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This project concerns the problem of fully automated panoramic image stitching. The 2D or multi-row stitching is more difficult. Previous approaches have used human input or restrictions on the image sequence in order to establish matching images. In this work, we formulate stitching as a multi-image matching problem, and use invariant local features to find matches between all of the images. Because of this our method is insensitive to the ordering, orientation, scale and illumination of the input images. It is also insensitive to noise images that are not part of a panorama, and can recognise multiple panoramas in an unordered image dataset.

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Course Currilcum

    • Image Homogeneity Reference Paper 00:00:00
    • Image Homogeneity Synopsis 00:00:00
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