Advanced Man-Machine Interaction

Transcription

1 Signals and Communication Technology Advanced Man-Machine Interaction Fundamentals and Implementation Bearbeitet von Karl-Friedrich Kraiss 1. Auflage Buch. XIX, 461 S. ISBN Format (B x L): 15,5 x 23,5 cm Gewicht: 882 g Weitere Fachgebiete > Technik > Technik Allgemein > Konstruktionslehre und -technik Zu Leseprobe schnell und portofrei erhältlich bei Die Online-Fachbuchhandlung beck-shop.de ist spezialisiert auf Fachbücher, insbesondere Recht, Steuern und Wirtschaft. Im Sortiment finden Sie alle Medien (Bücher, Zeitschriften, CDs, ebooks, etc.) aller Verlage. Ergänzt wird das Programm durch Services wie Neuerscheinungsdienst oder Zusammenstellungen von Büchern zu Sonderpreisen. Der Shop führt mehr als 8 Millionen Produkte.

2 Table of Contents 1 Introduction Non-Intrusive Acquisition of Human Action Hand Gesture Commands Image Acquisition and Input Data Vocabulary Recording Conditions Image Representation Example Feature Extraction Hand Localization Region Description Geometric Features Example Feature Classifi cation Classifi cation Concepts Classifi cation Algorithms Feature Selection Rule-based Classifi cation Maximum Likelihood Classifi cation Classifi cation Using Hidden Markov Models Example Static Gesture Recognition Application Dynamic Gesture Recognition Application Troubleshooting Facial Expression Commands Image Acquisition Image Preprocessing Face Localization Face Tracking Feature Extraction with Active Appearance Models Appearance Model AAM Search Feature Classifi cation Head Pose Estimation Determination of Line of Sight Circle Hough Transformation Determination of Lip Outline Lip modeling Facial Feature Recognition Eye Localization Application Facial Feature Recognition Mouth Localization Application. 90

3 X Table of Contents References Sign Language Recognition Recognition of Isolated Signs in Real-World Scenarios Image Acquisition and Input Data Image Preprocessing Background Modeling Feature Extraction Overlap Resolution Hand Tracking Feature Normalization Feature Classifi cation Test and Training Samples Sign Recognition Using Nonmanual Features Nonmanual Parameters Extraction of Nonmanual Features Recognition of continuous Sign Language using Subunits Subunit Models for Signs Transcription of Sign Language Linguistics-orientated Transcription of Sign Language Visually-orientated Transcription of Sign Language Sequential and Parallel Breakdown of Signs Modifi cation of HMMs to Parallel Hidden Markov Models Modeling Sign Language by means of PaHMMs Classifi cation Classifi cation of Single Signs by Means of Subunits and PaHMMs Classifi cation of Continuous Sign Language by Means of Subunits and PaHMMs Stochastic Language Modeling Training Initial Transcription Estimation of Model Parameters for Subunits Classifi cation of Single Signs Concatenation of Subunit Models to Word Models for Signs Enlargement of Vocabulary Size by New Signs Performance Evaluation Video-based Isolated Sign Recognition Subunit Based Recognition of Signs and Continuous Sign Language Discussion References

4 Table of Contents XI 4 Speech Communication and Multimodal Interfaces Speech Recognition Fundamentals of Hidden Markov Model-based Speech Recognition Training of Speech Recognition Systems Recognition Phase for HMM-based ASR Systems Information Theory Interpretation of Automatic Speech Recognition Summary of the Automatic Speech Recognition Procedure Speech Recognition Technology Applications of ASR Systems Speech Dialogs Introduction Initiative Strategies Models of Dialog Finite State Model Slot Filling Stochastic Model Goal Directed Processing Rational Conversational Agents Dialog Design Scripting and Tagging Multimodal Interaction In- and Output Channels Basics of Multimodal Interaction Advantages Taxonomy Multimodal Fusion Integration Methods Errors in Multimodal Systems Error Classifi cation User Specifi c Errors System Specifi c Errors Error Avoidance Error Resolution Emotions from Speech and Facial Expressions Background Application Scenarios Modalities Emotion Model Emotional Databases Acoustic Information Feature Extraction Feature Selection Classifi cation Methods

5 XII Table of Contents Linguistic Information N-Grams Bag-of-Words Phrase Spotting Visual Information Prerequisites Holistic Approaches Analytic Approaches Information Fusion Discussion References Person Recognition and Tracking Face Recognition Challenges in Automatic Face Recognition Structure of Face Recognition Systems Categorization of Face Recognition Algorithms Global Face Recognition using Eigenfaces Local Face Recognition based on Face Components or Template Matching Face Databases for Development and Evaluation Exercises Provided Images and Image Sequences Face Recognition Using the Eigenface Approach Face Recognition Using Face Components Full-body Person Recognition State of the Art in Full-body Person Recognition Color Features for Person Recognition Color Histograms Color Structure Descriptor Texture Features for Person Recognition Oriented Gaussian Derivatives Homogeneous Texture Descriptor Experimental Results Experimental Setup Feature Performance Camera-based People Tracking Segmentation Foreground Segmentation by Background Subtraction Morphological Operations Tracking Tracking Following Detection Combined Tracking and Detection Occlusion Handling Occlusion Handling without Separation

