High-Dimensional and Low-Quality Visual Information Processing From Structured Sensing and Understanding /

• Κύριος συγγραφέας: Deng, Yue (Συγγραφέας)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2015.
• Σειρά: Springer Theses, Recognizing Outstanding Ph.D. Research,
• Θέματα: Engineering.; Data structures (Computer science).; Data mining.; Image processing.; Signal, Image and Speech Processing.; Image Processing and Computer Vision.; Data Structures, Cryptology and Information Theory.; Data Mining and Knowledge Discovery.

 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. 

 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.