Computer Vision in Vehicle Technology : From Earth to Mars.

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Imiya, Atsushi
Otros Autores: Pajdla, Tomas, Alvarez, Jose M., López, Antonio M.
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Somerset : John Wiley & Sons, Incorporated, 2017.
Temas:
Computer vision.
Automotive telematics.
Automated vehicles > Equipment and supplies.
Drone aircraft > Equipment and supplies.
Nautical instruments.
Vision par ordinateur.
Télématique automobile.
Instruments nautiques.
Automotive telematics
Computer vision
Nautical instruments
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Cover; Title Page; Copyright; Contents; List of Contributors; Preface; Abbreviations and Acronyms; Chapter 1 Computer Vision in Vehicles; 1.1 Adaptive Computer Vision for Vehicles; 1.1.1 Applications; 1.1.2 Traffic Safety and Comfort; 1.1.3 Strengths of (Computer) Vision; 1.1.4 Generic and Specific Tasks; 1.1.5 Multi-module Solutions; 1.1.6 Accuracy, Precision, and Robustness; 1.1.7 Comparative Performance Evaluation; 1.1.8 There Are Many Winners; 1.2 Notation and Basic Definitions; 1.2.1 Images and Videos; 1.2.2 Cameras; 1.2.3 Optimization; 1.3 Visual Tasks; 1.3.1 Distance; 1.3.2 Motion.
  • 1.3.3 Object Detection and Tracking1.3.4 Semantic Segmentation; 1.4 Concluding Remarks; Acknowledgments; Chapter 2 Autonomous Driving; 2.1 Introduction; 2.1.1 The Dream; 2.1.2 Applications; 2.1.3 Level of Automation; 2.1.4 Important Research Projects; 2.1.5 Outdoor Vision Challenges; 2.2 Autonomous Driving in Cities; 2.2.1 Localization; 2.2.2 Stereo Vision-Based Perception in 3D; 2.2.3 Object Recognition; 2.3 Challenges; 2.3.1 Increasing Robustness; 2.3.2 Scene Labeling; 2.3.3 Intention Recognition; 2.4 Summary; Acknowledgments; Chapter 3 Computer Vision for MAVs; 3.1 Introduction.
  • 3.2 System and Sensors3.3 Ego-Motion Estimation; 3.3.1 State Estimation Using Inertial and Vision Measurements; 3.3.2 MAV Pose from Monocular Vision; 3.3.3 MAV Pose from Stereo Vision; 3.3.4 MAV Pose from Optical Flow Measurements; 3.4 3D Mapping; 3.5 Autonomous Navigation; 3.6 Scene Interpretation; 3.7 Concluding Remarks; Chapter 4 Exploring the Seafloor with Underwater Robots; 4.1 Introduction; 4.2 Challenges of Underwater Imaging; 4.3 Online Computer Vision Techniques; 4.3.1 Dehazing; 4.3.2 Visual Odometry; 4.3.3 SLAM; 4.3.4 Laser Scanning; 4.4 Acoustic Imaging Techniques.
  • 4.4.1 Image Formation4.4.2 Online Techniques for Acoustic Processing; 4.5 Concluding Remarks; Acknowledgments; Chapter 5 Vision-Based Advanced Driver Assistance Systems; 5.1 Introduction; 5.2 Forward Assistance; 5.2.1 Adaptive Cruise Control (ACC) and Forward Collision Avoidance (FCA); 5.2.2 Traffic Sign Recognition (TSR); 5.2.3 Traffic Jam Assist (TJA); 5.2.4 Vulnerable Road User Protection; 5.2.5 Intelligent Headlamp Control; 5.2.6 Enhanced Night Vision (Dynamic Light Spot); 5.2.7 Intelligent Active Suspension; 5.3 Lateral Assistance.
  • 5.3.1 Lane Departure Warning (LDW) and Lane Keeping System (LKS)5.3.2 Lane Change Assistance (LCA); 5.3.3 Parking Assistance; 5.4 Inside Assistance; 5.4.1 Driver Monitoring and Drowsiness Detection; 5.5 Conclusions and Future Challenges; 5.5.1 Robustness; 5.5.2 Cost; Acknowledgments; Chapter 6 Application Challenges from a Bird's-Eye View; 6.1 Introduction to Micro Aerial Vehicles (MAVs); 6.1.1 Micro Aerial Vehicles (MAVs); 6.1.2 Rotorcraft MAVs; 6.2 GPS-Denied Navigation; 6.2.1 Autonomous Navigation with Range Sensors; 6.2.2 Autonomous Navigation with Vision Sensors.

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