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Lazy Photographer. 林士涵. 林士涵. 林士涵. 林士涵. 林士涵. 林士涵. 林士涵. 林士涵. 黃彥翔. 黃彥翔. 黃彥翔. 黃彥翔. 黃彥翔. 黃彥翔. 黃彥翔. 黃彥翔. 黃彥翔. 黃彥翔. 黃彥翔. 黃彥翔. 黃彥翔. 黃彥翔. 黃彥翔. 黃彥翔. 黃彥翔. 張嫚家. 張嫚家. 張嫚家. 張嫚家. 張嫚家. 張嫚家. 張嫚家. 張嫚家. 張嫚家. 張嫚家. Motivation. ‧ A lazy photographer - PowerPoint PPT Presentation
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Lazy Lazy PhotographerPhotographer
黃彥翔黃彥翔黃彥翔 張嫚家 林士涵黃彥翔黃彥翔 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
Motivation
‧ A lazy photographer
‧ after traveling, we want to see photos as soon as possible.
Four approaches
‧ remove over exposure
‧ remove blur image
‧ remove (denote) duplication
‧ clustering the photos by scene
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)
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)
Duplication
‧ compare photo with SIFT feature
‧ compare with the next n photos
Clustering
‧ based on SIFT feature
‧ union similar photos
‧ set thresholds to detect
Result
‧ dataset : 120 photos with 23 over exposure
43 blur photos
‧ dataset2 : 60 photos in a single trip
DEMO TIMEDEMO TIME
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)
Conclusion & future
‧ Auto photo adjustment based on our system, fast and convenient
‧ replace or correct over exposure parts
‧ deblur
‧ user-friendly UI
Thank you!Thank you!