•Serge Belogie, Jitender Malik and Jan Puzch
• Qudrat-E-Alahy Ratul
Qudrat-E-Alahy Ratul, KUET, Khulna,
Bangladesh
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 It is easy for human to make difference
between two similar object.
 It is difficult for machine to make difference
between two similar object.
Typed
latter
Hand
writing(1)
Qudrat-E-Alahy Ratul, KUET, Khulna,
Bangladesh
Hand
writing(2)
2
• Develop an efficient algorithm to
overcome “shape similarity”
problem for machine.
• Solve the correspondence problem
between the two shapes
• Use the correspondence to
estimate an aligning transform
• Compute the distance between the
two shapes as a sum of matching
errors between corresponding
points.
Qudrat-E-Alahy Ratul, KUET, Khulna,
Bangladesh
3
Shape Context:
It is Shape descriptor that play the role of shape
matching.
Qudrat-E-Alahy Ratul, KUET, Khulna,
Bangladesh
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Bipartite graph matching:
If cij denotes the cost between two point the cost is
determined by:
Qudrat-E-Alahy Ratul, KUET, Khulna,
Bangladesh
5
Idle state:
We use affine model to choose a suitable family of
transformation.
A standard choice of affine model:
We use TPS(Thin Plate Spline) model transformation.
Regularization :
If there is noise in specified values then the
interpolation is relaxed by regularization.
Regularization parameter determine the amount of
smoothing.
Qudrat-E-Alahy Ratul, KUET, Khulna,
Bangladesh
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Qudrat-E-Alahy Ratul, KUET, Khulna,
Bangladesh
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• Our objective is prototype based object
recognition.
• Objects are categorized by idle
examples rather then a set of formal
rule.
• An sparrow is likely prototype of birds.
But not the penguin!
• Developing an computational
framework of nearest-neighborhood
methods using multiple stored view.
• We use BD.Ripley’s nearestneighborhood method .
Qudrat-E-Alahy Ratul, KUET, Khulna,
Bangladesh
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• Determine the shape using
TPS(Thin Plate Spline)
transformation model.
• After matching the shape estimate
the context distance as weighted
sum of three terms:
• Shape context distance
• Image appearance distance
• Bending energy.
Qudrat-E-Alahy Ratul, KUET, Khulna,
Bangladesh
9
Digit recognation:
Error is only 63 % using 20,000 training example.
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5
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was
detected
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Qudrat-E-Alahy Ratul, KUET, Khulna,
Bangladesh
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3-D object detection:
Using 72 view per object.
Qudrat-E-Alahy Ratul, KUET, Khulna,
Bangladesh
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Qudrat-E-Alahy Ratul, KUET, Khulna,
Bangladesh
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Qudrat-E-Alahy Ratul, KUET, Khulna,
Bangladesh
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