28,570 research outputs found
Video Compressive Sensing for Dynamic MRI
We present a video compressive sensing framework, termed kt-CSLDS, to
accelerate the image acquisition process of dynamic magnetic resonance imaging
(MRI). We are inspired by a state-of-the-art model for video compressive
sensing that utilizes a linear dynamical system (LDS) to model the motion
manifold. Given compressive measurements, the state sequence of an LDS can be
first estimated using system identification techniques. We then reconstruct the
observation matrix using a joint structured sparsity assumption. In particular,
we minimize an objective function with a mixture of wavelet sparsity and joint
sparsity within the observation matrix. We derive an efficient convex
optimization algorithm through alternating direction method of multipliers
(ADMM), and provide a theoretical guarantee for global convergence. We
demonstrate the performance of our approach for video compressive sensing, in
terms of reconstruction accuracy. We also investigate the impact of various
sampling strategies. We apply this framework to accelerate the acquisition
process of dynamic MRI and show it achieves the best reconstruction accuracy
with the least computational time compared with existing algorithms in the
literature.Comment: 30 pages, 9 figure
Coding local and global binary visual features extracted from video sequences
Binary local features represent an effective alternative to real-valued
descriptors, leading to comparable results for many visual analysis tasks,
while being characterized by significantly lower computational complexity and
memory requirements. When dealing with large collections, a more compact
representation based on global features is often preferred, which can be
obtained from local features by means of, e.g., the Bag-of-Visual-Word (BoVW)
model. Several applications, including for example visual sensor networks and
mobile augmented reality, require visual features to be transmitted over a
bandwidth-limited network, thus calling for coding techniques that aim at
reducing the required bit budget, while attaining a target level of efficiency.
In this paper we investigate a coding scheme tailored to both local and global
binary features, which aims at exploiting both spatial and temporal redundancy
by means of intra- and inter-frame coding. In this respect, the proposed coding
scheme can be conveniently adopted to support the Analyze-Then-Compress (ATC)
paradigm. That is, visual features are extracted from the acquired content,
encoded at remote nodes, and finally transmitted to a central controller that
performs visual analysis. This is in contrast with the traditional approach, in
which visual content is acquired at a node, compressed and then sent to a
central unit for further processing, according to the Compress-Then-Analyze
(CTA) paradigm. In this paper we experimentally compare ATC and CTA by means of
rate-efficiency curves in the context of two different visual analysis tasks:
homography estimation and content-based retrieval. Our results show that the
novel ATC paradigm based on the proposed coding primitives can be competitive
with CTA, especially in bandwidth limited scenarios.Comment: submitted to IEEE Transactions on Image Processin
Total Variation Regularized Tensor RPCA for Background Subtraction from Compressive Measurements
Background subtraction has been a fundamental and widely studied task in
video analysis, with a wide range of applications in video surveillance,
teleconferencing and 3D modeling. Recently, motivated by compressive imaging,
background subtraction from compressive measurements (BSCM) is becoming an
active research task in video surveillance. In this paper, we propose a novel
tensor-based robust PCA (TenRPCA) approach for BSCM by decomposing video frames
into backgrounds with spatial-temporal correlations and foregrounds with
spatio-temporal continuity in a tensor framework. In this approach, we use 3D
total variation (TV) to enhance the spatio-temporal continuity of foregrounds,
and Tucker decomposition to model the spatio-temporal correlations of video
background. Based on this idea, we design a basic tensor RPCA model over the
video frames, dubbed as the holistic TenRPCA model (H-TenRPCA). To characterize
the correlations among the groups of similar 3D patches of video background, we
further design a patch-group-based tensor RPCA model (PG-TenRPCA) by joint
tensor Tucker decompositions of 3D patch groups for modeling the video
background. Efficient algorithms using alternating direction method of
multipliers (ADMM) are developed to solve the proposed models. Extensive
experiments on simulated and real-world videos demonstrate the superiority of
the proposed approaches over the existing state-of-the-art approaches.Comment: To appear in IEEE TI
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