182 research outputs found
Online Structured Sparsity-based Moving Object Detection from Satellite Videos
Inspired by the recent developments in computer vision, low-rank and
structured sparse matrix decomposition can be potentially be used for extract
moving objects in satellite videos. This set of approaches seeks for rank
minimization on the background that typically requires batch-based optimization
over a sequence of frames, which causes delays in processing and limits their
applications. To remedy this delay, we propose an Online Low-rank and
Structured Sparse Decomposition (O-LSD). O-LSD reformulates the batch-based
low-rank matrix decomposition with the structured sparse penalty to its
equivalent frame-wise separable counterpart, which then defines a stochastic
optimization problem for online subspace basis estimation. In order to promote
online processing, O-LSD conducts the foreground and background separation and
the subspace basis update alternatingly for every frame in a video. We also
show the convergence of O-LSD theoretically. Experimental results on two
satellite videos demonstrate the performance of O-LSD in term of accuracy and
time consumption is comparable with the batch-based approaches with
significantly reduced delay in processing
Biometrics based privacy-preserving authentication and mobile template protection
Smart mobile devices are playing a more and more important role in our daily life. Cancelable biometrics is a promising mechanism to provide authentication to mobile devices and protect biometric templates by applying a noninvertible transformation to raw biometric data. However, the negative effect of nonlinear distortion will usually degrade the matching performance significantly, which is a nontrivial factor when designing a cancelable template. Moreover, the attacks via record multiplicity (ARM) present a threat to the existing cancelable biometrics, which is still a challenging open issue. To address these problems, in this paper, we propose a new cancelable fingerprint template which can not only mitigate the negative effect of nonlinear distortion by combining multiple feature sets, but also defeat the ARM attack through a proposed feature decorrelation algorithm. Our work is a new contribution to the design of cancelable biometrics with a concrete method against the ARM attack. Experimental results on public databases and security analysis show the validity of the proposed cancelable template
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