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Shape context, a descriptor for object recognition in computer vision.

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- 1. Shape Context Rocío Cabrera u1908272 Vanya Valindria u190825906/05/12 1
- 2. Introduction Can you guess what number it is? 05/06/12 2
- 3. Objectives “Have descriptors that can be computed in one image and used to find corresponding points, if visible, in another image.” “Given a query model image, to develop an algorithm capable of retrieving similar- shaped images from an extensive database” 05/06/12 3
- 4. Process Stages Solve the Use the Evaluate the correspondences Compute the correspondence . problem between the two shapes . to estimate an aligning transform distance between the two shapes ? distance and classify the shape05/06/12 4
- 5. SHAPE CONTEXT “A novel approach to measuring similarities between shapes and exploit it for object classification/recognition”05/06/12 5
- 6. Shape Context Computation Step 1. Obtain from ShapeP and ShapeQ n-samples uniformly spaced taken from their edge elements05/06/12 6
- 7. Shape Context Computation Step 2. Compute the Euclidean distance (r) and the angle (θ) from each point in the set to all the other n-1 points. Normalize r by the median distance (λ) and measure the angle relative to the positive x-axis.05/06/12 7
- 8. Shape Context Computation Step 3. Compute the log of the r vector. Discretize the distance and angle measurements05/06/12 8
- 9. Shape Context Computation Step 4. For each origin point, capture number of points that lie a given θ,R bin. Each shape context is a log-polar histogram of the coordinates of the n-1 points measured from the origin reference point.05/06/12 9
- 10. Shape Context Computation Shape context of the sample points in ShapeP and ShapeQ.05/06/12 10
- 11. Matching Shape Contexts How can we assign the sample points of ShapeP to correspond to those of ShapeQ? Determining shape correspondences such that: l Corresponding points have very similar descriptors l The correspondences are unique05/06/12 11
- 12. Matching Shape Contexts Define matching cost function Shape context Distance between the two normalized histograms Local appearance Dissimilarity of the tangent angles05/06/12 12
- 13. Matching Shape Contexts05/06/12 13
- 14. Modeling Transformation Given a set of correspondences, estimate a transformation that maps the model into the target Euclidean transformation Affine model Thin Plate Spline (TPS) 05/06/12 14
- 15. Classification/Recognition This enables a measure of shape similarity The dissimilarity between two shapes can be computed as the sum of matching errors between corresponding points, together with a term measuring the magnitude of the aligning transform Given a dissimilarity measure, a k-NN technique can be used for object classification/recognition 05/06/12 15
- 16. Method EvaluationAdvantages Drawbacks Incorporates invariance to: Sensitive local distortion or blurred edges Translation Problems in cluttered Scale background Rotation Occlusions05/06/12 16
- 17. Applications Digit recognition Silhouette similarity- based retrieval 3 D object recognition Trademark retrieval 05/06/12 17
- 18. Database for Digit Recognition MNIST datasets of handwritten digits: 60,000 training and 10,000 test digitsLinks:http://yann.lecun.com/exdb/mnist/ 05/06/12 18
- 19. Database for Silhouette MPEG-7 shape silhouette database (Core Experiment CE-Shape-1 part B) 1400 images: 70 shapes categories and 20 images per category Links: http://mpeg.chiariglione.org/standards/mpeg-7/mpeg-7.htm 05/06/12 19
- 20. Database for 3-D object recognition COIL-20 database 20 common household objects; turned every 5˚ for a total of 72 views per object Links: http://www1.cs.columbia.edu/CAVE/software/softlib/coil-20.php 05/06/12 20
- 21. Database for Trademark retrieval 300 different real- world trademark 05/06/12 21
- 22. MATLAB DEMO05/06/12 22
- 23. Conclusions The shape context method is simple to implement yet it is a rich shape descriptor The methodology makes it invariant to translation, scale and rotation Useful tool for shape matching and recognition05/06/12 23

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