Computer Vision in Vehicle Technology : Land, Sea, and Air.

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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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.