6 Table of Contents XIII Separation by Shape Separation by Color Separation by 3D Information Localization and Tracking on the Ground Plane Example References Interacting in Virtual Reality Visual Interfaces Spatial Vision Immersive Visual Displays Viewer Centered Projection Acoustic Interfaces Spatial Hearing Auditory Displays Wave Field Synthesis Binaural Synthesis Cross Talk Cancellation Haptic Interfaces Rendering a Wall Rendering Solid Objects Modeling the Behavior of Virtual Objects Particle Dynamics Rigid Body Dynamics Interacting with Rigid Objects Guiding Sleeves Sensitive Polygons Virtual Magnetism Snap-In Implementing Virtual Environments Scene Graphs Toolkits Cluster Rendering in Virtual Environments Examples A Simple Scene Graph Example More Complex Examples: SolarSystem and Robot An Interactive Environment, Tetris3D Summary Acknowledgements References

7 XIV Table of Contents 7 Interactive and Cooperative Robot Assistants Mobile and Humanoid Robots Interaction with Robot Assistants Classifi cation of Human-Robot Interaction Activities Performative Actions Commanding and Commenting Actions Learning and Teaching Robot Assistants Classifi cation of Learning through Demonstration Skill Learning Task Learning Interactive Learning Programming Robots via Observation: An Overview Interactive Task Learning from Human Demonstration Classifi cation of Task Learning The PbD Process System Structure Sensors for Learning through Demonstration Models and Representation of Manipulation Tasks Defi nitions Task Classes of Manipulations Hierarchical Representation of Tasks Subgoal Extraction from Human Demonstration Signal Processing Segmentation Step Grasp Classifi cation Static Grasp Classifi cation Dynamic Grasp Classifi cation Task Mapping and Execution Event-Driven Architecture Mapping Tasks onto Robot Manipulation Systems Human Comments and Advice Examples of Interactive Programming Telepresence and Telerobotics The Concept of Telepresence and Telerobotic Systems Components of a Telerobotic System Telemanipulation for Robot Assistants in Human-Centered Environments Exemplary Teleoperation Applications Using Teleoperated Robots as Remote Sensor Systems Controlling and Instructing Robot Assistants via Teleoperation References

8 Table of Contents XV 8 Assisted Man-Machine Interaction The Concept of User Assistance Assistance in Manual Control Needs for Assistance in Manual Control Context of Use in Manual Control Support of Manual Control Tasks Assistance in Man-Machine Dialogs Needs for Assistance in Dialogs Context of Dialogs Support of Dialog Tasks Summary References A LTI-LIB A C++ Open Source Computer Vision Library A.1 Installation and Requirements A.2 Overview A.2.1 Duplication and Replication A.2.2 Serialization A.2.3 Encapsulation A.2.4 Examples A.3 Data Structures A.3.1 Points and Pixels A.3.2 Vectors and Matrices A.3.3 Image Types A.4 Functional Objects A.4.1 Functor A.4.2 Classifi er A.5 Handling Images A.5.1 Convolution A.5.2 Image IO A.5.3 Color Spaces A.5.4 Drawing and Viewing A.6 Where to Go Next References B IMPRESARIO A GUI for Rapid Prototyping of Image Processing Systems B.1 Requirements, Installation, and Deinstallation B.2 Basic Components and Operations B.2.1 Document and Processing Graph B.2.2 Macros B.2.3 Processing Mode B.2.4 Viewer B.2.5 Summary and further Reading B.3 Adding Functionality to a Document

9 XVI Table of Contents B.3.1 Using the Macro Browser B.3.2 Rearranging and Deleting Macros B.4 Defi ning the Data Flow with Links B.4.1 Creating a Link B.4.2 Removing a Link B.5 Confi guring Macros B.5.1 Image Sequence B.5.2 Video Capture Device B.5.3 Video Stream B.5.4 Standard Macro Property Window B.6 Advanced Features and Internals of IMPRESARIO B.6.1 Customizing the Graphical User Interface B.6.2 Macros and Dynamic Link Libraries B.6.3 Settings Dialog and Directories B.7 IMPRESARIO Examples in this Book B.8 Extending IMPRESARIO with New Macros B.8.1 Requirements B.8.2 API Installation and further Reading B.8.3 IMPRESARIO Updates References Index