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Technical Program
Paper Detail
| Paper: | MA-P4.4 |
| Session: | Image Registration/Alignment and Mosaicking |
| Time: | Monday, October 9, 09:40 - 12:20 |
| Presentation: |
Poster
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| Title: |
IMAGE ALIGNMENT USING LEARNING PRIOR APPEARANCE MODEL |
| Authors: |
Ayman El-Baz; University of Louisville | | | | | Aly A. Farag; University of Louisville | | | | | Georgy Gimel'farb; University of Auckland | | | | | Alaa Abdel-Hakim; University of Louisville | | |
| Abstract: |
A new approach to align an image of a textured object with a given prototype is proposed. Visual appearance of the images, after equalizing their signals, is modeled with a Markov-Gibbs random field with pairwise interaction. Similarity to the prototype is measured by a Gibbs energy of signal cooccurrences in a characteristic subset of pixel pairs derived automatically from the prototype. An object is aligned by an affine transformation maximizing the similarity by using an automatic initialization followed by gradient search. Experiments confirm that our approach aligns complex objects better than popular conventional algorithms. |
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