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We are TEAM 01

We are TEAM 01. YI-HAN CHIANG Junior student PEI-YUN HSU Senior student HUI-YU LEE F irst-year graduated student. I ntroduction. Collage Collage photos into a frame Smart Automatically importance semantic meanings. Motivation - 1. Recent mobile apps. Motivation - 1. We want to

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We are TEAM 01

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  1. We are TEAM 01 • YI-HAN CHIANG • Junior student • PEI-YUN HSU • Senior student • HUI-YU LEE • First-year graduated student

  2. Introduction • Collage • Collage photos into a frame • Smart • Automatically • importance • semantic meanings

  3. Motivation - 1 • Recent mobile apps

  4. Motivation - 1 • We want to • collage photos automatically! • Put into appropriate frames !

  5. Motivation - 2 • Too many photos to pick

  6. Motivation - 2 • We want to • Pick representative photos ! • Collage them !

  7. can… • Select photos • Fit the best template • Pick semantic combinations • Output result

  8. can… • Select photos • Fit the best template • Pick semantic combinations • Output result

  9. Select photosRemove similar photos • Color histogram feature (YIQ) • Randomly pick one

  10. Select photosChoose importance photos • score = 0.6*Num of People +0.2*mean_Value +0.2*mean_Saturation • Sort&Random

  11. can… • Select photos • Fit the best template • Pick semantic combinations • Output result

  12. Fit the best templateEnumerate templates • for each case (4 – 7 )

  13. Fit the best templateFit the best template 0.2 0.1 0.4 0.3 0.7 Sum = |2 – 0.7| + | 1 – 0.4| +| 1 – 0.3| + |3 – 0.2| +|2 – 0.1| Find the template who has min Sum. 2 3 2 1 1

  14. can… • Select photos • Fit the best template • Pick semantic combinations • Output result

  15. Pick semantic combinationsEnumerate combinations • for example

  16. Pick semantic combinationsGood looking? • Consider completeness • Sky on the top(grayscale) • Symmetric(balance human #)

  17. can… • Select photos • Fit the best template • Pick semantic combinations • Output result

  18. Output resultLive demo time

  19. Output resultexample 1 - NTU

  20. Output resultexample 2 – birthday time

  21. Output resultexample 3 – basketball time

  22. Q&A

  23. The end

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