Automatic Logo Replacement Sibasish Acharya and Saurabh Palan
Project Overview <ul><li>Dataset Creation </li></ul><ul><li>Feature Detection </li></ul><ul><li>Pair-wise logo alignment <...
Dataset Creation
Feature Detection <ul><li>SIFT  </li></ul><ul><ul><li>Good but Not Panacea </li></ul></ul><ul><ul><li>Does not extract amp...
Pair-wise logo alignment <ul><li>RANSAC + TPS </li></ul><ul><ul><li>Initial feature matches fed to RANSAC are detected by ...
Logo Similarity Measurement <ul><li>Histogram comparison by   2  Difference </li></ul><ul><li>L1 Norm,  L2 Norm  </li></ul>
Logo Warping <ul><li>We obtained the best match clipart and warped it to the Logo  </li></ul><ul><li>We use the parameters...
Logo Detection <ul><li>Sliding Window </li></ul><ul><ul><li>Obtain Patch </li></ul></ul><ul><ul><li>Compare with training ...
Logo replacement <ul><li>Warp detected Logo towards reference logo </li></ul><ul><li>Warp replacement Logo towards detecte...
Logo appearance matching <ul><li>We use V of HSV  </li></ul><ul><li>Calculate mean of  V value of reference logo </li></ul...
Image Blending <ul><li>We calculate the bounding box from test image to be cropped out then replace bounding box by replac...
Conclusion <ul><li>Efficiency of Feature Detection algorithm determined the outcome of the project…best algorithm for our ...
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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.

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Automatic Logo Replacement

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