1. by Yevgeny Yusepovsky & Diana Tsamalashvili the supervisor: Arie Nakhmani 08/07/2010 1Control and Robotics Labaratory

2. 2 Contents 1)The Goal of the project 2)Possible solutions 3)The FFT algorithm 4)The FFT result 5)The feature based algorithm 6)Normalized Weighted sum result 7)Weighted Stitching result 8)Summary

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4. 4 Direct Alignment method Autocorrelation FFT based algorithm Feature based method SIFT based algorithm

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6. 6 1)Calculating: 2)Applying high pass filter 3)Transform to log-polar images: 4)Calculating:

7. Control and Robotics Laboratory7 Original image FFT in Cartesian coordinates FFT in Log-polar coordinates Rotated image FFT in Cartesian coordinates FFT in Log-polar coordinates

8. Control and Robotics Labaratory8 5)Computing R1: 6)Extracting the rotation and angle parameters: 7)Constructing I3 using extracted parameters 8)Repeating step 5-6 for images I1 and I3

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11. Control and Robotics Labaratory11 Number of frames: 46 (full movie) Time elapsed during creation: 11 seconds

12. Control and Robotics Labaratory12 I.The algorithm only works for two images of the exact same size. Especially for the rotation and scale computation, images also need to be square. II.The algorithm requires images that have an overlapping area larger than 30%. III.The algorithm only works for images in which the scale changes less than 1.8. Otherwise, the criterion of 30% overlapping area is not satisfied. IV.We cannot get full homography parameters for creating the panorama.

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14. Control and Robotics Labaratory14 Extract features from frames. (SIFT) Find and match common features between two adjusted frames Obtain a transformation between the frames. (RANSAC) Stitch the transformed frames together (WEIGHTED STITCHING)

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16. Control and Robotics Labaratory16 choose ‘n’ data points (Hypothetical inliers) Calculate the result (Hypothesize) Find all data points which meets error<‘Threshold’ ( Consensus) Return ‘k’ times. Use the best result till now and its consensus to obtain the final result.

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19. Control and Robotics Labaratory19 Affine transform creates black regions We create masks of the not black regions. Addition of frame by: (New Frame)*Mask+(Old Frames)*( 1 -Mask)

20. Control and Robotics Labaratory20 Bilinear interpolation creates artifacts on the edges. Remove two pixels from mask edges.

21. Control and Robotics Labaratory21 Multiply the masks by a function with minimal value on the edges and maximum in the frame center. Stitch using:

22. Control and Robotics Labaratory22 Weighted stitching of each two adjusted frames Fast transition function Smoothing is decreased by averaging on less frames, with lesser index difference between them.

23. Control and Robotics Labaratory23 Implemented two algorithms to obtain panorama. Researched and optimized the parameters. Used masks to stitch frames and delete border artifacts. Implemented two stitching methods for smoothing the transitions between frames. Obtained panorama using models of Translation, Euclidean, Similarity and Affine transforms.

24. Control and Robotics Labaratory24 Direct alignment method Fast Gives satisfying results for a simple Translation model May also be used for Euclidean and Similarity models Feature based method Reliable and robust Can resolve complex transformation models Sub pixel accuracy is required for complex transforms Weighted stitching is sharper, but in some cases less accurate than the normalized weighted sum.