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High-dimensional and low-quality vis...
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Deng, Yue.
High-dimensional and low-quality visual information processingfrom structured sensing and understanding /
Record Type:
Electronic resources : Monograph/item
Title/Author:
High-dimensional and low-quality visual information processingby Yue Deng.
Reminder of title:
from structured sensing and understanding /
Author:
Deng, Yue.
Published:
Berlin, Heidelberg :Springer Berlin Heidelberg :2015.
Description:
xv, 99 p. :ill., digital ;24 cm.
Contained By:
Springer eBooks
Subject:
Computer vision.
Online resource:
http://dx.doi.org/10.1007/978-3-662-44526-6
ISBN:
9783662445266 (electronic bk.)
High-dimensional and low-quality visual information processingfrom structured sensing and understanding /
Deng, Yue.
High-dimensional and low-quality visual information processing
from structured sensing and understanding /[electronic resource] :by Yue Deng. - Berlin, Heidelberg :Springer Berlin Heidelberg :2015. - xv, 99 p. :ill., digital ;24 cm. - Springer theses,2190-5053. - Springer theses..
Introduction -- Sparse Structure for Visual Signal Sensing -- Graph Structure for Visual Signal Sensing -- Discriminative Structure for Visual Signal Understanding -- Information Theoretic Structure for Visual Signal Understanding -- Conclusions.
This thesis primarily focuses on how to carry out intelligent sensing and understand the high-dimensional and low-quality visual information. After exploring the inherent structures of the visual data, it proposes a number of computational models covering an extensive range of mathematical topics, including compressive sensing, graph theory, probabilistic learning and information theory. These computational models are also applied to address a number of real-world problems including biometric recognition, stereo signal reconstruction, natural scene parsing, and SAR image processing.
ISBN: 9783662445266 (electronic bk.)
Standard No.: 10.1007/978-3-662-44526-6doiSubjects--Topical Terms:
200113
Computer vision.
LC Class. No.: TA1634
Dewey Class. No.: 006.37
High-dimensional and low-quality visual information processingfrom structured sensing and understanding /
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Introduction -- Sparse Structure for Visual Signal Sensing -- Graph Structure for Visual Signal Sensing -- Discriminative Structure for Visual Signal Understanding -- Information Theoretic Structure for Visual Signal Understanding -- Conclusions.
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This thesis primarily focuses on how to carry out intelligent sensing and understand the high-dimensional and low-quality visual information. After exploring the inherent structures of the visual data, it proposes a number of computational models covering an extensive range of mathematical topics, including compressive sensing, graph theory, probabilistic learning and information theory. These computational models are also applied to address a number of real-world problems including biometric recognition, stereo signal reconstruction, natural scene parsing, and SAR image processing.
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EB TA1634 D392 2015
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http://dx.doi.org/10.1007/978-3-662-44526-6
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