1 / 37

Localization and Segmentation of 2D High Capacity Color Barcodes

Localization and Segmentation of 2D High Capacity Color Barcodes. Devi Parikh Carnegie Mellon University. Gavin Jancke Microsoft Research, Redmond. Motivation. UPC Barcode. QR Code. Datamatrix. HCCB. Microsoft’s High Capacity Color Barcode. Application.

tauret
Download Presentation

Localization and Segmentation of 2D High Capacity Color Barcodes

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Localization and Segmentation of 2D High Capacity Color Barcodes Devi Parikh Carnegie Mellon University Gavin Jancke Microsoft Research, Redmond

  2. Motivation UPC Barcode QR Code Datamatrix

  3. HCCB Microsoft’s 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 Microsoft’s HCCB • Approach • Localization • Segmentation • Progressive Strategy • Results • Conclusions

  7. Microsoft’s HCCB 4 or 8 colors Triangles String of colors palette

  8. Microsoft’s HCCB

  9. Microsoft’s HCCB

  10. Microsoft’s HCCB

  11. Microsoft’s HCCB Aspect ratio: r R rows S = (r+1)*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 0 90 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. S E Segmentation: Symbols Number of symbols per row q(S,E) = Sq(samples|S,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!

More Related