shape matching and object recognition using shape contexts

SHAPE MATCHING AND OBJECT RECOGNITION

USING

SHAPE CONTEXTS

Seminar On CSE-4102

Paper By:• Serge Belogie, Jitender Malik and Jan

Puzch

Presented by:• Qudrat-E-Alahy Ratul

1Qudrat-E-Alahy Ratul, KUET, Khulna, Bangladesh

Typed latter

Hand writing(1

)

Hand writing(2

)

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.

2Qudrat-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.

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

Matching with shape Contexts

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

Sample(a) Sample(b) Log polar histogram

Correspond found using bipartite matching

4Qudrat-E-Alahy Ratul, KUET, Khulna, Bangladesh

Matching with shape Contexts(CONT.)

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

Where, p i is a point on the fi rst shape. (shape (a)).p j 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

Modeling Transformation

Idle state: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.

Regularization :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

Example of Transformation

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

Prototype Selection

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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

Prototype Selection(CONT.)

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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

Case Study

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9was

detected as 5

5was

detected as 0

9was

detected as 4

8was

detected as 0

5was

detected as 6

Error is only 63 % using 20,000 training example.

Digit recognation:

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

Case Study

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Using 72 view per object.

3-D object detection:

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

Conclusion

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•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

Thank you

13Qudrat-E-Alahy Ratul, KUET, Khulna, Bangladesh