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Finding Bucky

Finding Bucky

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Finding Bucky

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  1. Finding Bucky Identifying unapproved logo representations James Buchen, David Mateo, Liang Zheng Gooi

  2. Motivation • Detect altered versions of Bucky logo • Maintain UW - Madison Athletics brand • Develop method to be applied to any brand or logo

  3. Guidelines - Approved

  4. Guidelines - Not approved

  5. Two Step Process • Use the Viola-Jones image detector • Weak Classifier model • Requires Image training • Use SIFT, SURF, or FAST • Flagged images will be compared directly • Approve the image based on number of feature points

  6. Viola-Jones Image Detector • Complex classifier • Uses cascading weak classifiers • Trained using positive and negative images • Feature values are sum of pixels in clear rectangles subtracted from sum of pixels within shaded rectangles Feature Types

  7. Method • Input official Bucky logo and website screenshot • Generate Bucky variations • Collect other variations from internet • Train viola-jones image detector • Identify potential Bucky objects • Compare objects and official Bucky using SIFT • Generate output image

  8. Positive Images:GeneratedCollected

  9. Other technologies we looked into • Extract cartoon from other image • Create occlusion image • SURF detector, Edge Feature (EF) • “Content Based Image Retrieval through Object Extraction and Querying”

  10. Results-Poor

  11. Results-Good

  12. Sift-Negative • Altered Aspect Ratio produces negative response • Obscured Bucky only has ~half the feature points matching

  13. Sift-Positive • Almost all feature points match • Invariant to scaling

  14. Future Improvements • Increase variety of generated images • Increase number of collected images • Tune the detector • Choose decision threshold for SIFT • Develop automatic website crawler to scan many websites for Unapproved Bucky images.

  15. Thank you! Questions?

  16. References [1] Viola, P., and M. J. Jones, "Rapid Object Detection using a Boosted Cascade of Simple Features". Proceedings of the 2001 IEEE Computer Society Conference. Volume 1, 15 April 2001, pp. I-511–I-518. [2] Ojala, T., M. Pietikainen, and T. Maenpaa, "Multiresolution Gray-scale and Rotation Invariant Texture Classification With Local Binary Patterns". IEEE Transactions on Pattern Analysis and Machine Intelligence. Volume 24, No. 7 July 2002, pp. 971–987. [3] Dalal, N., and B. Triggs, "Histograms of Oriented Gradients for Human Detection". IEEE Computer Society Conference on Computer Vision and Pattern Recognition.Volume 1, (2005), pp. 886–893.