book

Academic Press Library in Signal Processing

by Sergios Theodoridis

September 2013

Intermediate to advanced

1088 pages

39h 9m

English

Academic Press

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Signal Processing at Your Fingertips!
Section 1Section 2Section 3Section 4Sections 5, 6 and 7
Chapter 2Chapter 3Chapter 4Chapter 5Chapter 6Chapter 7Chapter 8Chapter 10Chapter 11Chapter 12Chapter 14Chapter 15Chapter 16Chapter 17Chapter 18Chapter 19Chapter 20Chapter 21Chapter 23Chapter 24Chapter 25Chapter 27Chapter 28Chapter 30Chapter 32Chapter 34Chapter 35
Abstract4.01.1 Introduction4.01.2 Image capture4.01.3 Image displays4.01.4 Restoration

AbstractNomenclature4.02.1 Introduction4.02.2 Digital camera image processing chain4.02.3 Camera engineering4.02.4 Quality modeling4.02.5 System interactions4.02.6 Processing methods and algorithms4.02.7 Applications4.02.8 Open issues and future directions4.02.9 Implementations4.02.10 DatasetsGlossaryReferences
4.03.1 Introduction4.03.2 Basic steps of conventional document capture processing4.03.3 Document and image capture applications that are still challenging today4.03.4 Looking into the future of document and image capture4.03.5 Data capture via novel sensor multiplexing techniques4.03.6 Data sets and open source code4.03.7 Conclusions and future trendsReferences
Abstract4.04.1 The small screen challenge in mobile imaging4.04.2 Subpixel-based hardware design in mobile display4.04.3 Subpixel-based software design in mobile display: font rendering4.04.4 Subpixel-based software design in mobile display: color image down-sampling4.04.5 ConclusionReferences
Abstract4.05.1 Introduction4.05.2 Printing technologies4.05.3 Workflow4.05.4 Printer models4.05.5 Research directions in printingGlossaryReferences
Abstract4.06.1 Introduction4.06.2 Observation model4.06.3 Restoration algorithms4.06.4 Boundary effects4.06.5 Blur identification4.06.6 ConclusionGlossaryReferences
AbstractAcknowledgments4.07.1 Introduction4.07.2 Background4.07.3 Model problems4.07.4 Iterative methods for unconstrained problems4.07.5 Iterative methods with nonnegativity constraints4.07.6 Examples4.07.7 Concluding remarks and open questionsReferences
Abstract4.08.1 Historical background and overview4.08.2 Mobile imaging: following Feynman’s idea on infinitesimal machinery4.08.3 Mobile computing: interacting with computer without an interface4.08.4 Image processing at fingertips: where mobile imaging meets mobile computing4.08.5 Applications4.08.6 Open issues and problemsA. Appendix: course material, source codes and datasetsSupplementary dataSupplementary dataReferences
4.09.1 General background4.09.2 Chapter introductionsReferences
Abstract4.10.1 Introduction4.10.2 Related work4.10.3 Marginal Space Learning overview4.10.4 Face detection with Marginal Space Learning4.10.5 Multiple computational paths in Marginal Space Learning4.10.6 Experimental validation4.10.7 Applications4.10.8 Open issues and problems4.10.9 Datasets4.10.10 Conclusions and future trendsGlossaryReferences
Abstract4.11.1 Introduction: the probabilistic approach in image analysis4.11.2 Lattice based models and the Bayesian paradigm4.11.3 Some inverse problems4.11.4 Spatial point processes4.11.5 Multiple objects detection4.11.6 ConclusionReferences
AbstractAcknowledgment4.12.1 Introduction4.12.2 Materials4.12.3 Supervised learning of predictive models4.12.4 Semi-supervised learning of predictive models4.12.5 Unsupervised learning as the means to disentangle heterogeneity4.12.6 SummaryReferences
4.13.1 Basic tasks in video analysis4.13.2 Applications in video analysis4.13.3 Overview of chapters
AbstractNomenclature4.14.1 Introduction4.14.2 The human visual system4.14.3 Modeling the human visual system4.14.4 Foveated images and video4.14.5 Fixation selection4.14.6 Applications4.14.7 Open issues and problems4.14.8 Implementation/code4.14.9 Data sets4.14.10 Conclusions and future trendsGlossaryReferences
