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# Shape Matching and Object Recognition Using Shape Contexts

## on Oct 21, 2009

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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 ContextsPresentation Transcript

• Seminar On CSE-4102
Shape Matching and Object Recognition
Using
Shape Contexts
1
• 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
• 3
• 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
• 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
• 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.
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Digit recognation:
Error is only 63 % using 20,000 training example.