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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