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Interactive Displays: Natural Human-Interface Technologies [Kõva köide]

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(Intel Corporation)
  • Formaat: Hardback, 408 pages, kõrgus x laius x paksus: 250x175x27 mm, kaal: 803 g
  • Sari: Wiley Series in Display Technology
  • Ilmumisaeg: 26-Sep-2014
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 1118631374
  • ISBN-13: 9781118631379
Teised raamatud teemal:
Interactive Displays: Natural Human-Interface TechnologiesSuurem pilt
  • Formaat: Hardback, 408 pages, kõrgus x laius x paksus: 250x175x27 mm, kaal: 803 g
  • Sari: Wiley Series in Display Technology
  • Ilmumisaeg: 26-Sep-2014
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 1118631374
  • ISBN-13: 9781118631379
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781118631379.html
Märksõnad:

How we interface and interact with computing, communications and entertainment devices is going through revolutionary changes, with natural user inputs based on touch, voice, and vision replacing or augmenting the use of traditional interfaces based on the keyboard, mouse, joysticks, etc. As a result, displays are morphing from one-way interface devices that merely show visual content to two-way interaction devices that provide more engaging and immersive experiences. This book provides an in-depth coverage of the technologies, applications, and trends in the rapidly emerging field of interactive displays enabled by natural human-interfaces.

Key features:

  • Provides a definitive reference reading on all the touch technologies used in interactive displays, including their advantages, limitations, and future trends.
  • Covers the fundamentals and applications of speech input, processing and recognition techniques enabling voice-based interactions.
  • Offers a detailed review of the emerging vision-based sensing technologies, and user interactions using gestures of hands, body, face, and eye gazes.
  • Discusses multi-modal natural user interface schemes which intuitively combine touch, voice, and vision for life-like interactions.
  • Examines the requirements and technology status towards realizing “true” 3D immersive and interactive displays.
About the Author xiii
List of Contributors
xv
Series Editor's Foreword xvii
Preface xix
List of Acronyms
xxi
1 Senses, Perception, and Natural Human-Interfaces for Interactive Displays
1(26)
Achintya K. Bhowmik
1.1 Introduction
1(3)
1.2 Human Senses and Perception
4(5)
1.3 Human Interface Technologies
9(11)
1.3.1 Legacy Input Devices
9(2)
1.3.2 Touch-based Interactions
11(2)
1.3.3 Voice-based Interactions
13(2)
1.3.4 Vision-based Interactions
15(3)
1.3.5 Multimodal Interactions
18(2)
1.4 Towards "True" 3D Interactive Displays
20(3)
1.5 Summary
23(4)
References
24(3)
2 Touch Sensing
27(80)
Geoff Walker
2.1 Introduction
27(1)
2.2 Introduction to Touch Technologies
28(7)
2.2.1 Touchscreens
30(1)
2.2.2 Classifying Touch Technologies by Size and Application
30(2)
2.2.3 Classifying Touch Technologies by Materials and Structure
32(1)
2.2.4 Classifying Touch Technologies by the Physical Quantity Being Measured
33(1)
2.2.5 Classifying Touch Technologies by Their Sensing Capabilities
33(1)
2.2.6 The Future of Touch Technologies
34(1)
2.3 History of Touch Technologies
35(1)
2.4 Capacitive Touch Technologies
35(16)
2.4.1 Projected Capacitive (P-Cap)
35(12)
2.4.2 Surface Capacitive
47(4)
2.5 Resistive Touch Technologies
51(10)
2.5.1 Analog Resistive
51(6)
2.5.2 Digital Multi-touch Resistive (DMR)
