Academic Press Library in Signal Processing

Academic Press Library in Signal Processing

Image, Video Processing and Analysis, Hardware, Audio, Acoustic and Speech Processing

1st Edition - September 5, 2013

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  • Editors-in-Chief: Sergios Theodoridis, Rama Chellappa
  • eBook ISBN: 9780123972255
  • Hardcover ISBN: 9780123965011

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This fourth volume, edited and authored by world leading experts, gives a review of the principles, methods and techniques of important and emerging research topics and technologies in Image, Video Processing and Analysis, Hardware,  Audio, Acoustic and Speech Processing. With this reference source you will: Quickly grasp a new area of research  Understand the underlying principles of a topic and its application Ascertain how a topic relates to other areas and learn of the research issues yet to be resolved

Key Features

  • Quick tutorial reviews of important and emerging topics of research in Image, Video Processing and Analysis, Hardware, Audio, Acoustic and Speech Processing
  • Presents core principles and shows their application
  • Reference content on core principles, technologies, algorithms and applications
  • Comprehensive references to journal articles and other literature on which to build further, more specific and detailed knowledge
  • Edited by leading people in the field who, through their reputation, have been able to commission experts to write on a particular topic


PhD students

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R&D engineers in signal processing and wireless and mobile communications


Table of Contents

  • Introduction

    Signal Processing at Your Fingertips!

    About the Editors

    Section Editors

    Section 1

    Section 2

    Section 3

    Section 4

    Sections 5, 6 and 7

    Authors Biography

    Chapter 2

    Chapter 3

    Chapter 4

    Chapter 5

    Chapter 6

    Chapter 7

    Chapter 8

    Chapter 10

    Chapter 11

    Chapter 12

    Chapter 14

    Chapter 15

    Chapter 16

    Chapter 17

    Chapter 18

    Chapter 19

    Chapter 20

    Chapter 21

    Chapter 23

    Chapter 24

    Chapter 25

    Chapter 27

    Chapter 28

    Chapter 30

    Chapter 32

    Chapter 34

    Chapter 35

    Section 1: Image Enhancement/Restoration And Digital Imaging

    Chapter 1. Digital Imaging: Capture, Display, Restoration, and Enhancement


    4.01.1 Introduction

    4.01.2 Image capture

    4.01.3 Image displays

    4.01.4 Restoration

    Chapter 2. Image Quality in Consumer Digital Cameras



    4.02.1 Introduction

    4.02.2 Digital camera image processing chain

    4.02.3 Camera engineering

    4.02.4 Quality modeling

    4.02.5 System interactions

    4.02.6 Processing methods and algorithms

    4.02.7 Applications

    4.02.8 Open issues and future directions

    4.02.9 Implementations

    4.02.10 Datasets



    Chapter 3. Image and Document Capture—State-of-the-Art and a Glance into the Future

    4.03.1 Introduction

    4.03.2 Basic steps of conventional document capture processing

    4.03.3 Document and image capture applications that are still challenging today

    4.03.4 Looking into the future of document and image capture

    4.03.5 Data capture via novel sensor multiplexing techniques

    4.03.6 Data sets and open source code

    4.03.7 Conclusions and future trends


    Chapter 4. Image Display—Mobile Imaging and Interactive Image Processing


    4.04.1 The small screen challenge in mobile imaging

    4.04.2 Subpixel-based hardware design in mobile display

    4.04.3 Subpixel-based software design in mobile display: font rendering

    4.04.4 Subpixel-based software design in mobile display: color image down-sampling

    4.04.5 Conclusion


    Chapter 5. Image Display—Printing (Desktop, Commercial)


    4.05.1 Introduction

    4.05.2 Printing technologies

    4.05.3 Workflow

    4.05.4 Printer models

    4.05.5 Research directions in printing



    Chapter 6. Image Restoration: Fundamentals of Image Restoration


    4.06.1 Introduction

    4.06.2 Observation model

    4.06.3 Restoration algorithms

    4.06.4 Boundary effects

    4.06.5 Blur identification

    4.06.6 Conclusion



    Chapter 7. Iterative Methods for Image Restoration



    4.07.1 Introduction

    4.07.2 Background

    4.07.3 Model problems

    4.07.4 Iterative methods for unconstrained problems

    4.07.5 Iterative methods with nonnegativity constraints

    4.07.6 Examples

    4.07.7 Concluding remarks and open questions


    Chapter 8. Image Processing at Your Fingertips: The New Horizon of Mobile Imaging


