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