1. Lazy Photographer 黃彥翔 張嫚家 林士涵 黃彥翔 張嫚家黃彥翔張嫚家黃彥翔張嫚家黃彥翔張嫚家黃彥翔張嫚家黃彥翔 林士涵 黃彥翔 林士涵 黃彥翔 林士涵 黃彥翔張嫚家 林士涵 黃彥翔張嫚家 林士涵 黃彥翔張嫚家 林士涵 黃彥翔張嫚家 林士涵 黃彥翔

2. Motivation ‧ A lazy photographer ‧ after traveling, we want to see photos as soon as possible.

3. Four approaches ‧ remove over exposure ‧ remove blur image ‧ remove (denote) duplication ‧ clustering the photos by scene

4. Over exposure ‧ use Lab color space ‧ split photo to blocks ‧ use L value and the distance ab to (0,0) ‧ set various thresholds to detect ‧ “Correcting Over-Exposure in Photographs “ (must read)

5. Blur ‧ deal with vibration or defocused ‧ Use gradient magnitude + gradient direction as a feature vector ‧ take 100 blur photos and 100 non-blur to train a model by SVM ‧ “Blurred Image Detection and Classification” (must read)

6. Duplication ‧ compare photo with SIFT feature ‧ compare with the next n photos

7. Clustering ‧ based on SIFT feature ‧ union similar photos ‧ set thresholds to detect

8. Result ‧ dataset : 120 photos with 23 over exposure 43 blur photos ‧ dataset2 : 60 photos in a single trip

9. DEMO TIME

10. Result (cont.) ‧ Blur detection Recall 83.72% (36/43) Precision 76.60% (36/47) ‧ Over exposure Recall 82.60% (19/23) Precision 79.17% (19/24)

11. Conclusion & future ‧ Auto photo adjustment based on our system, fast and convenient ‧ replace or correct over exposure parts ‧ deblur ‧ user-friendly UI

13. Thank you!