1. Shape Matching and Object Recognition
Seminar On CSE-4102
Shape Matching and Object Recognition
Using
Shape Contexts
Paper By:
Serge Belogie, Jitender Malik and Jan Puzch
Presented by:
Qudrat-E-Alahy Ratul
Qudrat-E-Alahy Ratul, KUET, Khulna, Bangladesh

2. Qudrat-E-Alahy Ratul, KUET, Khulna, Bangladesh
INTRODUCTION
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)
Hand writing(2)
Qudrat-E-Alahy Ratul, KUET, Khulna, Bangladesh

3. Qudrat-E-Alahy Ratul, KUET, Khulna, Bangladesh
INTRODUCTION
Objective:
Develop an efficient algorithm to overcome “shape similarity” problem for machine.
Proposed steps:
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

4. Matching with shape Contexts
It is Shape descriptor that play the role of shape matching.
Sample(a)
Sample(b)
Log polar histogram
Correspond found using bipartite matching
Qudrat-E-Alahy Ratul, KUET, Khulna, Bangladesh

5. Matching with shape Contexts(CONT.)
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:
Qudrat-E-Alahy Ratul, KUET, Khulna, Bangladesh

6. Modeling Transformation
Idle state:
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.
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

7. Example of Transformation
Qudrat-E-Alahy Ratul, KUET, Khulna, Bangladesh

8. Qudrat-E-Alahy Ratul, KUET, Khulna, Bangladesh
Prototype Selection
Objective:
Our objective is prototype based object recognition.
Objects are categorized by idle examples rather then a set of formal rule.
Steps:
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 nearest-neighborhood method .
Qudrat-E-Alahy Ratul, KUET, Khulna, Bangladesh

9. Prototype Selection(CONT.)
Shape Distance:
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

10. Qudrat-E-Alahy Ratul, KUET, Khulna, Bangladesh
Case Study
Digit recognation:
Error is only 63 % using 20,000 training example. 9
was detected as 5 4 8 6
Qudrat-E-Alahy Ratul, KUET, Khulna, Bangladesh

11. Qudrat-E-Alahy Ratul, KUET, Khulna, Bangladesh
Case Study
3-D object detection:
Using 72 view per object.
Qudrat-E-Alahy Ratul, KUET, Khulna, Bangladesh

12. Qudrat-E-Alahy Ratul, KUET, Khulna, Bangladesh
Conclusion
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

13. Qudrat-E-Alahy Ratul, KUET, Khulna, Bangladesh
Thank you
Qudrat-E-Alahy Ratul, KUET, Khulna, Bangladesh