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Focus set based reconstruction of micro-objects

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Focus set based reconstruction of micro-objects- http://vision.eng.shu.ac.uk/mechrob04/MechRob-paper.pdf Focus set based reconstruction of micro-objects Jan Wedekind 13.9.2004 MiCRoN http://wwwipr.ira.uka.de/~micron/ MMVL http://www.shu.ac.uk/mmvl/ People: Balasundram Amavasai, Manuel Boissenin, Axel B¨rkle, u Fabio Caparrelli, Arul Selvan, Jon Travis microsystems & machine vision lab -1- MiCRoN http://wwwipr.ira.uka.de/~micron/
Focus set based reconstruction of micro-objects- http://vision.eng.shu.ac.uk/mechrob04/MechRob-paper.pdf micro-vision and micro-robotics Closed-loop control • Estimate pose of manipulator in real time Task planning • Estimate pose of known micro-objects • Recognize obstacles for avoiding collisions • Provide data for determining gripping points microsystems & machine vision lab -2- MiCRoN http://wwwipr.ira.uka.de/~micron/
Focus set based reconstruction of micro-objects- http://vision.eng.shu.ac.uk/mechrob04/MechRob-paper.pdf present state of micro-vision characteristics • Limited depth of focus • Teleoptical settings vision problems • unstable feature extraction • limited depth of view ⇒ most standard approaches are failing microsystems & machine vision lab -3- MiCRoN http://wwwipr.ira.uka.de/~micron/
Focus set based reconstruction of micro-objects- http://vision.eng.shu.ac.uk/mechrob04/MechRob-paper.pdf spin images (Andrew Johnson 1997) microsystems & machine vision lab -4- MiCRoN http://wwwipr.ira.uka.de/~micron/
Focus set based reconstruction of micro-objects- http://vision.eng.shu.ac.uk/mechrob04/MechRob-paper.pdf spin images (Andrew Johnson 1997) microsystems & machine vision lab -5- MiCRoN http://wwwipr.ira.uka.de/~micron/
Focus set based reconstruction of micro-objects- http://vision.eng.shu.ac.uk/mechrob04/MechRob-paper.pdf depth of focus • Acquire focus set of images g(x1 , x2 , z) • Compute local sharpness measure s(x1 , x2 , z) • Compute depth map d(x1 , x2 ) := argmax s(x1 , x2 , z) z∈Z microsystems & machine vision lab -6- MiCRoN http://wwwipr.ira.uka.de/~micron/
Focus set based reconstruction of micro-objects- http://vision.eng.shu.ac.uk/mechrob04/MechRob-paper.pdf systematic error test with micro-ridge • Algorithm fails when contrast low • Systematic errors • Trade-off between resolution and stability ⇒ Filter-bench image index z insufficient contrast PSF d c b a pixel−position in image y d c systematic error microsystems & machine vision lab -7- MiCRoN http://wwwipr.ira.uka.de/~micron/
Focus set based reconstruction of micro-objects- http://vision.eng.shu.ac.uk/mechrob04/MechRob-paper.pdf multiscale approach s maximum sharpness • Recursive filtering • Not real-time efficient z large filter kernel • Complexity s (N = number of pixel) – O(N ) time – O(N ) memory z √ 2 medium−sized filter kernel – 3 N O(N 3 ) s parallelisation • No systematic error image (x1,x2) z small filter−kernel microsystems & machine vision lab -8- MiCRoN http://wwwipr.ira.uka.de/~micron/
Focus set based reconstruction of micro-objects- http://vision.eng.shu.ac.uk/mechrob04/MechRob-paper.pdf results microsystems & machine vision lab -9- MiCRoN http://wwwipr.ira.uka.de/~micron/
Focus set based reconstruction of micro-objects- http://vision.eng.shu.ac.uk/mechrob04/MechRob-paper.pdf results microsystems & machine vision lab - 10 - MiCRoN http://wwwipr.ira.uka.de/~micron/
Focus set based reconstruction of micro-objects- http://vision.eng.shu.ac.uk/mechrob04/MechRob-paper.pdf results microsystems & machine vision lab - 11 - MiCRoN http://wwwipr.ira.uka.de/~micron/
Focus set based reconstruction of micro-objects- http://vision.eng.shu.ac.uk/mechrob04/MechRob-paper.pdf results microsystems & machine vision lab - 12 - MiCRoN http://wwwipr.ira.uka.de/~micron/

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Focus set based reconstruction of micro-objects

