Over exposure

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Lazy
Photographer
黃彥翔
張嫚家
林士涵
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 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!
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