    4.08.1 Historical background and overview

    4.08.2 Mobile imaging: following Feynman’s idea on infinitesimal machinery

    4.08.3 Mobile computing: interacting with computer without an interface

    4.08.4 Image processing at fingertips: where mobile imaging meets mobile computing

    4.08.5 Applications

    4.08.6 Open issues and problems

    A. Appendix: course material, source codes and datasets

    Supplementary data

    Supplementary data


    Section 2: Image Analysis And Recognition

    Chapter 9. Image Analysis and Recognition

    4.09.1 General background

    4.09.2 Chapter introductions


    Chapter 10. Multi-Path Marginal Space Learning for Object Detection


    4.10.1 Introduction

    4.10.2 Related work

    4.10.3 Marginal Space Learning overview

    4.10.4 Face detection with Marginal Space Learning

    4.10.5 Multiple computational paths in Marginal Space Learning

    4.10.6 Experimental validation

    4.10.7 Applications

    4.10.8 Open issues and problems

    4.10.9 Datasets

    4.10.10 Conclusions and future trends



    Chapter 11. Markov Models and MCMC Algorithms in Image Processing


    4.11.1 Introduction: the probabilistic approach in image analysis

    4.11.2 Lattice based models and the Bayesian paradigm

    4.11.3 Some inverse problems

    4.11.4 Spatial point processes

    4.11.5 Multiple objects detection

    4.11.6 Conclusion


    Chapter 12. Identifying Multivariate Imaging Patterns: Supervised, Semi-Supervised, and Unsupervised Learning Perspectives



