Computer vision in vehicle technology : land, sea & air

Computer vision in vehicle technology land, sea & air

Detalles Bibliográficos
Otros Autores: López, Antonio M., 1969- author (author), López, Antonio M., 1969- editor (editor), Imiya, Atsushi, author, Pajdla, Tomáš, author, Alvarez, J. M. (José M.), author
Formato: Libro electrónico
Idioma:Inglés
Publicado: Chichester, West Sussex, United Kingdom : John Wiley & Sons, Incorporated 2017.
Edición:1st ed
Materias:
Computer vision.
Automotive telematics.
Autonomous vehicles > Equipment and supplies.
Drone aircraft > Equipment and supplies.
Nautical instruments.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009849100506719
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 Tracking
  • 1.3.4 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 Sensors
  • 3.3 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 Formation
  • 4.4.2 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
  • 6.2.3 SFLY: Swarm of Micro Flying Robots
  • 6.2.4 SVO, a Visual-Odometry Algorithm for MAVs
  • 6.3 Applications and Challenges
  • 6.3.1 Applications
  • 6.3.2 Safety and Robustness
  • 6.4 Conclusions
  • Chapter 7 Application Challenges of Underwater Vision
  • 7.1 Introduction
  • 7.2 Offline Computer Vision Techniques for Underwater Mapping and Inspection
  • 7.2.1 2D Mosaicing
  • 7.2.2 2.5D Mapping
  • 7.2.3 3D Mapping
  • 7.2.4 Machine Learning for Seafloor Classification
  • 7.3 Acoustic Mapping Techniques
  • 7.4 Concluding Remarks
  • Chapter 8 Closing Notes
  • References
  • Index
  • EULA.

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