57(2)
2.5.3 Analog Multi-touch Resistive (AMR)
59(2)
2.6 Acoustic Touch Technologies
61(7)
2.6.1 Surface Acoustic Wave (SAW)
61(3)
2.6.2 Acoustic Pulse Recognition (APR)
64(3)
2.6.3 Dispersive Signal Technology (DST)
67(1)
2.7 Optical Touch Technologies
68(18)
2.7.1 Traditional Infrared
68(5)
2.7.2 Multi-touch Infrared
73(3)
2.7.3 Camera-based Optical
76(5)
2.7.4 In-glass Optical (Planar Scatter Detection -- PSD)
81(1)
2.7.5 Vision-based Optical
82(4)
2.8 Embedded Touch Technologies
86(10)
2.8.1 On-cell Mutual-capacitive
89(1)
2.5.2 Hybrid In-cell/On-cell Mutual-capacitive
90(1)
2.8.3 In-cell Mutual-capacitive
91(2)
2.8.4 in-cell Light Sensing
93(3)
2.9 Other Touch Technologies
96(2)
2.9.1 Force-sensing
96(2)
2.9.2 Combinations of Touch Technologies
98(1)
2.10 Summary
98(2)
2.11 Appendix
100(7)
References
101(6)
3 Voice in the User Interface
107(58)
Andrew Breen
Hung H. Bui
Richard Crouch
Kevin Farrell
Friedrich Faubel
Roberto Gemello
William F. Ganong III
Tim Haulick
Ronald M. Kaplan
Charles L. Ortiz
Peter F. Patel-Schneider
Holger Quast
Adwait Ratnaparkhi
Vlad Sejnoha
Jiaying Shen
Peter Stubley
Paul van Mulbregt
3.1 Introduction
107(3)
3.2 Voice Recognition
110(9)
3.2.1 Nature of Speech
110(2)
3.2.2 Acoustic Model and Front-end
112(1)
3.2.3 Aligning Speech to HMMs
113(1)
3.2.4 Language Model
114(1)
3.2.5 Search: Solving Crosswords at 1000 Words a Second
115(1)
3.2.6 Training Acoustic and Language Models
116(1)
3.2.7 Adapting Acoustic and Language Models for Speaker Dependent Recognition
116(1)
3.2.8 Alternatives to the "Canonical" System
117(1)
3.2.9 Performance
117(2)
3.3 Deep Neural Networks for Voice Recognition
119(3)
3.4 Hardware Optimization
122(1)
3.4.1 Lower Power Wake-up Computation
122(1)
3.4.2 Hardware Optimization for Specific Computations
123(1)
3.5 Signal Enhancement Techniques for Robust Voice Recognition
123(5)
3.5.1 Robust Voice Recognition
124(1)
3.5.2 Single-channel Noise Suppression
124(1)
3.5.3 Multi-channel Noise Suppression
125(1)
3.5.4 Noise Cancellation
125(2)
3.5.5 Acoustic Echo Cancellation
127(1)
3.5.6 Beamforming
127(1)
3.6 Voice Biometrics
128(2)
3.6.1 Introduction
128(1)
3.6.2 Existing Challenges to Voice Biometrics
129(1)
3.6.3 New Areas of Research in Voice Biometrics
130(1)
3.7 Speech Synthesis
130(4)
3.8 Natural Language Understanding
134(7)
3.8.1 Mixed Initiative Conversations
135(2)
3.8.2 Limitations of Slot and Filler Technology
137(4)
3.9 Multi-turn Dialog Management
141(3)
3.10 Planning and Reasoning
144(7)
3.10.1 Technical Challenges
144(2)
3.10.2 Semantic Analysis and Discourse Representation
146(1)
3.10.3 Pragmatics
147(1)
3.10.4 Dialog Management as Collaboration
148(1)
3.10.5 Planning and Re-planning
149(1)
3.10.6 Knowledge Representation and Reasoning
149(1)
3.10.7 Monitoring
150(1)
3.10.8 Suggested Readings
151(1)
3.11 Question Answering
151(3)
3.11.1 Question Analysis
152(1)
3.11.2 Find Relevant Information
152(1)
3.11.3 Answers and Evidence
153(1)
3.11.4 Presenting the Answer
153(1)
3.12 Distributed Voice Interface Architecture
154(3)
3.12.1 Distributed User Interfaces
154(1)
3.12.2 Distributed Speech and Language Technology
155(2)
3.13 Conclusion
157(8)
Acknowledgements
158(1)
References
158(7)
4 Visual Sensing and Gesture Interactions
165(16)
Achintya K. Bhowmik
4.1 Introduction
165(2)
4.2 Imaging Technologies: 2D and 3D
167(3)
4.3 Interacting with Gestures
170(7)
4.4 Summary
177(4)
References
178(3)
5 Real-Time 3D Sensing With Structured Light Techniques
181(34)
Tyler Bell
Nikolaus Karpinsky
Song Zhang
5.1 Introduction
181(2)