    4.12.1 Introduction

    4.12.2 Materials

    4.12.3 Supervised learning of predictive models

    4.12.4 Semi-supervised learning of predictive models

    4.12.5 Unsupervised learning as the means to disentangle heterogeneity

    4.12.6 Summary


    Section 3: Video Processing

    Chapter 13. Video Processing—An Overview

    4.13.1 Basic tasks in video analysis

    4.13.2 Applications in video analysis

    4.13.3 Overview of chapters

    Chapter 14. Foveated Image and Video Processing and Search



    4.14.1 Introduction

    4.14.2 The human visual system

    4.14.3 Modeling the human visual system

    4.14.4 Foveated images and video

    4.14.5 Fixation selection

    4.14.6 Applications

    4.14.7 Open issues and problems

    4.14.8 Implementation/code

    4.14.9 Data sets

    4.14.10 Conclusions and future trends



    Chapter 15. Segmentation-Free Biometric Recognition Using Correlation Filters


    4.15.1 Introduction

    4.15.2 Advanced correlation filters

    4.15.3 Pre- and post-processing images

    4.15.4 Correlation filters for videos

    4.15.5 Experiments: recognizing subjects in video only using ocular regions

    4.15.6 Conclusion

    A Appendix


    Chapter 16. Dynamical Systems in Video Analysis


    4.16.1 Introduction

    4.16.2 Model

    4.16.3 Identification

    4.16.4 Comparing dynamical models

    4.16.5 Applications

    4.16.6 Datasets

    4.16.7 Discussion


    Chapter 17. Image-Based Rendering


    4.17.1 Introduction

    4.17.2 Integral imaging

    4.17.3 Sampling

    4.17.4 Scene representation

    4.17.5 Rendering

    4.17.6 Applications

    4.17.7 Open issues and problems

    4.17.8 Implementation/code

    4.17.9 Data sets

    4.17.10 Conclusions and future trends



    Activity Retrieval in Large Surveillance Videos


    4.18.1 Introduction

    4.18.2 Feature extraction

    4.18.3 Indexing

    4.18.4 Search engine

    4.18.5 Experimental results

    4.18.6 Conclusion


    Chapter 19. Multi-Target Tracking in Video


    4.19.1 Introduction

    4.19.2 Problem formulation

    4.19.3 Challenges

    4.19.4 Feature extraction

    4.19.5 Prediction

    4.19.6 Localization and association

    4.19.7 Track initialization and termination

    4.19.8 Scene contextual information

    4.19.9 Summary and outlook


    Chapter 20. Compressive Sensing for Video Applications



    4.20.1 Introduction

    4.20.2 Imaging architectures

    4.20.3 Signal models and algorithms

    4.20.4 Existing systems for video compressive sensing

    4.20.5 Discussion


    Chapter 21. Virtual Vision for Camera Networks Research



    4.21.1 Introduction

    4.21.2 Related work

    4.21.3 Virtual vision simulators

    4.21.4 Prototype camera networks

    4.21.5 Conclusion



    Section 4: Hardware And Software

    Chapter 22. Introduction: Hardware and Software

    4.22.1 Hardware and software systems

    4.22.2 New developments: 3D integration

    Chapter 23. Distributed Smart Cameras for Distributed Computer Vision



    4.23.1 Introduction

    4.23.2 Basic techniques in computer vision

    4.23.3 Camera calibration

    4.23.4 Gesture recognition

    4.23.5 Tracking with overlapping fields-of-view

    4.23.6 Tracking in sparse camera networks

    4.23.7 Summary


    Chapter 24. Mapping Parameterized Dataflow Graphs onto FPGA Platforms


    4.24.1 Introduction

    4.24.2 Background

    4.24.3 Dynamic reconfiguration techniques in FPGAs

    4.24.4 Modeling dynamic reconfiguration using PSDF techniques

    4.24.5 Hardware mapping

    4.24.6 Case studies

    4.24.7 Conclusion


    Distributed Estimation


    4.25.1 Notation

    4.25.2 Network with a star topology

    4.25.3 Non-ideal networks with star topology

    4.25.4 Network with arbitrary topology

    4.25.5 Computational complexity and communication cost

    4.25.6 Conclusion



    Section 5: Audio Signal Processing

    Chapter 26. Introduction to Audio Signal Processing

    4.26.1 Background

    4.26.2 Overview of the chapters

    Chapter 27. Music Signal Processing


    4.27.1 Introduction

    4.27.2 Pitch and harmony

    4.27.3 Tempo and beat

    4.27.4 Timbre and instrumentation

    4.27.5 Melody and vocals


    Chapter 28. Perceptual Audio Coding



    4.28.1 Principles and background

    4.28.2 Concepts and architectures

    4.28.3 Standards

    4.28.4 Summary and conclusions


    Section 6: Acoustic Signal Processing

    Chapter 29. Introduction to Acoustic Signal Processing

    4.29.1 Background

    4.29.2 Overview of the chapters

    Chapter 30. Acoustic Echo Control



    List of Abbreviations

    4.30.1 Introduction

    4.30.2 Echo cancellation and postfiltering

    4.30.3 Echo suppression

    4.30.4 Multichannel acoustic echo cancellation

    4.30.5 Nonlinear modeling and cancellation of echo

    4.30.6 Application to realistic and real systems

    4.30.7 Links to codes and recommendations

    4.30.8 Conclusions, open issues, future trends



    Chapter 31. Dereverberation



    4.31.1 Introduction and overview

    4.31.2 Example applications

    4.31.3 Room reverberation

    4.31.4 Measurement of reverberation

    4.31.5 Spatial filtering for dereverberation

    4.31.6 Speech enhancement methods for dereverberation

    4.31.7 Acoustic channel-based methods for dereverberation

    4.31.8 Summary and conclusions

    List of Abbreviations


    Chapter 32. Sound Field Synthesis



    4.32.1 Introduction

    4.32.2 Acoustic wave equation

    4.32.3 Signal representations

    4.32.4 Response to sound sources

    4.32.5 Physical foundations of sound field synthesis

    4.32.6 Near-field Compensated Higher Order Ambisonics (NFC-HOA)

    4.32.7 Spectral division method (SDM)

    4.32.8 Wave Field Synthesis (WFS)

    4.32.9 Supplementary data

    4.32.10 Supplementary data


    Section 7: Speech Processing

    Chapter 33. Introduction to Speech Processing

    4.33.1 Background

    4.33.2 Overview of the chapters

    Chapter 34. Speech Production Modeling and Analysis


    4.34.1 Introduction

    4.34.2 Speech production modeling

    4.34.3 Estimating the voice source signal

    4.34.4 Glottal closure instants

    4.34.5 Voice source modeling


    Chapter 35. Enhancement


    4.35.1 Introduction

    4.35.2 Speech enhancement methods

    4.35.3 Enabling algorithms

    4.35.4 Intelligibility and quality measures

    List of Abbreviations



Product details

  • No. of pages: 1088
  • Language: English
  • Copyright: © Academic Press 2013
  • Published: September 5, 2013
  • Imprint: Academic Press
  • eBook ISBN: 9780123972255
  • Hardcover ISBN: 9780123965011

About the Editors in Chief

Sergios Theodoridis

Sergios Theodoridis
Sergios Theodoridis acquired a Physics degree with honors from the University of Athens, Greece in 1973 and a MSc and a Ph.D. degree in Signal Processing and Communications from the University of Birmingham, UK in 1975 and 1978 respectively. Since 1995 he has been a Professor with the Department of Informatics and Communications at the University of Athens.

Affiliations and Expertise

Department of Informatics and Telecommunications, University of Athens, Greece

Rama Chellappa

Prof. Rama Chellappa received the B.E. (Hons.) degree from the University of Madras, India, in 1975 and the M.E. (Distinction) degree from Indian Institute of Science, Bangalore, in 1977. He received M.S.E.E. and Ph.D. Degrees in Electrical Engineering from Purdue University, West Lafayette, IN, in 1978 and 1981 respectively. Since 1991, he has been a Professor of Electrical Engineering and an affiliate Professor of Computer Science at University of Maryland, College Park. He is also affiliated with the Center for Automation Research (Director) and the Institute for Advanced Computer Studies (Permanent Member). In 2005, he was named a Minta Martin Professor of Engineering. Prior to joining the University of Maryland, he was an Assistant (1981-1986) and Associate Professor (1986-1991) and Director of the Signal and Image Processing Institute (1988-1990) at University of Southern California, Los Angeles. Over the last 29 years, he has published numerous book chapters, peer-reviewed journal and conference papers. He has co-authored and edited books on MRFs, face and gait recognition and collected works on image processing and analysis. His current research interests are face and gait analysis, markerless motion capture, 3D modeling from video, image and video-based recognition and exploitation and hyper spectral processing.

Affiliations and Expertise

University of Maryland, College Park, USA

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