161 research outputs found
Stick-Breaking Policy Learning in Dec-POMDPs
Expectation maximization (EM) has recently been shown to be an efficient
algorithm for learning finite-state controllers (FSCs) in large decentralized
POMDPs (Dec-POMDPs). However, current methods use fixed-size FSCs and often
converge to maxima that are far from optimal. This paper considers a
variable-size FSC to represent the local policy of each agent. These
variable-size FSCs are constructed using a stick-breaking prior, leading to a
new framework called \emph{decentralized stick-breaking policy representation}
(Dec-SBPR). This approach learns the controller parameters with a variational
Bayesian algorithm without having to assume that the Dec-POMDP model is
available. The performance of Dec-SBPR is demonstrated on several benchmark
problems, showing that the algorithm scales to large problems while
outperforming other state-of-the-art methods
Low-Cost Compressive Sensing for Color Video and Depth
A simple and inexpensive (low-power and low-bandwidth) modification is made
to a conventional off-the-shelf color video camera, from which we recover
{multiple} color frames for each of the original measured frames, and each of
the recovered frames can be focused at a different depth. The recovery of
multiple frames for each measured frame is made possible via high-speed coding,
manifested via translation of a single coded aperture; the inexpensive
translation is constituted by mounting the binary code on a piezoelectric
device. To simultaneously recover depth information, a {liquid} lens is
modulated at high speed, via a variable voltage. Consequently, during the
aforementioned coding process, the liquid lens allows the camera to sweep the
focus through multiple depths. In addition to designing and implementing the
camera, fast recovery is achieved by an anytime algorithm exploiting the
group-sparsity of wavelet/DCT coefficients.Comment: 8 pages, CVPR 201
Adaptive Temporal Compressive Sensing for Video
This paper introduces the concept of adaptive temporal compressive sensing
(CS) for video. We propose a CS algorithm to adapt the compression ratio based
on the scene's temporal complexity, computed from the compressed data, without
compromising the quality of the reconstructed video. The temporal adaptivity is
manifested by manipulating the integration time of the camera, opening the
possibility to real-time implementation. The proposed algorithm is a
generalized temporal CS approach that can be incorporated with a diverse set of
existing hardware systems.Comment: IEEE Interonal International Conference on Image Processing
(ICIP),201
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