shape matching and object recognition using shape contexts
DESCRIPTION
Seminar On CSE-4102. Shape Matching and Object Recognition Using Shape Contexts. It is easy for human to make difference between two similar object. It is difficult for machine to make difference between two similar object. Shape Context:. - PowerPoint PPT PresentationTRANSCRIPT
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
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Hand writing(2
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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:
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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.
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Example of Transformation
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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
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