ALE
Image Processing Software
Deblurring, Anti-aliasing, and Superresolution.
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# Alignment

ALE aligns each supplemental frame, in sequence, with the merged rendering representing all previous frames. This page outlines the three supported transformation classes, the algorithm used for alignment, and the properties of the alignment algorithm. Following this is a discussion of practical use of alignment options, including alignment classes and alignment in the case of extended renderings.

## Transformations

ALE offers three classes of transformations:

Translations | introduced in version 0.0.0
Euclidean transformations (excluding reflections) | introduced in version 0.1.0
Projective transformations | introduced in version 0.2.0

## Algorithm

Alignment proceeds by a deterministic search, beginning with an initial transformation and modifying this transformation through a series of perturbations.

The initial transformation may be loaded from a file or selected by default. The default initial transformation is either the identity transformation or (when the `--follow` option is specified) the most recently merged frame's final alignment. (Note that changes in release 0.5.0 are not reflected here; these changes affect the interaction of the --follow and --trans-load flags.)

Once the initial transformation is determined, an initial perturbation amount is selected, and represents the step size by which each of the transformation parameters are changed. In translational or Euclidean alignment, the perturbation amount is applied to translation -- in units of pixels on the two image axes -- and rotation -- in units of degrees about the image center. (In version 0.4.8 and later, an additional configurable upper bound constrains rotational perturbation separately, preventing, e.g., a 360 degree perturbation of rotation.) In the case of projective alignment, the perturbation amount is applied to the position of the corners of the projected quadrilateral in units of pixels, where the projection is from the boundary of the supplemental image into the coordinate system of the accumulated image.

If possible, transformation parameters are changed to decrease the error between the two images being aligned. The perturbation amount is halved whenever it is determined that no parameter change of this size improves the alignment of the images. A lower bound on the perturbation amount determines when the alignment is complete.

The order in which parameters are considered for change is specified in the source code, and has the following property: No modified parameter is considered for further change until all other parameters have been considered. A consequence of this property is that parameters are always considered in a fixed (round robin) order.

When multiple levels of detail are used, the error may be calculated on images with a reduced level of detail. ALE versions 0.1.1 through 0.4.7 use a level of detail twice as fine as the perturbation amount for perturbation amounts larger than two, and full detail otherwise. Later versions default to this behavior, but can be configured differently. Earlier versions do not use reduced levels of detail.

## Properties

Several assumptions were made throughout the design and testing of the algorithm outlined above. These assumptions are outlined below.

The algorithm is based on a hill-climbing approach, which requires that any local minimum reachable from the starting point by traveling a path of decreasing error is also a global minimum (or, in this case, the correct alignment). While it is possible that the algorithm outlined above succeeds in some cases for which hill-climbing fails, it is still susceptible to entrapment in local minima.

As outlined above, depending on program options, transformation parameters may be changed by perturbations of several units (degrees or pixels) early in the alignment process. As long as no change of this magnitude moves the transformation out of the 'bowl' in which the minimum error -- and hence correct alignment -- lies, this is not a problem. However, it might break in some cases where a hill-climbing approach would succeed. (Notably, simulated annealing suffers from a similar problem, and it seems likely that a case could be constructed in such a way that the algorithm outlined above -- like simulated annealing -- could, contrarily, succeed where hill-climbing fails.)

Finally, the use of reduced level-of-detail relies on a high signal-to-noise ratio at low frequencies. Fortunately, this assumption seems to generally hold, but camera defects or radio interference could violate the assumption, possibly resulting in misalignment.

## Use of Alignment Classes

ALE is likely to be most useful when corresponding regions of different frames can be aligned by one of the available alignment classes.

As described by Steve Mann in his work on Video Orbits, the projective transformation offers particular versatility for camera imaging of (ideal Lambertian) flat scenes. In this case, any change in camera position and orientation can be corrected as long as points always have a defined projection onto the rendering plane (for which ALE uses the base of the pyramid R1).

In camera imaging of scenes with depth, correction for orientation is almost the same as for flat scenes, since, if focus and lens distortion is ignored, a scene with depth is indistinguishable from a flat scene from the perspective of a camera whose position is fixed.

For sequences of camera images with small changes in position or orientation, the projective transformations for alignment may closely approximate Euclidean transformations; in this case, using Euclidean transformations may achieve similar results and may require less time for alignment, since there are fewer parameters to tweak (three parameters instead of eight).

In the case of flatbed scanners that preserve the relative height and width of scans, any change in the position or orientation of flat objects can be corrected using the Euclidean alignment class.

If a flatbed scanner does not preserve relative height and width, but does preserve straight lines, then any change in the position or orientation of flat objects can be corrected with the projective alignment class.

However, even if a transformation is within the alignment class used, the alignment algorithm may still be unable to determine large transformations.

## Alignment in the case of Extended Renderings

By using the --extend flag, ALE can be used to create image mosaics spanning a spatial region larger than that represented by any single image in the frame sequence. In these cases, if adjacent frames in the sequence tend to be more closely aligned with each other than they are with the original frame, it may be helpful to also use the --follow flag as a hint to the alignment algorithm.

Copyright 2002, 2003 David Hilvert

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