Shape matching and object recognition using shape contexts
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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
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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
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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
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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
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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
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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
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Example of Transformation
7Qudrat-E-Alahy Ratul, KUET, Khulna, Bangladesh
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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
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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
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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
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Case Study
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Using 72 view per object.
3-D object detection:
Qudrat-E-Alahy Ratul, KUET, Khulna, Bangladesh
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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
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Thank you
13Qudrat-E-Alahy Ratul, KUET, Khulna, Bangladesh