By Dr David Capel (auth.)
ISBN-10: 0857293842
ISBN-13: 9780857293848
ISBN-10: 1447110498
ISBN-13: 9781447110491
This booklet investigates units of pictures such as many overlapping viewsofa scene, and the way the knowledge contained inside of them might be mixed to supply unmarried photographs of stronger caliber. The favourite identify for such options is body fusion. utilizing body fusion, it really is attainable to increase the fieldof view past that ofany unmarried picture, to lessen noise, to revive high-frequency content material, or even to extend spatial solution and dynamic diversity. the purpose during this ebook is to strengthen effective, strong and automatic body fusion algorithms that may be utilized to actual photograph sequences. a vital step required to allow body fusion is photograph registration: computing the point-to-point mapping among pictures of their overlapping quarter. This sub challenge is taken into account intimately, and a strong and effective resolution is proposed and its accuracy evaluated. types of body fusion are then thought of: photo mosaic ing and super-resolution. photograph mosaicing is the alignment of a number of photos right into a huge composition which represents a part of a 3D scene. Super-resolution is a extra refined procedure which goals to revive poor-quality video sequences through mod elling and elimination the degradations inherent within the imaging procedure, resembling noise, blur and spatial-sampling. A key aspect during this booklet is the idea of a very uncalibrated cam period. No previous wisdom of the digicam parameters, its movement, optics or photometric features is thought. the facility of the tools is illustrated with many genuine picture series examples.
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Extra info for Image Mosaicing and Super-resolution
Sample text
The set of point correspondences deemed to be inliers is used to compute a refined estimate of H. Guided matching The refined homography estimate returned by the optimal estimation stage is used to guide a search for more interest point corre spondences. The search procedure is similar to that used to obtain the putative correspondences: given an interest point x in the first image, a match is sought in a search window centred on the expected position x ' = Hx in the second image. Because the search is now guided , the size of the search window can be greatly reduced, typically 3 x 3 pixels, and the correlation threshold below which matche s are discarded may be increa sed.
Tiny perturbations to these parameters can cause large movements in the transferred points. To relieve any remaining doubts, we can conduct an experiment using synthetic data. Sets of synthetic point correspondences are generated by transforming 300 randomly distributed points under known homographies. 25 pixels is added to the point positions. The noisy correspondences are then used to compute ML homography estimates . 1 pixels. J Conclusion As stated at the start of this section, the purpose of this exercise was to get a feel for the accuracy of our registration method , and to get some idea as to whether the assumptions made are valid when applied to the kind of images that we expect to be dealing with.
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Image Mosaicing and Super-resolution by Dr David Capel (auth.)
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