Automatic Logo Replacement
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Automatic Logo Replacement

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As a part of Class project for CIS 581 Sibashish and I presented this method to do automatic logo replacement.

As a part of Class project for CIS 581 Sibashish and I presented this method to do automatic logo replacement.

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  • 1. Automatic Logo Replacement Sibasish Acharya and Saurabh Palan
  • 2. Project Overview
    • Dataset Creation
    • Feature Detection
    • Pair-wise logo alignment
    • Logo Similarity Measurement
    • Logo Warping
    • Logo Detection
    • Logo replacement
    • Logo appearance matching
    • Image Blending
    • Conclusion
  • 3. Dataset Creation
  • 4. Feature Detection
    • SIFT
      • Good but Not Panacea
      • Does not extract ample features
      • The clipart and reference logo lack similarity
    • HSV + PCA
    • Histogram of Orientation of Gradient (HOG)
      • Counts occurrences of gradient orientation in localized portions of an image
      • Nine bins
      • Extracts more features, thus comparative effective then SIFT
  • 5. Pair-wise logo alignment
    • RANSAC + TPS
      • Initial feature matches fed to RANSAC are detected by minimum SSD matching
      • 1000 iterations to Minimizes Bending Energy
      • In our case since the clipart is completely different from Logo, we select the best from the worst possible combination.
  • 6. Logo Similarity Measurement
    • Histogram comparison by  2 Difference
    • L1 Norm, L2 Norm
  • 7. Logo Warping
    • We obtained the best match clipart and warped it to the Logo
    • We use the parameters obtained from RANSAC + TPS for warping
  • 8. Logo Detection
    • Sliding Window
      • Obtain Patch
      • Compare with training Dataset
      • Compute dissimilarity with training dataset
      • Find minimum dissimilarity
        • If (Minimum dissimilarity <= threshold)
        • Else
          • NOT DETECTED
    • SVM
      • Did not work well…
  • 9. Logo replacement
    • Warp detected Logo towards reference logo
    • Warp replacement Logo towards detected logo
  • 10. Logo appearance matching
    • We use V of HSV
    • Calculate mean of V value of reference logo
    • Calculate mean of V value of detected logo
    • Add the differences to the logo clipart
  • 11. Image Blending
    • We calculate the bounding box from test image to be cropped out then replace bounding box by replacement logo using 3 level Pyramid Blending
  • 12. Conclusion
    • Efficiency of Feature Detection algorithm determined the outcome of the project…best algorithm for our application - HOG