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)

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

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

7. 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

8. 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

9. 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)