Multimodal Scene Understanding

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  • Publisher : Academic Press
  • Release : 16 July 2019
  • ISBN : 9780128173596
  • Page : 422 pages
  • Rating : 4.5/5 from 103 voters

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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

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Multimodal Scene Understanding

Multimodal Scene Understanding
  • Author : Michael Yang,Bodo Rosenhahn,Vittorio Murino
  • Publisher : Academic Press
  • Release Date : 2019-07-16
  • ISBN : 9780128173596
GET THIS BOOKMultimodal Scene Understanding

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

Multimodal Machine Learning

Multimodal Machine Learning
  • Author : Santosh Kumar,Sanjay Kumar Singh
  • Publisher : Academic Press
  • Release Date : 2021-05-15
  • ISBN : 0128237376
GET THIS BOOKMultimodal Machine Learning

Multimodal Machine Learning: Techniques and Applications explains recent advances in multimodal machine learning, providing a coherent set of fundamentals for designing efficient multimodal learning algorithms for different applications. The book addresses the main challenges in multimodal machine learning based computing paradigms, including multimodal representation learning, translation and mapping, modality alignment, multimodal fusion and co-learning. The book also explores the important texture feature descriptors based on recognition and transform techniques. It is ideal for senior undergraduates, graduate students, and researchers in

Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support

Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support
  • Author : Danail Stoyanov,Zeike Taylor,Gustavo Carneiro,Tanveer Syeda-Mahmood,Anne Martel,Lena Maier-Hein,João Manuel R.S. Tavares,Andrew Bradley,João Paulo Papa,Vasileios Belagiannis,Jacinto C. Nascimento,Zhi Lu,Sailesh Conjeti,Mehdi Moradi,Hayit Greenspan,Anant Madabhushi
  • Publisher : Springer
  • Release Date : 2018-09-19
  • ISBN : 9783030008895
GET THIS BOOKDeep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support

This book constitutes the refereed joint proceedings of the 4th International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2018, and the 8th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2018, held in conjunction with the 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018, in Granada, Spain, in September 2018. The 39 full papers presented at DLMIA 2018 and the 4 full papers presented at ML-CDS 2018 were carefully reviewed and selected from 85 submissions to DLMIA and 6 submissions to ML-CDS. The

Deep Neural Networks for Multimodal Imaging and Biomedical Applications

Deep Neural Networks for Multimodal Imaging and Biomedical Applications
  • Author : Suresh, Annamalai,Udendhran, R.,Vimal, S.
  • Publisher : IGI Global
  • Release Date : 2020-06-26
  • ISBN : 9781799835929
GET THIS BOOKDeep Neural Networks for Multimodal Imaging and Biomedical Applications

The field of healthcare is seeing a rapid expansion of technological advancement within current medical practices. The implementation of technologies including neural networks, multi-model imaging, genetic algorithms, and soft computing are assisting in predicting and identifying diseases, diagnosing cancer, and the examination of cells. Implementing these biomedical technologies remains a challenge for hospitals worldwide, creating a need for research on the specific applications of these computational techniques. Deep Neural Networks for Multimodal Imaging and Biomedical Applications provides research exploring the

Big Data in Multimodal Medical Imaging

Big Data in Multimodal Medical Imaging
  • Author : Ayman El-Baz,Jasjit S. Suri
  • Publisher : CRC Press
  • Release Date : 2019-11-06
  • ISBN : 9781351380720
GET THIS BOOKBig Data in Multimodal Medical Imaging

There is an urgent need to develop and integrate new statistical, mathematical, visualization, and computational models with the ability to analyze Big Data in order to retrieve useful information to aid clinicians in accurately diagnosing and treating patients. The main focus of this book is to review and summarize state-of-the-art big data and deep learning approaches to analyze and integrate multiple data types for the creation of a decision matrix to aid clinicians in the early diagnosis and identification of

Challenges and Trends in Multimodal Fall Detection for Healthcare

Challenges and Trends in Multimodal Fall Detection for Healthcare
  • Author : Hiram Ponce,Lourdes Martínez-Villaseñor,Jorge Brieva,Ernesto Moya-Albor
  • Publisher : Springer Nature
  • Release Date : 2020-01-28
  • ISBN : 9783030387488
GET THIS BOOKChallenges and Trends in Multimodal Fall Detection for Healthcare

This book focuses on novel implementations of sensor technologies, artificial intelligence, machine learning, computer vision and statistics for automated, human fall recognition systems and related topics using data fusion. It includes theory and coding implementations to help readers quickly grasp the concepts and to highlight the applicability of this technology. For convenience, it is divided into two parts. The first part reviews the state of the art in human fall and activity recognition systems, while the second part describes a

