Discover the captivating world of computer vision and deep learning for autonomous driving with our comprehensive and in-depth guide. Immerse yourself in an in-depth exploration of cutting-edge topics, carefully crafted to engage tertiary students and ignite the curiosity of researchers and professionals in the field. From fundamental principles to practical applications, this comprehensive guide offers a gentle introduction, expert evaluations of state-of-the-art methods, and inspiring research directions. With a broad range of topics covered, it is also an invaluable resource for university programs offering computer vision and deep learning courses. This book provides clear and simplified algorithm descriptions, making it easy for beginners to understand the complex concepts. We also include carefully selected problems and examples to help reinforce your learning. Don't miss out on this essential guide to computer vision and deep learning for autonomous driving.
Author(s): Rui Fan; Sicen Guo; Mohammud Junaid Bocus
Publisher: Springer Nature Singapore
Year: 2023
Language: English
Pages: 686
Cover
Front Matter
1. In-Sensor Visual Devices for Perception and Inference
2. Environmental Perception Using Fish-Eye Cameras for Autonomous Driving
3. Stereo Matching: Fundamentals, State-of-the-Art, and Existing Challenges
4. Semantic Segmentation for Autonomous Driving
5. 3D Object Detection in Autonomous Driving
6. Collaborative 3D Object Detection
7. Enabling Robust SLAM for Mobile Robots with Sensor Fusion
8. Visual SLAM for Texture-Less Environment
9. Multi-task Perception for Autonomous Driving
10. Bird’s Eye View Perception for Autonomous Driving
11. Road Environment Perception for Safe and Comfortable Driving
