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PANORAMA STITCHING

january
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PANORAMA STITCHING

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    1. PANORAMA STITCHING Abhishek Agarwal Manas Agrawal Raghav Agrawal

    2. Panorama Stitching Combining multiple photographic images with overlapping fields of view Producing a segmented panorama or high-resolution image

    3. Approach Parameter Estimation Shift Estimation Projection Estimation Image Registration: Transforming all the images to a common image plane. Blending: Stitching the images compensate for exposure differences and other mis-alignments

    4. Direct (pixel-based) Parameter Estimation: Fourier Based shift estimation using phase correlation. Blending: Average shift based

    5. Feature Based Stitching

    6. Feature Point Detector Various Approaches- Difference of Gauss (DoG) Bad Stability, Too many Scale Space Filter Speeded Up Robust Feature (SURF) Using the integral image to filter the image reduce the precision and stability Harris/Laplace Good stability, relatively inefficient, which is balanced by the reduction of scale samples Harris/Affine Too Complex

    7. Feature Detection Harris

    8. Harris Detector Change in appearance for shift [u,v]

    9. Harris Detector - steps Compute Gaussian derivatives at each pixel Compute second moment matrix M in a Gaussian window around each pixel Compute corner response function R Threshold R Find local maxima of response function (non-maximum suppression)

    10. Harris Detector - steps

    11. Harris Detector - steps

    12. Harris Detector - steps

    13. Harris Detector – steps

    14. Harris/Laplace Scale Adapted Harris Corner Detector Scale Adapted M

    15. FEATURE MATCHING SIFT(scale invariant feature transform) SIFT is the most popular feature descriptor because of its stability and precision highly outperforms other descriptors. Each feature is assigned a descriptor vector and feature vectors are matched in different images. Advantage of SIFT Scale and rotation invariant Resistance of Noise in position and intensity High match precision

    16. Implementation Get the 16*16 Neighbor of every feature point. Find the gradient of every pixel in the Neighborhood. Build an orientation histogram for the feature point. Assign the feature point a principal orientation. The gradient of every pixel in the Neighborhood is rotated according to the principal orientation (for rotation invariance) Split the 16*16 Neighborhood into 4*4 squares, calculate the orientation distribution in each square, and build a descriptor. Normalize the descriptor vector

    17. Feature Matching Best match is defined as the minimum of the sum of the absolute difference between descriptor vectors. There are several speeded up matching method.

    18. PARAMETER ESTIMATION Transformation Matrix Estimation Transformation Model Principle (RANSAC)

    19. Blending Gain Compensation Since the transform matrix is known, the two image can be simply aligned together. The stitched image here has obvious artifact. The most direct one is the difference of intensity. Gain Compensation is a fundamental way to eliminate intensity difference.

    20. Implementation (Gain Compensation) Find the overlap region of the two aligned image. Calculate the average intensity of the overlap region of the two image respectively. Calculate the difference of the average intensity of the overlapped region. Add the difference of the average intensity to one of the image wholly, Then ,the two image’ average intensity in the overlapped region should be the same.

    21. References R. Szeliski. Image alignment and stitching: A tutorial. Technical Report MSR-TR-2004-92, Microsoft Research, Last updated, December 10, 2006. D. Lowe. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2):91¨C110, 2004. M. Fischler and R. Bolles. Random sample consensus: A paradigm for model fitting with application to image analysis and automated cartography. Communications of the ACM, 24:381¨C395, 1981. M. Brown and D. G. Lowe. Automatic panoramic image stitching using invariant features. Int. J. of Computer Vision, 74(1):59¨C73, 2006. Brown, M.; Lowe, D.G.. Recognising panoramas. Computer Vision, 2003. Proceedings. Ninth IEEE International Conference on. 13-16 Oct. 2003 Page(s):1218 - 1225 vol.2

    22. QUESTIONS??

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