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Context-aware Exposure Auto-correction

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Presentation on theme: "Context-aware Exposure Auto-correction"— Presentation transcript:

1 Context-aware Exposure Auto-correction

2 Global exposure auto-correction
over-exposed under-exposed low-contrast input automatic histogram stretching

3 Global exposure auto-correction
Detection: valid histogram range < threshold Method: stretch histogram, adjust gamma curve Test Images # Global Correction Percentage 1370 63 4.6% #: Globally over-exposed, under-exposed & low-contrast images Test Images include party, family, vacation, landscape, street view, pets

4 Local exposure auto-correction
High dynamic range scene input Auto adjustment [WLPG] Local shadow / Highlight [ours]

5 Local exposure auto-correction
Back-lighting object input Auto adjustment [WLPG] Local shadow / Highlight [ours]

6 High dynamic range scene detection
segment sky region scene region input extract features sky detection , , local contrast in scene region sky histogram scene histogram confidence map of sky classifier

7 Samples of high dynamic range scene
True: False:

8 High dynamic range scene
Test Images detected sky image Percentage 1370 316 23% True HDR scene # Percentage False HDR Scene 190 13.87% 126 #: True HDR scene images / Test Images

9 Back-lighting object detection
The most attractive backlit object is human! extract features face detection Histogram, local contrast in face/body region input classifier Histogram of image body detection input

10 Samples of back-lighting object
True: False:

11 Back-lighting object Test Images detected human image Percentage 1370
595 43.4% True backlit human # Percentage False backlit human 102 7.44% 493 #: True backlit human images / Test Images

12 ( hdr scene 13.87% + backlit human 7.44% )
Summary Global incorrect exposure v.s. local incorrect exposure The “detected Human + Sky images” account for almost 66.5% of the whole test images Global Local 4.6% 21.31% ( hdr scene 13.87% + backlit human 7.44% )

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