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Scene segmentation & interpretation Shape context, a descriptor for object recognition in computer vision.

- 1. Shape Context Roco Cabrera u1908272 Vanya Valindria u190825906/05/121

2. IntroductionCan you guess what number it is? 05/06/122 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 05/06/123 4. Process StagesSolve the Use the Evaluate thecorrespondencesCompute thecorrespondence.problem between the two shapes . to estimate an aligningtransformdistance between the two shapes? distance and classify theshape05/06/124 5. SHAPE CONTEXT A novel approach to measuring similarities between shapes and exploit it for object classification/recognition05/06/125 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 () from each point in the set to all the other n-1 points. Normalize r by the median distance () and measure the angle relative to the positive x-axis.05/06/127 8. Shape Context ComputationStep 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 ,R bin.Each shape context is a log-polar histogram of the coordinates of the n-1points measured from the origin reference point.05/06/12 9 10. Shape Context Computation Shape context of the sample points in ShapeP andShapeQ.05/06/12 10 11. Matching Shape Contexts How can we assign the sample points of ShapeP tocorrespond to those of ShapeQ? Determining shape correspondences such that: l Corresponding points have very similar descriptors l The correspondences are unique05/06/1211 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 Contexts05/06/1213 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 computedas the sum of matching errors between correspondingpoints, together with a term measuring the magnitudeof the aligning transform Given a dissimilarity measure, a k-NN technique can be used for object classification/recognition 05/06/1215 16. Method EvaluationAdvantagesDrawbacks Incorporates invariance to: Sensitive local distortion orblurred edgesTranslation ProblemsinclutteredScalebackgroundRotationOcclusions05/06/12 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 ofhandwritten digits: 60,000 training and10,000 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 householdobjects; turned every 5 fora total of 72 views perobjectLinks: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 05/06/1221 22. MATLAB DEMO05/06/12 22 23. Conclusions The shape context method is simple to implementyet 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