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III.1. Object Registration and
Tracking.
III.2. Fusion of frequency and
spatial domain data for motion estimation and segmetnation
Research in digital video
technology is constantly evolving, as new communications systems are
increasingly using multimedia content. Thus, the analysis of multiple
motions in video is particularly important, as it allows the
characterization of the sequence, and its segmentation into differently
moving entities. Numerous methods have been developed for the motion
estimation in video, using information from the spatial or spectral
domain. We investigate new approaches for the analysis of multiple
motions in video, which integrate Fourier and spatial domain
information.
The tasks of interest are finding the number of moving
objects, velocity estimation, object tracking, and motion segmentation.
The proposed, hybrid approach, performs the motion estimation based on
frequency domain information, but also uses spatial information for
precise object localization. Unlike existing frequency domain methods,
its use is not limited to constant translational motions, but can also
address the problem of roto-translational and non-constant motions. The
validity, effectiveness, and potential of the proposed approaches is
verified through experiments with both synthetic and real video
sequences.
Another task of interest tackled by
our group is the estimation of the positions of multiple translating
objects in a video sequence, via a different hybrid approach to motion
estimation.
We suggest minimizing the squared error in both the spatial and
frequency domains. We show that, both theoretically and experimentally,
the spatially global nature of FT leads to a motion estimation error
that is much lower than that obtained via spatial motion estimation. On
the other hand, spatial analysis is useful for accurate segmentation.
We thus describe a new, hybrid approach combining the above two
estimates of motion and segmentation. We examine the robustness
of minimizing the error terms in both domains, both theoretically and
experimentally. Experiments with real and synthetic sequences
demonstrate the capabilities of the proposed algorithm.
There is great interest in the
detection and analysis of repetitive motions, as they are appear in
numerous applications. Their analysis can lead to useful results in
recognition and classification, concerning human and animal activities,
or even dynamic objects. It also plays a central role in characterizing
the motions in a video as purely periodic, purely translational, or a
combination of both. Existing work on periodic motions takes place in
the spatial domain and involves the extraction of motion trajectories
via feature tracking or region matching. This often requires human
intervention, or is otherwise computationally costly, and often
unreliable. Also, current methods cannot deal with motions that are not
purely periodic without some form of pre-processing. We develop a novel
approach for the detection and analysis of periodic trajectories over a
static background, which avoids the shortcomings of existing spatial
approaches by combining time-frequency and spatial analysis. It is able
to detect periodicities that are pure or superposed on translational
motions, thus allowing the characterization of the motions in a video.
We show, both theoretically and experimentally, that our method is
robust under Gaussian deviations from strict periodicity as well. The
capabilities and improvements of this approach on existing methods are
shown through experiments on both synthetic and real sequences.
III.3. 3D Motion and Structure from
Image Motion, computed from a video
sequence showing a scene with moving objects and taken from a still or
moving
camera.
III.4. 3D Motion and Structure from Two-View motion (Temporal
Stereo) , based on the disparity between two images
taken from one camera but at two different times.
III.5. 3D Surfaces and
Motion from multiple video sequences, showing moving objects and
taken from multiple cameras.
II.6. 3D Surfaces from
Active Stereo, Focus, Vergence, Zoom and Aperture, from the
parameter values assumed during active acquisition of video
sequences from multiple cameras.
III.7. Integrated Analysis
Guided Synthesis of Video Sequences, for 3D based compression,
augmentation and synthesis of video sequences, aimed at enhancing
the perception of specific visual characteristics, e.g.,
motion.
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