An online algorithm for separating sparse and low-dimensional signal sequences from their sum, and its applications in video processing
dc.contributor.advisor | Namrata Vaswani | |
dc.contributor.author | Guo, Han | |
dc.contributor.department | Electrical and Computer Engineering | |
dc.date | 2019-11-04T21:48:47.000 | |
dc.date.accessioned | 2020-06-30T03:18:34Z | |
dc.date.available | 2020-06-30T03:18:34Z | |
dc.date.copyright | Thu Aug 01 00:00:00 UTC 2019 | |
dc.date.embargo | 2001-01-01 | |
dc.date.issued | 2019-01-01 | |
dc.description.abstract | <p>In signal processing, ``low-rank + sparse'' is an important assumption when separating two signals from their sum. Many applications, e.g., video foreground/background separation are well-formulated by this assumption. In this work, with the ``low-rank + sparse'' assumption, we design and evaluate an online algorithm, called practical recursive projected compressive sensing (prac-ReProCS) for recovering a time sequence of sparse vectors St and a time sequence of dense vectors Lt from their sum, Mt = St + Lt, when the Lt's lie in a slowly changing low-dimensional subspace of the full space.</p> <p>In the first part of this work (Chapter 1-5), we study and discuss the prac-ReProCS algorithm, the practical version of the original ReProCS algorithm. We apply prac-ReProCS to a key application -- video layering, where the goal is to separate a video sequence into a slowly changing background sequence and a sparse foreground sequence that consists of one or more moving regions/objects on-the-fly. Via experiments we show that prac-ReProCS has significantly better performance compared with other state-of-the-art robust-pca methods when applied to video foreground-background separation.</p> <p>In the second part of this work (Chapter 6), we study the problem of video denoising. We apply prac-ReProCS to video denoising as a preprocessing step. We develop a novel approach to video denoising that is based on the idea that many noisy or corrupted videos can be split into three parts -- the ``low-rank laye'', the ``sparse layer'' and a small residual which is small and bounded. We show using extensive experiments, layering-then-denoising is effective, especially for long videos with small-sized images that those corrupted by general large variance noise or by large sparse noise, e.g., salt-and-pepper noise.</p> <p>In the last part of this work (Chapter 7), we discuss an independent problem called logo detection and propose a future research direction where prac-ReProCS can be combined with deep learning solutions.</p> | |
dc.format.mimetype | application/pdf | |
dc.identifier | archive/lib.dr.iastate.edu/etd/17457/ | |
dc.identifier.articleid | 8464 | |
dc.identifier.contextkey | 15681449 | |
dc.identifier.s3bucket | isulib-bepress-aws-west | |
dc.identifier.submissionpath | etd/17457 | |
dc.identifier.uri | https://dr.lib.iastate.edu/handle/20.500.12876/31640 | |
dc.language.iso | en | |
dc.source.bitstream | archive/lib.dr.iastate.edu/etd/17457/Guo_iastate_0097E_18273.pdf|||Fri Jan 14 21:23:32 UTC 2022 | |
dc.subject.disciplines | Engineering | |
dc.subject.keywords | compressive sensing | |
dc.subject.keywords | low-rank | |
dc.subject.keywords | robust PCA | |
dc.subject.keywords | sparse recovery | |
dc.subject.keywords | video denoising | |
dc.subject.keywords | video layering | |
dc.title | An online algorithm for separating sparse and low-dimensional signal sequences from their sum, and its applications in video processing | |
dc.type | article | |
dc.type.genre | dissertation | |
dspace.entity.type | Publication | |
relation.isOrgUnitOfPublication | a75a044c-d11e-44cd-af4f-dab1d83339ff | |
thesis.degree.discipline | Electrical Engineering | |
thesis.degree.level | dissertation | |
thesis.degree.name | Doctor of Philosophy |
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