logging in or signing up DeviParikh WACV 2008 Fenwick Download Post to : URL : Related Presentations : Share Add to Flag Embed Email Send to Blogs and Networks Add to Channel Uploaded from authorPOINTLite Insert YouTube videos in PowerPont slides with aS Desktop Copy embed code: (To copy code, click on the text box) Embed: URL: Thumbnail: WordPress Embed Customize Embed The presentation is successfully added In Your Favorites. Views: 294 Category: Education License: All Rights Reserved Like it (0) Dislike it (0) Added: February 27, 2008 This Presentation is Public Favorites: 0 Presentation Description No description available. Comments Posting comment... Premium member Presentation Transcript Slide1: Localization and Segmentation of 2D High Capacity Color Barcodes Gavin Jancke Microsoft Research, Redmond Devi Parikh Carnegie Mellon UniversityMotivation: Motivation UPC Barcode QR Code DatamatrixHCCB: HCCB Microsoft’s High Capacity Color BarcodeApplication: Application Uniquely identifying commercial audiovisual works such as motion pictures, video games, broadcasts, digital video recordings and other media Goal: Goal Locate and Segment the barcode from consumer imagesOverview: Overview Design specifications of Microsoft’s HCCB Approach Localization Segmentation Progressive Strategy Results ConclusionsMicrosoft’s HCCB: Microsoft’s HCCB 4 or 8 colors Triangles String of colors paletteMicrosoft’s HCCB: Microsoft’s HCCB Microsoft’s HCCB: Microsoft’s HCCBMicrosoft’s HCCB: Microsoft’s HCCB Microsoft’s HCCB: Microsoft’s HCCB R rows S symbols per row S = (r+1)*R Aspect ratio: rApproach: Approach Thresholding Orientation prediction Corner localization Row localization Symbol localization Color assignments Barcode localization Barcode segmentation point inside the barcode is known Localization: Thresholding: Localization: Thresholding Identify thick white band and row separators Normalization Adaptive Localization: Orientation: Localization: Orientation orientation orientation distance -90 90 0 summationLocalization: Corners: Localization: Corners Rough estimates whiteness mask non-texture mask combined maskLocalization: Corners: Localization: Corners Gradient based refinementLocalization: Corners: Localization: Corners Line based refinementSegmentation: Rows: Segmentation: Rows Summation Flip?Segmentation: Symbols: Segmentation: Symbols Local search Number of symbols per row q(S,E) = Sq(samples|S,E)Segmentation: Colors: Segmentation: Colors PaletteObservations: 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! ObservationsProgressive strategy: 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 strategiesResults: Results Dataset of 500 images Performance metric: % barcodes successfully decoded Decoder model: Barcode successfully decoded if 80% of symbols are correctly identifiedResults: Results Allows for explicit trade-off between accuracy and computational timeResults: ResultsResults: ResultsResults: ResultsResults: ResultsResults: ResultsResults: ResultsResults: ResultsResults: ResultsResults: ResultsResults: ResultsConclusions: 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 strategyAcknowledgements: Acknowledgements Microsoft Research Larry Zitnick Andy Wilson Zhengyou Zhang Carnegie Mellon University Advisor: Tsuhan ChenSlide37: Thank you! You do not have the permission to view this presentation. In order to view it, please contact the author of the presentation.
DeviParikh WACV 2008 Fenwick Download Post to : URL : Related Presentations : Share Add to Flag Embed Email Send to Blogs and Networks Add to Channel Uploaded from authorPOINTLite Insert YouTube videos in PowerPont slides with aS Desktop Copy embed code: (To copy code, click on the text box) Embed: URL: Thumbnail: WordPress Embed Customize Embed The presentation is successfully added In Your Favorites. Views: 294 Category: Education License: All Rights Reserved Like it (0) Dislike it (0) Added: February 27, 2008 This Presentation is Public Favorites: 0 Presentation Description No description available. Comments Posting comment... Premium member Presentation Transcript Slide1: Localization and Segmentation of 2D High Capacity Color Barcodes Gavin Jancke Microsoft Research, Redmond Devi Parikh Carnegie Mellon UniversityMotivation: Motivation UPC Barcode QR Code DatamatrixHCCB: HCCB Microsoft’s High Capacity Color BarcodeApplication: Application Uniquely identifying commercial audiovisual works such as motion pictures, video games, broadcasts, digital video recordings and other media Goal: Goal Locate and Segment the barcode from consumer imagesOverview: Overview Design specifications of Microsoft’s HCCB Approach Localization Segmentation Progressive Strategy Results ConclusionsMicrosoft’s HCCB: Microsoft’s HCCB 4 or 8 colors Triangles String of colors paletteMicrosoft’s HCCB: Microsoft’s HCCB Microsoft’s HCCB: Microsoft’s HCCBMicrosoft’s HCCB: Microsoft’s HCCB Microsoft’s HCCB: Microsoft’s HCCB R rows S symbols per row S = (r+1)*R Aspect ratio: rApproach: Approach Thresholding Orientation prediction Corner localization Row localization Symbol localization Color assignments Barcode localization Barcode segmentation point inside the barcode is known Localization: Thresholding: Localization: Thresholding Identify thick white band and row separators Normalization Adaptive Localization: Orientation: Localization: Orientation orientation orientation distance -90 90 0 summationLocalization: Corners: Localization: Corners Rough estimates whiteness mask non-texture mask combined maskLocalization: Corners: Localization: Corners Gradient based refinementLocalization: Corners: Localization: Corners Line based refinementSegmentation: Rows: Segmentation: Rows Summation Flip?Segmentation: Symbols: Segmentation: Symbols Local search Number of symbols per row q(S,E) = Sq(samples|S,E)Segmentation: Colors: Segmentation: Colors PaletteObservations: 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! ObservationsProgressive strategy: 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 strategiesResults: Results Dataset of 500 images Performance metric: % barcodes successfully decoded Decoder model: Barcode successfully decoded if 80% of symbols are correctly identifiedResults: Results Allows for explicit trade-off between accuracy and computational timeResults: ResultsResults: ResultsResults: ResultsResults: ResultsResults: ResultsResults: ResultsResults: ResultsResults: ResultsResults: ResultsResults: ResultsConclusions: 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 strategyAcknowledgements: Acknowledgements Microsoft Research Larry Zitnick Andy Wilson Zhengyou Zhang Carnegie Mellon University Advisor: Tsuhan ChenSlide37: Thank you!