By Dr David Capel (auth.)
This booklet investigates units of pictures which include many overlapping viewsofa scene, and the way the data contained inside them could be mixed to supply unmarried photographs of stronger caliber. The favourite identify for such options is body fusion. utilizing body fusion, it's attainable to increase the fieldof view past that ofany unmarried photograph, to lessen noise, to revive high-frequency content material, or even to extend spatial answer and dynamic diversity. the purpose during this ebook is to improve effective, strong and automatic body fusion algorithms that may be utilized to actual picture sequences. a necessary step required to allow body fusion is picture registration: computing the point-to-point mapping among photos of their overlapping area. This sub challenge is taken into account intimately, and a powerful and effective resolution is proposed and its accuracy evaluated. sorts of body fusion are then thought of: photograph mosaic ing and super-resolution. photograph mosaicing is the alignment of a number of photographs 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 by means of mod elling and elimination the degradations inherent within the imaging procedure, resembling noise, blur and spatial-sampling. A key point during this e-book is the belief of a very uncalibrated cam period. No past wisdom of the digital camera parameters, its movement, optics or photometric features is thought. the ability of the tools is illustrated with many actual photograph series examples.
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Extra info for Image Mosaicing and Super-resolution
10 shows the distribution of the individual x and y components of the d' computed using corresponding feature-points in images 1 and 2 from the "Mosaic" and "Lego" test sets. Given our assumptions, we expect the two components to be independently and normally distributed with equal variances. The fitted normal distributions are overlaid on each histogram , from which the normality assumption seems reasonable . In fact, the normality may be verified using a X 2 goodness-of-fit test [142,160] . 5 "Mo.
1 Assessment criteria The correct way to assess the qualit y of an estimated homography It is to compare it with the ground-truth transformation H, Given such a ground-truth, we require a method of comparison which provides a meaningful and intuitive measure of the accuracy of the estimated homography. Simple, direct compariso ns of the estimated parameters are of little value in this respect. - (X2) - H (3 . lOa) (X2) (3 . lOb) where the notation H(x) indicates the mapping of an inhomogeneous 2D point x under a homography.
7 in order to bring the control image into accurate alignment with the evidence image, both geometrically and photometrically. The steps are as follows. 1. 4. 2. 7. 3. Attenuation Using the estimated photometric parameters, transform the colour channels of the control image. The two images should now be exactly alike modulo the latent mark. Subtract the control image from the evidence image to obtain the separated image. Often, such as is the case with bank notes imaged using a flat-bed scanner, a full 8 dof homography is unnecessary for aligning the two images.