7,208 research outputs found
An Improved Observation Model for Super-Resolution under Affine Motion
Super-resolution (SR) techniques make use of subpixel shifts between frames
in an image sequence to yield higher-resolution images. We propose an original
observation model devoted to the case of non isometric inter-frame motion as
required, for instance, in the context of airborne imaging sensors. First, we
describe how the main observation models used in the SR literature deal with
motion, and we explain why they are not suited for non isometric motion. Then,
we propose an extension of the observation model by Elad and Feuer adapted to
affine motion. This model is based on a decomposition of affine transforms into
successive shear transforms, each one efficiently implemented by row-by-row or
column-by-column 1-D affine transforms.
We demonstrate on synthetic and real sequences that our observation model
incorporated in a SR reconstruction technique leads to better results in the
case of variable scale motions and it provides equivalent results in the case
of isometric motions
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
An Efficient Algorithm for Video Super-Resolution Based On a Sequential Model
In this work, we propose a novel procedure for video super-resolution, that
is the recovery of a sequence of high-resolution images from its low-resolution
counterpart. Our approach is based on a "sequential" model (i.e., each
high-resolution frame is supposed to be a displaced version of the preceding
one) and considers the use of sparsity-enforcing priors. Both the recovery of
the high-resolution images and the motion fields relating them is tackled. This
leads to a large-dimensional, non-convex and non-smooth problem. We propose an
algorithmic framework to address the latter. Our approach relies on fast
gradient evaluation methods and modern optimization techniques for
non-differentiable/non-convex problems. Unlike some other previous works, we
show that there exists a provably-convergent method with a complexity linear in
the problem dimensions. We assess the proposed optimization method on {several
video benchmarks and emphasize its good performance with respect to the state
of the art.}Comment: 37 pages, SIAM Journal on Imaging Sciences, 201
Investigation of a new method for improving image resolution for camera tracking applications
Camera based systems have been a preferred choice in many motion tracking applications due to the ease of installation and the ability to work in unprepared environments. The concept of these systems is based on extracting image information (colour and shape properties) to detect the object location. However, the resolution of the image and the camera field-of- view (FOV) are two main factors that can restrict the tracking applications for which these systems can be used. Resolution can be addressed partially by using higher resolution cameras but this may not always be possible or cost effective.
This research paper investigates a new method utilising averaging of offset images to improve the effective resolution using a standard camera. The initial results show that the minimum detectable position change of a tracked object could be improved by up to 4 times
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