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High-Dimensional and Low-Quality Visual Information Processing

From Structured Sensing and Understanding

  • Yue Deng

Part of the Springer Theses book series (Springer Theses)

Table of contents

About this book

Introduction

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.

Keywords

Compressive Sensing Computer Vision Discriminative Learning, Information Theory, Optimization Image Processing Machine Learning Manifold Learning

Authors and affiliations

  • Yue Deng
    • 1
  1. 1.Tsinghua UniversityBeijingChina

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-662-44526-6
  • Copyright Information Springer-Verlag Berlin Heidelberg 2015
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-662-44525-9
  • Online ISBN 978-3-662-44526-6
  • Series Print ISSN 2190-5053
  • Series Online ISSN 2190-5061
  • Buy this book on publisher's site
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