Description: Multimodal Scene Understanding Algorithms, Applications and Deep Learning A unique presentation of multi-sensory data and multi-modal deep learning Michael Ying Yang (Edited by), Bodo Rosenhahn (Edited by), Vittorio Murino (Edited by) 9780128173589, Elsevier Science Paperback / softback, published 17 July 2019 422 pages 23.5 x 19 x 2.7 cm, 0.86 kg 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. Introduction to Multimodal Scene Understanding Michael Ying Yang, Bodo Rosenhahn and Vittorio Murino 2. Multi-modal Deep Learning for Multi-sensory Data Fusion Asako Kanezaki, Ryohei Kuga, Yusuke Sugano and Yasuyuki Matsushita 3. Multi-Modal Semantic Segmentation: Fusion of RGB and Depth Data in Convolutional Neural Networks Zoltan Koppanyi, Dorota Iwaszczuk, Bing Zha, Can Jozef Saul, Charles K. Toth and Alper Yilmaz 4. Learning Convolutional Neural Networks for Object Detection with very little Training Data Christoph Reinders, Hanno Ackermann, Michael Ying Yang and Bodo Rosenhahn 5. Multi-modal Fusion Architectures for Pedestrian Detection Dayan Guan, Jiangxin Yang, Yanlong Cao, Michael Ying Yang and Yanpeng Cao 6. ThermalGAN: Multimodal Color-to-Thermal Image Translation for Person Re-Identification in Multispectral Dataset Vladimir A. Knyaz and Vladimir V. Kniaz 7. A Review and Quantitative Evaluation of Direct Visual-Inertia Odometry Lukas von Stumberg, Vladyslav Usenko and Daniel Cremers 8. Multimodal Localization for Embedded Systems: A Survey Imane Salhi, Martyna Poreba, Erwan Piriou, Valerie Gouet-Brunet and Maroun Ojail 9. Self-Supervised Learning from Web Data for Multimodal Retrieval Raul Gomez, Lluis Gomez, Jaume Gibert and Dimosthenis Karatzas 10. 3D Urban Scene Reconstruction and Interpretation from Multi-sensor Imagery Hai Huang, Andreas Kuhn, Mario Michelini, Matthais Schmitz and Helmut Mayer 11. Decision Fusion of Remote Sensing Data for Land Cover Classification Arnaud Le Bris, Nesrine Chehata, Walid Ouerghemmi, Cyril Wendl, Clement Mallet, Tristan Postadjian and Anne Puissant 12. Cross-modal learning by hallucinating missing modalities in RGB-D vision Nuno Garcia, Pietro Morerio and Vittorio Murino Subject Areas: Image processing [UYT], Signal processing [UYS]
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BIC Subject Area 1: Image processing [UYT]
BIC Subject Area 2: Signal processing [UYS]
Item Height: 235 mm
Item Width: 191 mm
Author: Michael Yang, Bodo Rosenhahn, Vittorio Murino
Publication Name: Multimodal Scene Understanding: Algorithms, Applications and Deep Learning
Format: Paperback
Language: English
Publisher: Elsevier Science Publishing Co INC International Concepts
Subject: Computer Science
Publication Year: 2019
Type: Textbook
Item Weight: 860 g
Number of Pages: 422 Pages
