Multimodal scene understanding : algorithms, applications and deep learning / edited by Michael Ying Yang, Bodo Rosenhahn, Vittorio Murino.
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 th...
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| Language: | English |
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London ; San Diego, CA :
Academic Press,
[2019]
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| Subjects: |
MARC
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| 245 | 0 | 0 | |a Multimodal scene understanding : |b algorithms, applications and deep learning / |c edited by Michael Ying Yang, Bodo Rosenhahn, Vittorio Murino. |
| 264 | 1 | |a London ; |a San Diego, CA : |b Academic Press, |c [2019] | |
| 300 | |a 1 online resource (ix, 412 pages) : |b illustrations (some color), maps | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a volume |b nc |2 rdacarrier | ||
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |a Front Cover; Multimodal Scene Understanding; Copyright; Contents; List of Contributors; 1 Introduction to Multimodal Scene Understanding; 1.1 Introduction; 1.2 Organization of the Book; References; 2 Deep Learning for Multimodal Data Fusion; 2.1 Introduction; 2.2 Related Work; 2.3 Basics of Multimodal Deep Learning: VAEs and GANs; 2.3.1 Auto-Encoder; 2.3.2 Variational Auto-Encoder (VAE); 2.3.3 Generative Adversarial Network (GAN); 2.3.4 VAE-GAN; 2.3.5 Adversarial Auto-Encoder (AAE); 2.3.6 Adversarial Variational Bayes (AVB); 2.3.7 ALI and BiGAN | |
| 505 | 8 | |a 2.4 Multimodal Image-to-Image Translation Networks2.4.1 Pix2pix and Pix2pixHD; 2.4.2 CycleGAN, DiscoGAN, and DualGAN; 2.4.3 CoGAN; 2.4.4 UNIT; 2.4.5 Triangle GAN; 2.5 Multimodal Encoder-Decoder Networks; 2.5.1 Model Architecture; 2.5.2 Multitask Training; 2.5.3 Implementation Details; 2.6 Experiments; 2.6.1 Results on NYUDv2 Dataset; 2.6.2 Results on Cityscape Dataset; 2.6.3 Auxiliary Tasks; 2.7 Conclusion; References; 3 Multimodal Semantic Segmentation: Fusion of RGB and Depth Data in Convolutional Neural Networks; 3.1 Introduction; 3.2 Overview; 3.2.1 Image Classi cation and the VGG Network | |
| 505 | 8 | |a 3.2.2 Architectures for Pixel-level Labeling3.2.3 Architectures for RGB and Depth Fusion; 3.2.4 Datasets and Benchmarks; 3.3 Methods; 3.3.1 Datasets and Data Splitting; 3.3.2 Preprocessing of the Stanford Dataset; 3.3.3 Preprocessing of the ISPRS Dataset; 3.3.4 One-channel Normal Label Representation; 3.3.5 Color Spaces for RGB and Depth Fusion; 3.3.6 Hyper-parameters and Training; 3.4 Results and Discussion; 3.4.1 Results and Discussion on the Stanford Dataset; 3.4.2 Results and Discussion on the ISPRS Dataset; 3.5 Conclusion; References | |
| 505 | 8 | |a 4 Learning Convolutional Neural Networks for Object Detection with Very Little Training Data4.1 Introduction; 4.2 Fundamentals; 4.2.1 Types of Learning; 4.2.2 Convolutional Neural Networks; 4.2.2.1 Arti cial neuron; 4.2.2.2 Arti cial neural network; 4.2.2.3 Training; 4.2.2.4 Convolutional neural networks; 4.2.3 Random Forests; 4.2.3.1 Decision tree; 4.2.3.2 Random forest; 4.3 Related Work; 4.4 Traf c Sign Detection; 4.4.1 Feature Learning; 4.4.2 Random Forest Classi cation; 4.4.3 RF to NN Mapping; 4.4.4 Fully Convolutional Network; 4.4.5 Bounding Box Prediction; 4.5 Localization | |
| 505 | 8 | |a 4.6 Clustering4.7 Dataset; 4.7.1 Data Capturing; 4.7.2 Filtering; 4.8 Experiments; 4.8.1 Training and Test Data; 4.8.2 Classi cation; 4.8.3 Object Detection; 4.8.4 Computation Time; 4.8.5 Precision of Localizations; 4.9 Conclusion; Acknowledgment; References; 5 Multimodal Fusion Architectures for Pedestrian Detection; 5.1 Introduction; 5.2 Related Work; 5.2.1 Visible Pedestrian Detection; 5.2.2 Infrared Pedestrian Detection; 5.2.3 Multimodal Pedestrian Detection; 5.3 Proposed Method; 5.3.1 Multimodal Feature Learning/Fusion; 5.3.2 Multimodal Pedestrian Detection; 5.3.2.1 Baseline DNN model | |
| 520 | |a 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. | ||
| 588 | 0 | |a Online resource; title from digital title page (viewed on October 10, 2019). | |
| 650 | 0 | |a Computational intelligence. | |
| 650 | 0 | |a Computer vision. | |
| 650 | 0 | |a Algorithms. | |
| 650 | 0 | |a Engineering. | |
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 0 | |a Computer algorithms. | |
| 650 | 7 | |a Computer algorithms |2 fast | |
| 650 | 7 | |a Computer vision |2 fast | |
| 650 | 7 | |a Algorithms |2 fast | |
| 650 | 7 | |a Artificial intelligence |2 fast | |
| 650 | 7 | |a Computational intelligence |2 fast | |
| 650 | 7 | |a Engineering |2 fast | |
| 700 | 1 | |a Yang, Michael Ying, |e editor. | |
| 700 | 1 | |a Rosenhahn, Bodo, |e editor. |1 https://id.oclc.org/worldcat/entity/E39PCjxvBfRgQyKMbbDBD89GXm | |
| 700 | 1 | |a Murino, Vittorio, |e editor. |1 https://id.oclc.org/worldcat/entity/E39PCjrdXW8yKCrqqjpKXGtCpK | |
| 776 | 0 | 8 | |i Ebook version : |z 9780128173596 |
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