Shape matching and object recognition using shape contexts
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Shape matching and object recognition using shape contexts

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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
    View slide
  • 3
    Qudrat-E-Alahy Ratul, KUET, Khulna, Bangladesh
    View slide
  • 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