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The World of Fast Moving Objects
The notion of a Fast Moving Object (FMO), i.e. an object that moves over a
distance exceeding its size within the exposure time, is introduced. FMOs may,
and typically do, rotate with high angular speed. FMOs are very common in
sports videos, but are not rare elsewhere. In a single frame, such objects are
often barely visible and appear as semi-transparent streaks.
A method for the detection and tracking of FMOs is proposed. The method
consists of three distinct algorithms, which form an efficient localization
pipeline that operates successfully in a broad range of conditions. We show
that it is possible to recover the appearance of the object and its axis of
rotation, despite its blurred appearance. The proposed method is evaluated on a
new annotated dataset. The results show that existing trackers are inadequate
for the problem of FMO localization and a new approach is required. Two
applications of localization, temporal super-resolution and highlighting, are
presented
Statistical performance analysis of a fast super-resolution technique using noisy translations
It is well known that the registration process is a key step for
super-resolution reconstruction. In this work, we propose to use a
piezoelectric system that is easily adaptable on all microscopes and telescopes
for controlling accurately their motion (down to nanometers) and therefore
acquiring multiple images of the same scene at different controlled positions.
Then a fast super-resolution algorithm \cite{eh01} can be used for efficient
super-resolution reconstruction. In this case, the optimal use of images
for a resolution enhancement factor is generally not enough to obtain
satisfying results due to the random inaccuracy of the positioning system. Thus
we propose to take several images around each reference position. We study the
error produced by the super-resolution algorithm due to spatial uncertainty as
a function of the number of images per position. We obtain a lower bound on the
number of images that is necessary to ensure a given error upper bound with
probability higher than some desired confidence level.Comment: 15 pages, submitte
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