The document discusses a seminar on shape matching and object recognition using shape contexts. Shape contexts are shape descriptors that can be used to match shapes. They involve creating log polar histograms of shapes and finding correspondences between points on different shapes using bipartite graph matching. The approach was used for applications like digit recognition, achieving an error rate of 63% using 20,000 training examples, and 3D object detection using 72 views per object.

1. Seminar On CSE-4102Shape Matching and Object RecognitionUsingShape Contexts1Qudrat-E-Alahy Ratul, KUET, Khulna, Bangladesh

2. It is easy for human to make difference between two similar object.

3. It is difficult for machine to make difference between two similar object.2Qudrat-E-Alahy Ratul, KUET, Khulna, Bangladesh

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5. 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

6. 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.

7. Total cost of matching:5Qudrat-E-Alahy Ratul, KUET, Khulna, Bangladesh

8. Idle state:Regularization :We use affine model to choose a suitable family of transformation.

9. A standard choice of affine model:T(x)=Ax+oWe use TPS(Thin Plate Spline) model transformation.

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

11. Regularization parameter determine the amount of smoothing.6Qudrat-E-Alahy Ratul, KUET, Khulna, Bangladesh

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