Turbulence mitigation refers to the stabilization of videos with non-uniform deformations due to the influence of optical
turbulence. Typical approaches for turbulence mitigation follow averaging or de-warping techniques. Although these methods
can reduce the turbulence, they distort the independently moving objects which can often be of great interest. In this paper,
we address the novel problem of simultaneous turbulence mitigation and moving object detection. We propose a novel threeterm
low-rank matrix decomposition approach in which we decompose the turbulence sequence into three components: the
background, the turbulence, and the object...Recent Projects
Seeing Through Turbulence (TPAMI 12)
Turbulence mitigation refers to the stabilization of videos with non-uniform deformations due to the influence of optical
turbulence. Typical approaches for turbulence mitigation follow averaging or de-warping techniques. Although these methods
can reduce the turbulence, they distort the independently moving objects which can often be of great interest. In this paper,
we address the novel problem of simultaneous turbulence mitigation and moving object detection. We propose a novel threeterm
low-rank matrix decomposition approach in which we decompose the turbulence sequence into three components: the
background, the turbulence, and the object...
Seeing Through Water (CVPR 11)
Several attempts have been lately proposed to tackle the
problem of recovering the original image of an underwater
scene using a sequence distorted by water waves. The
main drawback of the state of the art is that it heavily
depends on modelling the waves, which in fact is illposed
since the actual behavior of the waves along with the
imaging process are more complicated, and include several
noise components; therefore, their results are not satisfactory.
In this paper, we revisit the problem by proposing a
data-driven two-stage approach, each stage is targeted toward
a certain type of noise....
Horizon Constraint for Unambiguous UAV Navigation (ICRA 2011)
When the UAV goes to high altitudes such that
the observed surface of the earth becomes planar, the structure
and motion recovery of the earth's moving plane becomes
ambiguous. This planar degeneracy has been pointed out very
often in the literature; therefore, current navigation methods
either completely fail or give many confusing solutions in such
scenario. Interestingly, the horizon line in planar scenes is
straight and distinctive; hence, easily detected. Therefore, we
show in this paper that the horizon line provides two degrees
of freedom that control the relative orientation between the
camera coordinate system and the local surface of earth. The
recovered degrees of freedom help linearize and disambiguate
the planar flow, and therefore we obtain a unique solution for
the UAV motion estimation....
Action Recognition By Motion Decomposition (ICCV 2011)
Recognition of actions in a video acquired by a
moving camera typically requires standard
steps such as motion compensation, moving object detection
and object tracking. The errors from the motion compensation propagate to the object detection stage, resulting
in miss-detections, which further complicates the tracking
stage, resulting in cluttered and incorrect tracks. In this project, we propose a novel approach
which does not follow the standard steps, and
avoids the aforementioned difficulties. Our approach is
based on Lagrangian particle trajectories which are a set of
dense trajectories obtained by advecting optical flow over
time, thus capturing the ensemble motions of a scene...
Identity Recognition in Aerial Images (CVPR 2010)
Human identity recognition is an important yet underaddressed
problem. Previous methods were strictly limited
to high quality photographs, where the principal techniques
heavily rely on body details such as face detection. In this
paper, we propose an algorithm to address the novel problem
of human identity recognition over a set of unordered
low quality aerial images...Publications
