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Business Identification: Spatial Detection

Business Identification: Spatial Detection. Alexander Darino Weeks 7 & 8 (Abridged). Weaknesses to Current Approach. Business Name Matching. Business Spatial Detection. Latitude Longitude. Geocoding Reverse Geocoding. Nearby Businesses. Business Identification. Image. OCR.

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Business Identification: Spatial Detection

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  1. Business Identification:Spatial Detection Alexander Darino Weeks 7 & 8 (Abridged)

  2. Weaknesses to Current Approach Business Name Matching Business Spatial Detection Latitude Longitude Geocoding Reverse Geocoding Nearby Businesses BusinessIdentification Image OCR Detected Text

  3. Alternative: Image Matching

  4. Alternative: Image Matching • Weaknesses: • Low Availability of Storefront Images (< 50% Avg) • George Aiken area businesses with photos: 18/35 • Brueggers area businesses with photos: 22/40 • Tambellini area businesses with photos: 8/22 • Available Images too small (100 x 100) • Not a viable solution

  5. Alternative: Template Matching • Tambellini • Tambellini • Tambellini • Tambellini • Tambellini • Tambellini • Tambellini • Tambellini

  6. Alternative: Template Matching • SIFT is not a robust solution. • Maybe Haar features will work? • Moving right along…

  7. Moving away from SIFT and revisiting Scene Text Recognition

  8. STR Implementation • STR Implementation: “Automatic Detection and Recognition of Signs From Natural Scenes” Multiresolution-based potential characters detection Character/layout geometry and color properties analysis Refined Detection Local affine rectification

  9. Multiresolution-based potential characters detection • Laplacian-of-Guassian Edge Detection • Dice image/edges into Patches • Combine patches with similar properties into regions • Obtain bounding box of region as candidate text • Properties include: • Mean • Variance • Intensity(?)

  10. Multiresolution-based potential characters detection

  11. Multiresolution-based potential characters detection Patches qualify if:

  12. Multiresolution-based potential characters detection

  13. Multiresolution-based potential characters detection

  14. Multiresolution-based potential characters detection

  15. Note to self: I need to fix this

  16. Color Properties Analysis • Implemented Gaussian Mixture Model (GMM) to obtain μ and σ of foreground/background for: R/G/B/H/I • Calculated Confidences that component (RGBHI) can be used to recognize characters Multiresolution-based potential characters detection Character/layout geometry and color properties analysis Refined Detection Local affine rectification

  17. Original

  18. Red

  19. Green

  20. Blue

  21. Hue

  22. Intensity

  23. Evaluation • The highest confidence was found in Intensity even though most letters vanish, vs Hue where letters are easily distinguisible • This suggests text recognition should occur individually per character • The paper further suggests it needs the patches around the individual characters • (Woops)

  24. Next Steps • Goal: Finish STR by next Friday • Fix text detector • Work with Amir over weekend to implement remaining STR algorithms

  25. Thank You

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