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Localization and Segmentation of 2D High Capacity Color Barcodes

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Performance metric: % barcodes successfully decoded. Decoder model: Barcode successfully decoded if 80% of symbols are correctly identified ... – PowerPoint PPT presentation

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Title: Localization and Segmentation of 2D High Capacity Color Barcodes


1
Localization and Segmentation of 2D High Capacity
Color Barcodes
Gavin Jancke Microsoft Research, Redmond
Devi Parikh Carnegie Mellon University
2
Motivation
UPC Barcode
QR Code
Datamatrix
3
HCCB
Microsofts High Capacity Color Barcode
4
Application
  • Uniquely identifying commercial audiovisual
    works such as motion pictures, video games,
    broadcasts, digital video recordings and other
    media

5
Goal
  • Locate and Segment the barcode from consumer
    images

6
Overview
  • Design specifications of Microsofts HCCB
  • Approach
  • Localization
  • Segmentation
  • Progressive Strategy
  • Results
  • Conclusions

7
Microsofts HCCB
4 or 8 colors Triangles String of colors
palette
8
Microsofts HCCB
9
Microsofts HCCB
10
Microsofts HCCB
11
Microsofts HCCB
Aspect ratio r
R rows
S (r1)R
S symbols per row
12
Approach
Thresholding
Orientation prediction
Barcode localization
Corner localization
Row localization
Symbol localization
Barcode segmentation
Color assignments
point inside the barcode is known
13
Localization Thresholding
  • Identify thick white band and row separators
  • Normalization
  • Adaptive

14
Localization Orientation
summation
distance
-90
90
0
orientation
orientation
15
Localization Corners
  • Rough estimates

whiteness mask
non-texture mask
combined mask
16
Localization Corners
  • Gradient based refinement

17
Localization Corners
  • Line based refinement

18
Segmentation Rows
Flip?
Summation
19
Segmentation Symbols
Number of symbols per row
q(S,E) Sq(samplesS,E)
Local search
20
Segmentation Colors
Palette
21
Observations
  • Segmentation results given accurate localization
  • Satisfactory
  • Corner localization
  • Unsatisfactory
  • No one strategy works well on all images
  • However
  • (1) Errors of different strategies are
    complementary
  • (2) Results are verifiable with decoder in
    the loop!

22
Progressive strategy
  • Hence progressive strategy!
  • Similar to ensemble of weak classifiers
  • Or hypothesize-and-test
  • Multiple strategies
  • Rough gradient line, or rough line, or
    rough gradient, or rough alone
  • Different values of threshold during rough corner
    detection
  • Total 12
  • Order of strategies

23
Results
  • Dataset of 500 images
  • Performance metric barcodes successfully
    decoded
  • Decoder model Barcode successfully decoded if
    80 of symbols are correctly identified

24
Results
Allows for explicit trade-off between accuracy
and computational time
25
Results
26
Results
27
Results
28
Results
29
Results
30
Results
31
Results
32
Results
33
Results
34
Results
35
Conclusions
  • 2D High Capacity Color Barcode (HCCB)
  • Successful localization and segmentation of HCCB
    from consumer images
  • Varying densities, aspect ratios, lighting, color
    balance, image quality, etc.
  • Simple computer vision and image processing
    techniques
  • Progressive strategy

36
Acknowledgements
  • Microsoft Research
  • Larry Zitnick
  • Andy Wilson
  • Zhengyou Zhang
  • Carnegie Mellon University
  • Advisor Tsuhan Chen

37
  • Thank you!
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