Seminar On CSE-4102Shape Matching and Object RecognitionUsingShape Contexts1Qudrat-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.2Qudrat-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.

3Qudrat-E-Alahy Ratul, KUET, Khulna, Bangladesh

Shape Context:It is Shape descriptor that play the role of shape matching.Log polar histogramSample(a)Sample(b)Correspond found using bipartite matching4Qudrat-E-Alahy Ratul, KUET, Khulna, Bangladesh

It is Shape descriptor that play the role of shape matching.

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:5Qudrat-E-Alahy Ratul, KUET, Khulna, Bangladesh

If cij denotes the cost between two point the cost is determined by:

The concept of using dummy node. To minimize Total cost.

Total cost of matching:

Idle state:Regularization :We use affine model to choose a suitable family of transformation.A standard choice of affine model:T(x)=Ax+oWe 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.6Qudrat-E-Alahy Ratul, KUET, Khulna, Bangladesh

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.

If there is noise in specified values then the interpolation is relaxed by regularization.

Regularization parameter determine the amount of smoothing.

7Qudrat-E-Alahy Ratul, KUET, Khulna, Bangladesh

8Qudrat-E-Alahy Ratul, KUET, Khulna, Bangladesh

9Qudrat-E-Alahy Ratul, KUET, Khulna, Bangladesh

10Digit recognation:Error is only 63 % using 20,000 training example.Qudrat-E-Alahy Ratul, KUET, Khulna, Bangladesh

113-D object detection:Using 72 view per object.Qudrat-E-Alahy Ratul, KUET, Khulna, Bangladesh

12A 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

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.

Thank you13Qudrat-E-Alahy Ratul, KUET, Khulna, Bangladesh