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An Online Algorithm for Separating S...
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Guo, Han.
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An Online Algorithm for Separating Sparse and Low-Dimensional Signal Sequences From Their Sum, and Its Applications in Video Processing.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
An Online Algorithm for Separating Sparse and Low-Dimensional Signal Sequences From Their Sum, and Its Applications in Video Processing./
作者:
Guo, Han.
出版者:
Ann Arbor : ProQuest Dissertations & Theses, : 2019,
面頁冊數:
102 p.
附註:
Source: Dissertations Abstracts International, Volume: 81-05, Section: B.
Contained By:
Dissertations Abstracts International81-05B.
標題:
Applied mathematics. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=22587332
ISBN:
9781088353356
An Online Algorithm for Separating Sparse and Low-Dimensional Signal Sequences From Their Sum, and Its Applications in Video Processing.
Guo, Han.
An Online Algorithm for Separating Sparse and Low-Dimensional Signal Sequences From Their Sum, and Its Applications in Video Processing.
- Ann Arbor : ProQuest Dissertations & Theses, 2019 - 102 p.
Source: Dissertations Abstracts International, Volume: 81-05, Section: B.
Thesis (Ph.D.)--Iowa State University, 2019.
This item must not be sold to any third party vendors.
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.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.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.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.
ISBN: 9781088353356Subjects--Topical Terms:
2122814
Applied mathematics.
Subjects--Index Terms:
Compressive sensing
An Online Algorithm for Separating Sparse and Low-Dimensional Signal Sequences From Their Sum, and Its Applications in Video Processing.
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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.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.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.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.
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