High-Dimensional and Low-Quality Visual Information Processing
From Structured Sensing and Understanding
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Beschreibung
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.
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.
Nominated by Tsinghua University as an outstanding Ph.D. thesis Proposes a number of computational models to handle the Big Data challenges in visual information processing Solves a number of real-world computer vision tasks that includes biometric recognition, 3D reconstruction, natural scene parsing and SAR image understanding Includes supplementary material: sn.pub/extras
Autor*in
Yue Deng
Themen in »High-Dimensional and Low-Quality Visual Information Processing«
Compressive Sensing Discriminative Learning, Information Theory, Optimization Image Processing Machine Learning Manifold Learning