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Symmetry

Symmetry. Find points with shape edges using different threshold angles. Clustering based on points. Find matching clusters. Computer symmetry plane. My method. Previous pose normalization. Problems. A better way to do cluster ? How to evaluate matching clusters more properly

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Symmetry

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  1. Symmetry • Find points with shape edges using different threshold angles

  2. Clustering based on points

  3. Find matching clusters

  4. Computer symmetry plane My method Previous pose normalization

  5. Problems • A better way to do cluster? • How to evaluate matching clusters more properly • Now using Gaussian mixture distribution • The covariance matrices are restricted to be diagonal • The distance score

  6. Next • Use other feature points ( find the points by learning ) • Use features along with (x,y,z) to do clustering • Find 10 hard/easy ones in Cleft dataset • Prepare points (Giving examples) • Schedule a meeting with Michael • Triangles are more reliable (distance to be large) • Get manual landmarks from Jiun-hong’s

  7. Jiun-hong’s pose normalize method • On automatic landmarks_> should be nice

  8. Ideas • Bipartite matching • Fit clusters into lines • The use of bounding box • Corners of 3D • Use front and back info as a constrain • How to evaluate the results

  9. Using position, such as exhausting searching, can do symmetry and find matching points back and forth a couple of times

  10. Learn: what is a good matching!! • Give it some heads, learn from samples, what’s “good” matches • Instead of use self’s constrains

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