Image alignment and feature extraction

[Ankit Agrawal]

Hi Stuart,

OK I need to plead ignorance here, is there a decent reference on / can
> someone explain to me:
> a) what ORB is :p
>
ORB is a feature detection and description pipeline like SIFT and contains
Oriented FAST(oFAST) and Rotated BRIEF(rBRIEF) as its feature detector and
feature descriptor respectively. In short, Oriented FAST feature detector
is the FAST corner detector computed along with its orientation(which is
computed as in [1] Section 3.2). Rotated BRIEF is the BRIEF feature
descriptor computed by rotating the test-decision pairs along the
orientation of oFAST. Refer [2] and [1].

b) What the difference between FAST, oFAST and rBRIEF and STAR (and FREAK?)
> is and which one likely to be the most appropriate for random pictures of
> the Sun?
>
Currently, I cannot answer this question in complete confidence, partly
because I am yet to complete and test FREAK and partly because I am not
aware of the data, or the kind of solar images that are going to be
aligned. Can you give some examples of pairs of images that are required to
be aligned in Solar Physics. Some queries that one would have about the
data are like : Are the solar images that need to be aligned generated from
the same camera but at different time intervals? Is the scaling or the zoom
factor changing? Is it only the Sun's rotation about its axis that is
generating these two different images or some other factor is also
involved? Currently, ORB is the best bet, because of its speed and
invariance to both scaling and rotation.

c) What else needs to be done to turn this feature detection and
> description work into registration?
>
Image Registration Pipeline can be summarized as : Detect
features(keypoints) --> Compute Descriptors --> Match keypoints --> Treat
one image as the reference and Estimate the Geometric Transformation
required to get the second image using the matches. ORB does the first
three and the 4th stage is what needs to be done. These slides[3] explain
it better.

[1] http://www.vision.cs.chubu.ac.jp/CV-R/pdf/Rublee_iccv2011.pdf
[2] http://cvlabwww.epfl.ch/~lepetit/papers/calonder_eccv10.pdf
[3]
http://cs.haifa.ac.il/hagit/courses/CP/Lectures/CP05_FeaturesRegistX4.pdf
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