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Automatic Logo Replacement

Automatic Logo Replacement






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    Automatic Logo Replacement Automatic Logo Replacement Presentation Transcript

    • Automatic Logo Replacement Sibasish Acharya and Saurabh Palan
    • 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
    • Dataset Creation
    • 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
    • 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.
    • Logo Similarity Measurement
      • Histogram comparison by  2 Difference
      • L1 Norm, L2 Norm
    • Logo Warping
      • We obtained the best match clipart and warped it to the Logo
      • We use the parameters obtained from RANSAC + TPS for warping
    • Logo Detection
      • Sliding Window
        • Obtain Patch
        • Compare with training Dataset
        • Compute dissimilarity with training dataset
        • Find minimum dissimilarity
          • If (Minimum dissimilarity <= threshold)
          • LOGO DETECTED
          • Else
            • NOT DETECTED
      • SVM
        • Did not work well…
    • Logo replacement
      • Warp detected Logo towards reference logo
      • Warp replacement Logo towards detected logo
    • 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
    • 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
    • Conclusion
      • Efficiency of Feature Detection algorithm determined the outcome of the project…best algorithm for our application - HOG