5.2 Structured Pattern Codifications
183(8)
5.2.1 2D Pseudo-random Codifications
183(1)
5.2.2 Binary Structured Codifications
184(3)
5.2.3 N-ary Codifications
187(1)
5.2.4 Continuous Sinusoidal Phase Codifications
187(4)
5.3 Structured Light System Calibration
191(2)
5.4 Examples of 3D Sensing with DFP Techniques
193(2)
5.5 Real-Time 3D Sensing Techniques
195(6)
5.5.1 Fundamentals of Digital-light-processing (DLP) Technology
196(2)
5.5.2 Real-Time 3D Data Acquisition
198(1)
5.5.3 Real-Time 3D Data Processing and Visualization
199(1)
5.5.4 Example of Real-Time 3D Sensing
200(1)
5.6 Real-Time 3D Sensing for Human Computer Interaction Applications
201(3)
5.6.1 Real-Time 3D Facial Expression Capture and its HCI Implications
201(1)
5.6.2 Real-Time 3D Body Part Gesture Capture and its HCI Implications
202(2)
5.6.3 Concluding Human Computer Interaction Implications
204(1)
5.7 Some Recent Advancements
204(4)
5.7.1 Real-Time 3D Sensing and Natural 2D Color Texture Capture
204(2)
5.7.2 Superfast 3D Sensing
206(2)
5.8 Summary
208(7)
Acknowledgements
209(1)
References
209(6)
6 Real-Time Stereo 3D Imaging Techniques
215(18)
Lazaros Nalpantidis
6.1 Introduction
215(1)
6.2 Background
216(3)
6.3 Structure of Stereo Correspondence Algorithms
219(3)
6.3.1 Matching Cost Computation
220(1)
6.3.2 Matching Cost Aggregation
221(1)
6.4 Categorization of Characteristics
222(3)
6.4.1 Depth Estimation Density
222(2)
6.4.2 Optimization Strategy
224(1)
6.5 Categorization of Implementation Platform
225(4)
6.5.1 CPU-only Methods
225(1)
6.5.2 GPU-accelerated Methods
226(1)
6.5.3 Hardware Implementations (FPGAs, ASICs)
227(2)
6.6 Conclusion
229(4)
References
229(4)
7 Time-of-Flight 3D-Imaging Techniques
233(18)
Daniel Van Nieuwenhove
7.1 Introduction
233(1)
7.2 Time-of-Flight 3D Sensing
233(2)
7.3 Pulsed Time-of-Flight Method
235(1)
7.4 Continuous Time-of-Flight Method
236(1)
7.5 Calculations
236(3)
7.6 Accuracy
239(1)
7.7 Limitations and Improvements
240(4)
7.7.1 TOF Challenges
240(1)
7.7.2 Theoretical Limits
241(1)
7.7.3 Distance Aliasing
242(1)
7.7.4 Multi-path and Scattering
243(1)
7.7.5 Power Budget and Optimization
243(1)
7.8 Time-of-Flight Camera Components
244(1)
7.9 Typical Values
244(3)
7.9.1 Light Power Range
244(1)
7.9.2 Background Light
245(2)
7.10 Current State of the Art
247(1)
7.11 Conclusion
247(4)
References
248(3)
8 Eye Gaze Tracking
251(34)
Heiko Drewes
8.1 Introduction and Motivation
251(2)
8.2 The Eyes
253(3)
8.3 Eye Trackers
256(4)
8.3.1 Types of Eye Trackers
256(1)
8.3.2 Corneal Reflection Method
257(3)
8.4 Objections and Obstacles
260(3)
8.4.1 Human Aspects
260(1)
8.4.2 Outdoor Use
261(1)
8.4.3 Calibration
261(1)
8.4.4 Accuracy
261(1)
8.4.5 Midas Touch Problem
262(1)
8.5 Eye Gaze Interaction Research
263(1)
8.6 Gaze Pointing
264(6)
8.6.1 Solving the Midas Touch Problem
264(1)
8.6.2 Solving the Accuracy Issue
265(1)
8.6.3 Comparison of Mouse and Gaze Pointing
266(1)
8.6.4 Mouse and Gaze Coordination
267(2)
8.6.5 Gaze Pointing Feedback
269(1)
8.7 Gaze Gestures
270(5)
8.7.1 The Concept of Gaze Gestures
270(1)
8.7.2 Gesture Detection Algorithm
270(1)
8.7.3 Human Ability to Perform Gaze Gestures
271(1)
8.7.4 Gaze Gesture Alphabets
272(1)
8.7.5 Gesture Separation from Natural Eye Movement
273(1)
8.7.6 Applications for Gaze Gestures
274(1)
8.8 Gaze as Context
275(5)
8.8.1 Activity Recognition
275(2)
8.8.2 Reading Detection
277(2)
8.8.3 Attention Detection
279(1)
8.8.4 Using Gaze Context
280(1)
8.9 Outlook
280(5)
References
281(4)
9 Multimodal Input for Perceptual User Interfaces
285(28)
Joseph J. LaViola Jr.
