{"product_id":"multimodal-scene-understanding-algorithms-applications-and-deep-learning-paperback-9780128173589","title":"Multimodal Scene Understanding; Algorithms, Applications and Deep Learning (Paperback) 9780128173589","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eMultimodal Scene Understanding\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eAlgorithms, Applications and Deep Learning\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cem\u003e\u003cp\u003eA unique presentation of multi-sensory data and multi-modal deep learning\u003c\/p\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eMichael Ying Yang (Edited by), Bodo Rosenhahn (Edited by), Vittorio Murino (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780128173589, Elsevier Science\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003ePaperback, published 17 July 2019\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e422 pages\u003cbr\u003e23.4 x 19 x 2.7 cm, 0.86 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003ci\u003eMultimodal Scene Understanding: Algorithms, Applications and Deep Learning \u003c\/i\u003epresents 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. \u003c\/p\u003e  \u003cp\u003eResearchers 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. \u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e1. Introduction to Multimodal Scene Understanding \u003ci\u003eMichael Ying Yang, Bodo Rosenhahn and Vittorio Murino \u003c\/i\u003e2. Multi-modal Deep Learning for Multi-sensory Data Fusion \u003ci\u003eAsako Kanezaki, Ryohei Kuga, Yusuke Sugano and Yasuyuki Matsushita \u003c\/i\u003e3. Multi-Modal Semantic Segmentation: Fusion of RGB and Depth Data in Convolutional Neural Networks \u003ci\u003eZoltan Koppanyi, Dorota Iwaszczuk, Bing Zha, Can Jozef Saul, Charles K. Toth and Alper Yilmaz \u003c\/i\u003e4. Learning Convolutional Neural Networks for Object Detection with very little Training Data \u003ci\u003eChristoph Reinders, Hanno Ackermann, Michael Ying Yang and Bodo Rosenhahn \u003c\/i\u003e5. Multi-modal Fusion Architectures for Pedestrian Detection \u003ci\u003eDayan Guan, Jiangxin Yang, Yanlong Cao, Michael Ying Yang and Yanpeng Cao \u003c\/i\u003e6. ThermalGAN: Multimodal Color-to-Thermal Image Translation for Person Re-Identification in Multispectral Dataset \u003ci\u003eVladimir A. Knyaz and Vladimir V. Kniaz \u003c\/i\u003e7. A Review and Quantitative Evaluation of Direct Visual-Inertia Odometry \u003ci\u003eLukas von Stumberg, Vladyslav Usenko and Daniel Cremers \u003c\/i\u003e8. Multimodal Localization for Embedded Systems: A Survey \u003ci\u003eImane Salhi, Martyna Poreba, Erwan Piriou, Valerie Gouet-Brunet and Maroun Ojail \u003c\/i\u003e9. Self-Supervised Learning from Web Data for Multimodal Retrieval \u003ci\u003eRaul Gomez, Lluis Gomez, Jaume Gibert and Dimosthenis Karatzas \u003c\/i\u003e10. 3D Urban Scene Reconstruction and Interpretation from Multi-sensor Imagery \u003ci\u003eHai Huang, Andreas Kuhn, Mario Michelini, Matthais Schmitz and Helmut Mayer \u003c\/i\u003e11. Decision Fusion of Remote Sensing Data for Land Cover Classification \u003ci\u003eArnaud Le Bris, Nesrine Chehata, Walid Ouerghemmi, Cyril Wendl, Clement Mallet, Tristan Postadjian and Anne Puissant \u003c\/i\u003e12. Cross-modal learning by hallucinating missing modalities in RGB-D vision \u003ci\u003eNuno Garcia, Pietro Morerio and Vittorio Murino\u003c\/i\u003e\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Image processing [\u003ca title=\"See our other books on Image processing\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Image%20processing%20%5BUYT%5D%22\"\u003eUYT\u003c\/a\u003e], Signal processing [\u003ca title=\"See our other books on Signal processing\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Signal%20processing%20%5BUYS%5D%22\"\u003eUYS\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Academic Press","offers":[{"title":"Default Title","offer_id":46650092290328,"sku":"9780128173589","price":96.99,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/products\/9780128173589.jpg?v=1694105805","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/multimodal-scene-understanding-algorithms-applications-and-deep-learning-paperback-9780128173589","provider":"Freshly Printed Books","version":"1.0","type":"link"}