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Kuliah Umum Kapita Selekta ITTelkom, 13 Oktober 2012


Peb Ruswono Aryan
http://about.me/peb
   Peb Ruswono Aryan
   Edu :
     S1 Informatika ITB, 2002-2007
     S2 Informatika ITB, 2008-2010
   Work :
     R & D, Digital Mark Reader (DMR), 2007-2010
     Dosen, STEI ITB, 2010-present
   Interest :
     Visualization
     Image Processing and Interpretation
+   =

   Multiscale tricks
   If it must fail, it must fail early
   Iterative Reduction by (re-)Weighting
   Statistical Model of Shape & Appearance for
    constrained search [Tim Cootes]
   Shape : point location
   Appearance : image patch around point
   X = Xavg + StdDev  X = Xavg + Pb
   P : PCA , b : parameter (reduced search space)
   Steps :
     Foreach point in shape model:
      ▪ Find most similar patches along normal direction
      ▪ Test if current shape fit with learned model
      ▪ Continue until convergence
   Hx = z, given x & z, find H
   Set of pair of matched points
   Overdetermined system of linear equation
   RANSAC
     Pick small subset
     Calculate H
     Check whether H is supported by the rest of
      population
     Select best H with biggest support (minimize
      error)
Color
consistency!
   Poisson Equation
   Laplace(source[mask]) = Laplace(dest[mask])
   Boundary Condition : destination pixels
   Discrete Poisson Operator
   Sparse Matrix Linear Solver
     Direct
     Iterative Relaxation


   http://pebbie.wordpress.com/2012/10/21/poisson-image-editing-revised/
   Poisson Equation where f = 0
   Result : Smooth interpolation
This is gone
   Fill patches with highest information content
     Structure (edge)
     Boundary-patch
     High variance : chaotic textures (e.g. grass)
     Low variance : plain
   Math can also means Fun especially when it’s
    applied, you don’t know what you’ve
    missed
   Innovation in Research seeks the balance
    between pragmatic and platonic (practical vs
    ideal), there are still lot of areas to explore
   For everything one can imagine, someone is
    making it somewhere
   DO NOT Afraid!
   DO NOT Worry!
   DO NOT Hesitate!
Please ask me!

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Fun with Computer Vision

  • 1. Kuliah Umum Kapita Selekta ITTelkom, 13 Oktober 2012 Peb Ruswono Aryan http://about.me/peb
  • 2.
  • 3. Peb Ruswono Aryan  Edu :  S1 Informatika ITB, 2002-2007  S2 Informatika ITB, 2008-2010  Work :  R & D, Digital Mark Reader (DMR), 2007-2010  Dosen, STEI ITB, 2010-present  Interest :  Visualization  Image Processing and Interpretation
  • 4. + =
  • 5.
  • 6. Multiscale tricks  If it must fail, it must fail early  Iterative Reduction by (re-)Weighting
  • 7.
  • 8. Statistical Model of Shape & Appearance for constrained search [Tim Cootes]  Shape : point location  Appearance : image patch around point  X = Xavg + StdDev  X = Xavg + Pb  P : PCA , b : parameter (reduced search space)  Steps :  Foreach point in shape model: ▪ Find most similar patches along normal direction ▪ Test if current shape fit with learned model ▪ Continue until convergence
  • 9.
  • 10. Hx = z, given x & z, find H  Set of pair of matched points  Overdetermined system of linear equation  RANSAC  Pick small subset  Calculate H  Check whether H is supported by the rest of population  Select best H with biggest support (minimize error)
  • 11.
  • 12.
  • 14. Poisson Equation  Laplace(source[mask]) = Laplace(dest[mask])  Boundary Condition : destination pixels  Discrete Poisson Operator  Sparse Matrix Linear Solver  Direct  Iterative Relaxation  http://pebbie.wordpress.com/2012/10/21/poisson-image-editing-revised/
  • 15.
  • 16.
  • 17.
  • 18. Poisson Equation where f = 0  Result : Smooth interpolation
  • 20. Fill patches with highest information content  Structure (edge)  Boundary-patch  High variance : chaotic textures (e.g. grass)  Low variance : plain
  • 21.
  • 22.
  • 23.
  • 24.
  • 25. Math can also means Fun especially when it’s applied, you don’t know what you’ve missed  Innovation in Research seeks the balance between pragmatic and platonic (practical vs ideal), there are still lot of areas to explore  For everything one can imagine, someone is making it somewhere
  • 26. DO NOT Afraid!  DO NOT Worry!  DO NOT Hesitate!