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Multimodal Scene Understanding

2019-07-16 Technology & Engineering
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Release : 2019-07-16
Genre : Technology & Engineering
Kind : eBook
Book Rating : 599/5 ( reviews)

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Book Synopsis Multimodal Scene Understanding by : Michael Ying Yang

Download or read book Multimodal Scene Understanding written by Michael Ying Yang. This book was released on 2019-07-16. Available in PDF, EPUB and Kindle. Book excerpt: 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. - Contains state-of-the-art developments on multi-modal computing - Shines a focus on algorithms and applications - Presents novel deep learning topics on multi-sensor fusion and multi-modal deep learning

Multimodal Computational Attention for Scene Understanding

2014
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Release : 2014
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Kind : eBook
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Book Synopsis Multimodal Computational Attention for Scene Understanding by : Boris Schauerte

Download or read book Multimodal Computational Attention for Scene Understanding written by Boris Schauerte. This book was released on 2014. Available in PDF, EPUB and Kindle. Book excerpt:

Multimodal Computational Attention for Scene Understanding and Robotics

2016-05-11 Technology & Engineering
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Release : 2016-05-11
Genre : Technology & Engineering
Kind : eBook
Book Rating : 963/5 ( reviews)

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Book Synopsis Multimodal Computational Attention for Scene Understanding and Robotics by : Boris Schauerte

Download or read book Multimodal Computational Attention for Scene Understanding and Robotics written by Boris Schauerte. This book was released on 2016-05-11. Available in PDF, EPUB and Kindle. Book excerpt: This book presents state-of-the-art computational attention models that have been successfully tested in diverse application areas and can build the foundation for artificial systems to efficiently explore, analyze, and understand natural scenes. It gives a comprehensive overview of the most recent computational attention models for processing visual and acoustic input. It covers the biological background of visual and auditory attention, as well as bottom-up and top-down attentional mechanisms and discusses various applications. In the first part new approaches for bottom-up visual and acoustic saliency models are presented and applied to the task of audio-visual scene exploration of a robot. In the second part the influence of top-down cues for attention modeling is investigated.

Real-time Multimodal Semantic Scene Understanding for Autonomous UGV Navigation

2021
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Release : 2021
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Book Synopsis Real-time Multimodal Semantic Scene Understanding for Autonomous UGV Navigation by : Yifei Zhang

Download or read book Real-time Multimodal Semantic Scene Understanding for Autonomous UGV Navigation written by Yifei Zhang. This book was released on 2021. Available in PDF, EPUB and Kindle. Book excerpt: Robust semantic scene understanding is challenging due to complex object types, as well as environmental changes caused by varying illumination and weather conditions. This thesis studies the problem of deep semantic segmentation with multimodal image inputs. Multimodal images captured from various sensory modalities provide complementary information for complete scene understanding. We provided effective solutions for fully-supervised multimodal image segmentation and few-shot semantic segmentation of the outdoor road scene. Regarding the former case, we proposed a multi-level fusion network to integrate RGB and polarimetric images. A central fusion framework was also introduced to adaptively learn the joint representations of modality-specific features and reduce model uncertainty via statistical post-processing.In the case of semi-supervised semantic scene understanding, we first proposed a novel few-shot segmentation method based on the prototypical network, which employs multiscale feature enhancement and the attention mechanism. Then we extended the RGB-centric algorithms to take advantage of supplementary depth cues. Comprehensive empirical evaluations on different benchmark datasets demonstrate that all the proposed algorithms achieve superior performance in terms of accuracy as well as demonstrating the effectiveness of complementary modalities for outdoor scene understanding for autonomous navigation.

Real-time Multimodal Semantic Scene Understanding for Autonomous UGV Navigation

2021
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Release : 2021
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Kind : eBook
Book Rating : /5 ( reviews)

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Book Synopsis Real-time Multimodal Semantic Scene Understanding for Autonomous UGV Navigation by : Yifei Zhang

Download or read book Real-time Multimodal Semantic Scene Understanding for Autonomous UGV Navigation written by Yifei Zhang. This book was released on 2021. Available in PDF, EPUB and Kindle. Book excerpt: Robust semantic scene understanding is challenging due to complex object types, as well as environmental changes caused by varying illumination and weather conditions. This thesis studies the problem of deep semantic segmentation with multimodal image inputs. Multimodal images captured from various sensory modalities provide complementary information for complete scene understanding. We provided effective solutions for fully-supervised multimodal image segmentation and few-shot semantic segmentation of the outdoor road scene. Regarding the former case, we proposed a multi-level fusion network to integrate RGB and polarimetric images. A central fusion framework was also introduced to adaptively learn the joint representations of modality-specific features and reduce model uncertainty via statistical post-processing.In the case of semi-supervised semantic scene understanding, we first proposed a novel few-shot segmentation method based on the prototypical network, which employs multiscale feature enhancement and the attention mechanism. Then we extended the RGB-centric algorithms to take advantage of supplementary depth cues. Comprehensive empirical evaluations on different benchmark datasets demonstrate that all the proposed algorithms achieve superior performance in terms of accuracy as well as demonstrating the effectiveness of complementary modalities for outdoor scene understanding for autonomous navigation.

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