High-Dimensional and Low-Quality Visual Information Processing From Structured Sensing and Understanding
Deng, Yue.
High-Dimensional and Low-Quality Visual Information Processing From Structured Sensing and Understanding by Yue Deng. - 1 recurso en línea (XV, 99 páginas 23 ilustraciones, 18 ilustraciones a color.) - Springer Theses, Recognizing Outstanding Ph.D. Research, 2190-5053 Engineering (Springer-11647) .
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.
9783662445266
10.1007/978-3-662-44526-6 doi
Fotónica
TA1634 / .D464 2015 EB
High-Dimensional and Low-Quality Visual Information Processing From Structured Sensing and Understanding by Yue Deng. - 1 recurso en línea (XV, 99 páginas 23 ilustraciones, 18 ilustraciones a color.) - Springer Theses, Recognizing Outstanding Ph.D. Research, 2190-5053 Engineering (Springer-11647) .
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.
9783662445266
10.1007/978-3-662-44526-6 doi
Fotónica
TA1634 / .D464 2015 EB