Multimodal Scene Understanding: Algorithms, Applications and Deep Learning presents recent advances in multi-modal computing, with a focus on computer vision and photogrammetry. It provides the latest algorithms and applications that involve combining multiple sources of information and describes the role and approaches of multi-sensory data and multi-modal deep learning. The book is ideal for researchers from the fields of computer vision, remote sensing, robotics, and photogrammetry, thus helping foster interdisciplinary interaction and collaboration between these realms.
Researchers collecting and analyzing multi-sensory data collections – for example, KITTI benchmark (stereo+laser) - from different platforms, such as autonomous vehicles, surveillance cameras, UAVs, planes and satellites will find this book to be very useful.
1. Chapter 1: Introduction to Multimodal Scene Understanding - p. ix 2. Chapter 2: Deep Learning for Multimodal Data Fusion - p. 7 3. Chapter 3: Multimodal Semantic Segmentation: Fusion of RGB and Depth Data in Convolutional Neural Networks - p. 39 4. Chapter 4: Learning Convolutional Neural Networks for Object Detection with Very Little Training Data - p. 64 5. Chapter 5: Multimodal Fusion Architectures for Pedestrian Detection - p. 100 6. Chapter 6: Multispectral Person Re-Identification Using GAN for Color-to-Thermal Image Translation - p. 133 7. Chapter 7: A Review and Quantitative Evaluation of Direct Visual–Inertial Odometry - p. 158 8. Chapter 8: Multimodal Localization for Embedded Systems: A Survey - p. 198 9. Chapter 9: Self-Supervised Learning from Web Data for Multimodal Retrieval - p. 278 10. Chapter 10: 3D Urban Scene Reconstruction and Interpretation from Multisensor Imagery - p. 306 11. Chapter 11: Decision Fusion of Remote-Sensing Data for Land Cover Classification - p. 340 12. Chapter 12: Cross-modal Learning by Hallucinating Missing Modalities in RGB-D Vision - p. 382 13. Index - p. 403
Multimodal Scene Understanding: Algorithms, Applications and Deep Learning presents recent advances in multi-modal computing, with a focus on computer vision and photogrammetry. It provides the latest algorithms and applications that involve combining multiple sources of information and describes the role and approaches of multi-sensory data and multi-modal deep learning. The book is ideal for researchers from the fields of computer vision, remote sensing, robotics, and photogrammetry, thus helping foster interdisciplinary interaction and collaboration between these realms.
Researchers collecting and analyzing multi-sensory data collections – for example, KITTI benchmark (stereo+laser) - from different platforms, such as autonomous vehicles, surveillance cameras, UAVs, planes and satellites will find this book to be very useful.
1. Chapter 1: Introduction to Multimodal Scene Understanding - p. ix 2. Chapter 2: Deep Learning for Multimodal Data Fusion - p. 7 3. Chapter 3: Multimodal Semantic Segmentation: Fusion of RGB and Depth Data in Convolutional Neural Networks - p. 39 4. Chapter 4: Learning Convolutional Neural Networks for Object Detection with Very Little Training Data - p. 64 5. Chapter 5: Multimodal Fusion Architectures for Pedestrian Detection - p. 100 6. Chapter 6: Multispectral Person Re-Identification Using GAN for Color-to-Thermal Image Translation - p. 133 7. Chapter 7: A Review and Quantitative Evaluation of Direct Visual–Inertial Odometry - p. 158 8. Chapter 8: Multimodal Localization for Embedded Systems: A Survey - p. 198 9. Chapter 9: Self-Supervised Learning from Web Data for Multimodal Retrieval - p. 278 10. Chapter 10: 3D Urban Scene Reconstruction and Interpretation from Multisensor Imagery - p. 306 11. Chapter 11: Decision Fusion of Remote-Sensing Data for Land Cover Classification - p. 340 12. Chapter 12: Cross-modal Learning by Hallucinating Missing Modalities in RGB-D Vision - p. 382 13. Index - p. 403