Sarah Buchanan
Corey Pittman
9.1 Introduction
285(1)
9.2 Multimodal Interaction Types
286(1)
9.3 Multimodal Interfaces
287(16)
9.3.1 Touch Input
287(7)
9.3.2 3D Gesture
294(5)
9.3.3 Eye Tracking and Gaze
299(1)
9.3.4 Facial Expressions
300(1)
9.3.5 Brain-computer Input
301(2)
9.4 Multimodal Integration Strategies
303(2)
9.4.1 Frame-based Integration
304(1)
9.4.2 Unification-based Integration
304(1)
9.4.3 Procedural Integration
305(1)
9.4.4 Symbolic/Statistical Integration
305(1)
9.5 Usability Issues with Multimodal Interaction
305(2)
9.6 Conclusion
307(6)
References
308(5)
10 Multimodal Interaction in Biometrics: Technological and Usability Challenges
313(30)
Norman Poh
Phillip A. Tresadern
Rita Wong
10.1 Introduction
313(7)
10.1.1 Motivations for Identity Assurance
314(1)
10.1.2 Biometrics
314(1)
10.1.3 Application Characteristics of Multimodal Biometrics
314(2)
10.1.4 2D and 3D Face Recognition
316(1)
10.1.5 A Multimodal Case Study
317(1)
10.1.6 Adaptation to Blind Subjects
318(2)
10.1.7
Chapter Organization
320(1)
10.2 Anatomy of the Mobile Biometry Platform
320(8)
10.2.1 Face Analysis
320(3)
10.2.2 Voice Analysis
323(2)
10.2.3 Model Adaptation
325(1)
10.2.4 Data Fusion
326(1)
10.2.5 Mobile Platform Implementation
326(1)
10.2.6 MoBio Database and Protocol
327(1)
10.3 Case Study: Usability Study for the Visually Impaired
328(10)
10.3.1 Impact of Head Pose Variations on Performance
329(1)
10.3.2 User Interaction Module: Head Pose Quality Assessment
329(4)
10.3.3 User-Interaction Module: Audio Feedback Mechanism
333(3)
10.3.4 Usability Testing with the Visually Impaired
336(2)
10.4 Discussions and Conclusions
338(5)
Acknowledgements
339(1)
References
339(4)
11 Towards "True" 3D Interactive Displays
343(32)
Jim Larimer
Philip J. Bos
Achintya K. Bhowmik
11.1 Introduction
343(3)
11.2 The Origins of Biological Vision
346(6)
11.3 Light Field Imaging
352(7)
11.4 Towards "True" 3D Visual Displays
359(9)
11.5 Interacting with Visual Content on a 3D Display
368(3)
11.6 Summary
371(4)
References
371(4)
Index 375
Achintya K. Bhowmik, Intel Corporation, USA Dr. Achin Bhowmik is the director of perceptual computing technology and solutions at Intel Corporation, where his group is focused on developing next-generation computing solutions based on natural human-computer interaction and visual computing technologies and applications. He is a senior member of the IEEE as well as program committee member of SID and IMID. He is associate editor of the Journal of the Society for Information Display, and was guest editor for two special volumes on "Advances in OLED Displays" and "Interactive Displays". Dr. Bhowmik is an Adjunct Professor at Kyung-Hee University, Seoul, Korea teaching courses on digital imaging & display, digital image processing and optics of liquid crystal displays. He is on the board of directors for OpenCV, the organization behind the open source computer vision library.