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Shape Matching and Object Recognition Using Shape Contexts
 

Shape Matching and Object Recognition Using Shape Contexts

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I gave this presentation from an IEEE transection on patternt analysis and machine intelligence, Vol. 24, No. 24, April 2002.

I gave this presentation from an IEEE transection on patternt analysis and machine intelligence, Vol. 24, No. 24, April 2002.

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    Shape Matching and Object Recognition Using Shape Contexts Shape Matching and Object Recognition Using Shape Contexts Presentation Transcript

    • Seminar On CSE-4102
      Shape Matching and Object Recognition
      Using
      Shape Contexts
      1
      Qudrat-E-Alahy Ratul, KUET, Khulna, Bangladesh
      • It is easy for human to make difference between two similar object.
      • It is difficult for machine to make difference between two similar object.
      2
      Qudrat-E-Alahy Ratul, KUET, Khulna, Bangladesh
    • 3
      Qudrat-E-Alahy Ratul, KUET, Khulna, Bangladesh
    • Shape Context:
      • It is Shape descriptor that play the role of shape matching.
      Log polar histogram
      Sample(a)
      Sample(b)
      Correspond found using bipartite matching
      4
      Qudrat-E-Alahy Ratul, KUET, Khulna, Bangladesh
    • Bipartite graph matching:
      • If cij denotes the cost between two point the cost is determined by:
      Where,
      pi is a point on the first shape. (shape (a)).
      pj is a point on the second shape.(shape(b)).
      • The concept of using dummy node. To minimize Total cost.
      • Total cost of matching:
      5
      Qudrat-E-Alahy Ratul, KUET, Khulna, Bangladesh
    • Idle state:
      Regularization :
      • We use affine model to choose a suitable family of transformation.
      • A standard choice of affine model:
      T(x)=Ax+o
      • We use TPS(Thin Plate Spline) model transformation.
      • If there is noise in specified values then the interpolation is relaxed by regularization.
      • Regularization parameter determine the amount of smoothing.
      6
      Qudrat-E-Alahy Ratul, KUET, Khulna, Bangladesh
    • 7
      Qudrat-E-Alahy Ratul, KUET, Khulna, Bangladesh
    • 8
      Qudrat-E-Alahy Ratul, KUET, Khulna, Bangladesh
    • 9
      Qudrat-E-Alahy Ratul, KUET, Khulna, Bangladesh
    • 10
      Digit recognation:
      Error is only 63 % using 20,000 training example.
      Qudrat-E-Alahy Ratul, KUET, Khulna, Bangladesh
    • 11
      3-D object detection:
      Using 72 view per object.
      Qudrat-E-Alahy Ratul, KUET, Khulna, Bangladesh
    • 12
      • A key characteristics of this approach is estimation of shape similarities and correspondence depends upon shape context.
      • In the experiment gray-scaled picture is used.
      • Some algorithm are modified while experimenting.
      Qudrat-E-Alahy Ratul, KUET, Khulna, Bangladesh
    • Thank you
      13
      Qudrat-E-Alahy Ratul, KUET, Khulna, Bangladesh