Abstract4.15.1 Introduction4.15.2 Advanced correlation filters4.15.3 Pre- and post-processing images4.15.4 Correlation filters for videos4.15.5 Experiments: recognizing subjects in video only using ocular regions4.15.6 ConclusionA AppendixReferences
Abstract4.16.1 Introduction4.16.2 Model4.16.3 Identification4.16.4 Comparing dynamical models4.16.5 Applications4.16.6 Datasets4.16.7 DiscussionReferences
Abstract4.17.1 Introduction4.17.2 Integral imaging4.17.3 Sampling4.17.4 Scene representation4.17.5 Rendering4.17.6 Applications4.17.7 Open issues and problems4.17.8 Implementation/code4.17.9 Data sets4.17.10 Conclusions and future trendsGlossaryReferences
Abstract4.18.1 Introduction4.18.2 Feature extraction4.18.3 Indexing4.18.4 Search engine4.18.5 Experimental results4.18.6 ConclusionReferences
Abstract4.19.1 Introduction4.19.2 Problem formulation4.19.3 Challenges4.19.4 Feature extraction4.19.5 Prediction4.19.6 Localization and association4.19.7 Track initialization and termination4.19.8 Scene contextual information4.19.9 Summary and outlookReferences
AbstractAcknowledgments4.20.1 Introduction4.20.2 Imaging architectures4.20.3 Signal models and algorithms4.20.4 Existing systems for video compressive sensing4.20.5 DiscussionReferences
AbstractAcknowledgments4.21.1 Introduction4.21.2 Related work4.21.3 Virtual vision simulators4.21.4 Prototype camera networks4.21.5 ConclusionGlossaryReferences
4.22.1 Hardware and software systems4.22.2 New developments: 3D integration
AbstractAcknowledgment4.23.1 Introduction4.23.2 Basic techniques in computer vision4.23.3 Camera calibration4.23.4 Gesture recognition4.23.5 Tracking with overlapping fields-of-view4.23.6 Tracking in sparse camera networks4.23.7 SummaryReferences
Abstract4.24.1 Introduction4.24.2 Background4.24.3 Dynamic reconfiguration techniques in FPGAs4.24.4 Modeling dynamic reconfiguration using PSDF techniques4.24.5 Hardware mapping4.24.6 Case studies4.24.7 ConclusionReferences
Abstract4.25.1 Notation4.25.2 Network with a star topology4.25.3 Non-ideal networks with star topology4.25.4 Network with arbitrary topology4.25.5 Computational complexity and communication cost4.25.6 ConclusionAppendixReferences
4.26.1 Background4.26.2 Overview of the chapters
Abstract4.27.1 Introduction4.27.2 Pitch and harmony4.27.3 Tempo and beat4.27.4 Timbre and instrumentation4.27.5 Melody and vocalsReferences
AbstractIntroduction4.28.1 Principles and background4.28.2 Concepts and architectures4.28.3 Standards4.28.4 Summary and conclusionsReferences
4.29.1 Background4.29.2 Overview of the chapters
AbstractNomenclatureList of Abbreviations4.30.1 Introduction4.30.2 Echo cancellation and postfiltering4.30.3 Echo suppression4.30.4 Multichannel acoustic echo cancellation4.30.5 Nonlinear modeling and cancellation of echo4.30.6 Application to realistic and real systems4.30.7 Links to codes and recommendations4.30.8 Conclusions, open issues, future trendsGlossaryReferences
AbstractAcknowledgments4.31.1 Introduction and overview4.31.2 Example applications4.31.3 Room reverberation4.31.4 Measurement of reverberation4.31.5 Spatial filtering for dereverberation4.31.6 Speech enhancement methods for dereverberation4.31.7 Acoustic channel-based methods for dereverberation4.31.8 Summary and conclusionsList of AbbreviationsReferences
AbstractAcknowledgments4.32.1 Introduction4.32.2 Acoustic wave equation4.32.3 Signal representations4.32.4 Response to sound sources4.32.5 Physical foundations of sound field synthesis4.32.6 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 data4.32.10 Supplementary dataReferences
4.33.1 Background4.33.2 Overview of the chapters
Abstract4.34.1 Introduction4.34.2 Speech production modeling4.34.3 Estimating the voice source signal4.34.4 Glottal closure instants4.34.5 Voice source modelingReferences
Abstract4.35.1 Introduction4.35.2 Speech enhancement methods4.35.3 Enabling algorithms4.35.4 Intelligibility and quality measuresList of AbbreviationsReferences