  • 1. Focus set based reconstruction of micro-objects- http://vision.eng.shu.ac.uk/mechrob04/MechRob-paper.pdf Focus set based reconstruction of micro-objects Jan Wedekind 13.9.2004 MiCRoN http://wwwipr.ira.uka.de/~micron/ MMVL http://www.shu.ac.uk/mmvl/ People: Balasundram Amavasai, Manuel Boissenin, Axel B¨rkle, u Fabio Caparrelli, Arul Selvan, Jon Travis microsystems & machine vision lab -1- MiCRoN http://wwwipr.ira.uka.de/~micron/
  • 2. Focus set based reconstruction of micro-objects- http://vision.eng.shu.ac.uk/mechrob04/MechRob-paper.pdf micro-vision and micro-robotics Closed-loop control • Estimate pose of manipulator in real time Task planning • Estimate pose of known micro-objects • Recognize obstacles for avoiding collisions • Provide data for determining gripping points microsystems & machine vision lab -2- MiCRoN http://wwwipr.ira.uka.de/~micron/
  • 3. Focus set based reconstruction of micro-objects- http://vision.eng.shu.ac.uk/mechrob04/MechRob-paper.pdf present state of micro-vision characteristics • Limited depth of focus • Teleoptical settings vision problems • unstable feature extraction • limited depth of view ⇒ most standard approaches are failing microsystems & machine vision lab -3- MiCRoN http://wwwipr.ira.uka.de/~micron/
  • 4. Focus set based reconstruction of micro-objects- http://vision.eng.shu.ac.uk/mechrob04/MechRob-paper.pdf spin images (Andrew Johnson 1997) microsystems & machine vision lab -4- MiCRoN http://wwwipr.ira.uka.de/~micron/
  • 5. Focus set based reconstruction of micro-objects- http://vision.eng.shu.ac.uk/mechrob04/MechRob-paper.pdf spin images (Andrew Johnson 1997) microsystems & machine vision lab -5- MiCRoN http://wwwipr.ira.uka.de/~micron/
  • 6. Focus set based reconstruction of micro-objects- http://vision.eng.shu.ac.uk/mechrob04/MechRob-paper.pdf depth of focus • Acquire focus set of images g(x1 , x2 , z) • Compute local sharpness measure s(x1 , x2 , z) • Compute depth map d(x1 , x2 ) := argmax s(x1 , x2 , z) z∈Z microsystems & machine vision lab -6- MiCRoN http://wwwipr.ira.uka.de/~micron/
  • 7. Focus set based reconstruction of micro-objects- http://vision.eng.shu.ac.uk/mechrob04/MechRob-paper.pdf systematic error test with micro-ridge • Algorithm fails when contrast low • Systematic errors • Trade-off between resolution and stability ⇒ Filter-bench image index z insufficient contrast PSF d c b a pixel−position in image y d c systematic error microsystems & machine vision lab -7- MiCRoN http://wwwipr.ira.uka.de/~micron/
  • 8. Focus set based reconstruction of micro-objects- http://vision.eng.shu.ac.uk/mechrob04/MechRob-paper.pdf multiscale approach s maximum sharpness • Recursive filtering • Not real-time efficient z large filter kernel • Complexity s (N = number of pixel) – O(N ) time – O(N ) memory z √ 2 medium−sized filter kernel – 3 N O(N 3 ) s parallelisation • No systematic error image (x1,x2) z small filter−kernel microsystems & machine vision lab -8- MiCRoN http://wwwipr.ira.uka.de/~micron/
  • 9. Focus set based reconstruction of micro-objects- http://vision.eng.shu.ac.uk/mechrob04/MechRob-paper.pdf results microsystems & machine vision lab -9- MiCRoN http://wwwipr.ira.uka.de/~micron/
  • 10. Focus set based reconstruction of micro-objects- http://vision.eng.shu.ac.uk/mechrob04/MechRob-paper.pdf results microsystems & machine vision lab - 10 - MiCRoN http://wwwipr.ira.uka.de/~micron/
  • 11. Focus set based reconstruction of micro-objects- http://vision.eng.shu.ac.uk/mechrob04/MechRob-paper.pdf results microsystems & machine vision lab - 11 - MiCRoN http://wwwipr.ira.uka.de/~micron/
  • 12. Focus set based reconstruction of micro-objects- http://vision.eng.shu.ac.uk/mechrob04/MechRob-paper.pdf results microsystems & machine vision lab - 12 - MiCRoN http://wwwipr.ira.uka.de/~micron/