© devi parikh 2008 localization and segmentation of 2d high capacity color barcodes gavin jancke...

© Devi Parikh 2008

Localization and Segmentation of

2D High Capacity Color Barcodes

Gavin Jancke

Microsoft Research, Redmond

Devi Parikh

Carnegie Mellon University

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Motivation

UPC Barcode

QR Code Datamatrix

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HCCB

Microsoft’s High Capacity Color Barcode

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Application Uniquely identifying commercial audiovisual

works such as motion pictures, video games, broadcasts, digital video recordings and other media

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GoalLocate and Segment the barcode from consumer

images

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Overview

Design specifications of Microsoft’s HCCB

Approach

Localization

Segmentation

Progressive Strategy

Results

Conclusions

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Microsoft’s HCCB

4 or 8 colors

Triangles

String of colors

palette

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Microsoft’s HCCB

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Microsoft’s HCCB

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Microsoft’s HCCB

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Microsoft’s HCCB

R rows

S symbols per rowS = (r+1)*R

Aspect ratio: r

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Approach

Thresholding

Orientation prediction

Corner localization

Row localization

Symbol localization

Color assignments

Barcode localization

Barcode segmentation

point inside the barcode is known

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Localization: Thresholding

Identify thick white band and row separators

Normalization

Adaptive

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Localization: Orientation

orientation orientation

dist

ance

-90 900sum

mat

ion

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Localization: Corners Rough estimates

whiteness mask non-texture mask combined mask

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Localization: Corners Gradient based refinement

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Localization: Corners Line based refinement

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Segmentation: Rows

Summation

Flip?

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Segmentation: Symbols

S E

Local search

Number of symbols per row

q(S,E) = q(samples|S,E)

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Segmentation: Colors

Palette

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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!

Observations

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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

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Results

Dataset of 500 images

Performance metric: % barcodes successfully decoded

Decoder model: Barcode successfully decoded if 80% of symbols are correctly identified

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Results

Allows for explicit trade-off between accuracy and computational time

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Results

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Results

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Results

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Results

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Results

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Results

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Results

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Results

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Results

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Results

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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

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Acknowledgements

Microsoft Research Larry Zitnick Andy Wilson Zhengyou Zhang

Carnegie Mellon University Advisor: Tsuhan Chen

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Thank you!