Lookup Tables / Blue to pic1.jpg (blue)
  • close everything
  • open pic1.jpg
  • apply Image / Type / 8bit to get gray image
  • apply Process / Binary / Make Binary to get binary image
  • start Octave or Matlab
  • change directory to directory with this training by cd
  • launch tr1


Part 2: Training on 2D processing

  • start ImageJ
  • open pic1.jpg
  • turn it grayscale: Image / Type / 8bit
  • descrease noise by threshold: Image / Adjust / Threshold
    • select Black & White from menu and move with low and high end of range to emphasize the seen objects. Then Apply and close the threshold window
  • make the image binary: Process / Binary / Make binary
  • if the picture contains now black object on white background, change it to white object on black background: Image / Lookup Tables / Invert LUT
  • try to eliminate holes in object by Process / Binary / Dilate and Process / Binary / Erode
  • try Process / Binary / Skeletonize
  • restart ImageJ
  • open pic1.jpg
  • start segmentation to 2 colors by Plugins / Segmentation / k-means clustering (select two colors)
  • return to the opened pic1.jpg and three times apply Process /Smooth

then again parform the same segmentation

  • compare the two segmented images and select better

restart ImageJ open pic2.jpg apply Process / Filters / Gaussian Blur / 1.0 apply Process / Find edges apply Process / Binary / Make Binary apply thinning by Process / Binary / Skeletonize

start Octave or Matlab change directory to directory with this training by cd launch tr2

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