High-Dimensional and Low-Quality Visual Information Processing [electronic resource] : From Structured Sensing and Understanding /
Deng, Yue.author.
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.
Description based upon print version of record.
Doctoral thesis accepted by Tsinghua University, Beijing, China.
Includes bibliographical references and index.
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.
Signal processing.
Image processing.
Speech processing systems.
Optical data processing.
Data structures (Computer science).
Data mining.
Signal, Image and Speech Processing.
Image Processing and Computer Vision.
Data Structures and Information Theory.
Data Mining and Knowledge Discovery.