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Midpoint of nasion midpoint of nasal tip, midline of philtrum midline of chin ( mentum )

Midpoint of nasion midpoint of nasal tip, midline of philtrum midline of chin ( mentum ). Learn landmarks. Use all landmarks doctor’s marked as positive samples Then randomly choose points from face that’s far away from landmarks  negative samples To build the model

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Midpoint of nasion midpoint of nasal tip, midline of philtrum midline of chin ( mentum )

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  1. Midpoint of nasion • midpoint of nasal tip, • midline of philtrum • midline of chin (mentum)

  2. Learn landmarks • Use all landmarks doctor’s marked as positive samples • Then randomly choose points from face that’s far away from landmarks  negative samples • To build the model • Then, relize each landmark( or two symmetry landmarks) will be one class • Curvature co-ocurance (texture) instead of (curvature)

  3. Another idea about landmarks • From paper • http://www.ri.cmu.edu/publication_view.html?pub_id=6417&menu_code=0307 • Maybe want to do a point model

  4. About landmarks • Learn how many “types” of different landmarks • (unsupervised cluster)

  5. Tasks() • Write arff files in matlab (done) • Write all the 40 you need(done) • Learn en areas • Write down en areas first, for all 40 (done, did fairly good job on cross validation) • Use model to do some test data ( try cleft dataset? Can be 5 hard and 5 easy) • Learn nose side areas? • Learn (what ever side) areas • May want to start with landmarks that all images have

  6. Pick 5 hard/ 5 easy from Cleft • Extremely hard • 013 018 • Middle • 017 021 004 008 023 030 031 033 035 • Might be easy • 022 005 020 001 025 026 027 037 • Smooth 20 up to 26

  7. Learn en areas • Write down en areas first, for all 40 (done, did fairly good job on cross validation) • Use model to do some test data ( try cleft dataset? Can be 5 hard and 5 easy) • Find hard/easy ones for cleft data • Write down each data as the desciptor • Predict every point ( to be 1 or 0)

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