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016
  • Author : Sebastien Ourselin,Leo Joskowicz,Mert R. Sabuncu,Gozde Unal,William Wells
  • Publisher : Springer
  • Release Date : 2016-10-17
  • ISBN : 9783319467238
GET THIS BOOKMedical Image Computing and Computer-Assisted Intervention – MICCAI 2016

The three-volume set LNCS 9900, 9901, and 9902 constitutes the refereed proceedings of the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016, held in Athens, Greece, in October 2016. Based on rigorous peer reviews, the program committee carefully selected 228 revised regular papers from 756 submissions for presentation in three volumes. The papers have been organized in the following topical sections: Part I: brain analysis, brain analysis - connectivity; brain analysis - cortical morphology; Alzheimer disease; surgical guidance and tracking; computer aided interventions;

Interpretability of Machine Intelligence in Medical Image Computing and Multimodal Learning for Clinical Decision Support

Interpretability of Machine Intelligence in Medical Image Computing and Multimodal Learning for Clinical Decision Support
  • Author : Kenji Suzuki,Mauricio Reyes,Tanveer Syeda-Mahmood,Ender Konukoglu,Ben Glocker,Roland Wiest,Yaniv Gur,Hayit Greenspan,Anant Madabhushi
  • Publisher : Springer Nature
  • Release Date : 2019-10-24
  • ISBN : 9783030338503
GET THIS BOOKInterpretability of Machine Intelligence in Medical Image Computing and Multimodal Learning for Clinical Decision Support

This book constitutes the refereed joint proceedings of the Second International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2019, and the 9th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2019, held in conjunction with the 22nd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2019, in Shenzhen, China, in October 2019. The 7 full papers presented at iMIMIC 2019 and the 3 full papers presented at ML-CDS 2019 were carefully reviewed and selected from 10 submissions to iMIMIC and numerous submissions

Multimodal Analytics for Next-Generation Big Data Technologies and Applications

Multimodal Analytics for Next-Generation Big Data Technologies and Applications
  • Author : Kah Phooi Seng,Li-minn Ang,Alan Wee-Chung Liew,Junbin Gao
  • Publisher : Springer
  • Release Date : 2019-07-18
  • ISBN : 9783319975986
GET THIS BOOKMultimodal Analytics for Next-Generation Big Data Technologies and Applications

This edited book will serve as a source of reference for technologies and applications for multimodality data analytics in big data environments. After an introduction, the editors organize the book into four main parts on sentiment, affect and emotion analytics for big multimodal data; unsupervised learning strategies for big multimodal data; supervised learning strategies for big multimodal data; and multimodal big data processing and applications. The book will be of value to researchers, professionals and students in engineering and computer

Machine Learning for Multimodal Interaction

Machine Learning for Multimodal Interaction
  • Author : Samy Bengio,Hervé Bourlard
  • Publisher : Springer Science & Business Media
  • Release Date : 2005-01-31
  • ISBN : 9783540245094
GET THIS BOOKMachine Learning for Multimodal Interaction

This book constitutes the thoroughly refereed post-proceedings of the First International Workshop on Machine Learning for Multimodal Interaction, MLMI 2004, held in Martigny, Switzerland in June 2004. The 30 revised full papers presented were carefully selected during two rounds of reviewing and revision. The papers are organized in topical sections on HCI and applications, structuring and interaction, multimodal processing, speech processing, dialogue management, and vision and emotion.

The Handbook of Multimodal-Multisensor Interfaces, Volume 2

The Handbook of Multimodal-Multisensor Interfaces, Volume 2
  • Author : Sharon Oviatt,Björn Schuller,Philip Cohen,Daniel Sonntag,Gerasimos Potamianos,Antonio Krüger
  • Publisher : Morgan & Claypool
  • Release Date : 2018-10-08
  • ISBN : 9781970001693
GET THIS BOOKThe Handbook of Multimodal-Multisensor Interfaces, Volume 2

The Handbook of Multimodal-Multisensor Interfaces provides the first authoritative resource on what has become the dominant paradigm for new computer interfaces: user input involving new media (speech, multi-touch, hand and body gestures, facial expressions, writing) embedded in multimodal-multisensor interfaces that often include biosignals. This edited collection is written by international experts and pioneers in the field. It provides a textbook, reference, and technology roadmap for professionals working in this and related areas. This second volume of the handbook begins with

Remote Sensing Imagery

Remote Sensing Imagery
  • Author : Florence Tupin,Jordi Inglada,Jean-Marie Nicolas
  • Publisher : John Wiley & Sons
  • Release Date : 2014-02-19
  • ISBN : 9781118898925
GET THIS BOOKRemote Sensing Imagery

Dedicated to remote sensing images, from their acquisition to theiruse in various applications, this book covers the global lifecycleof images, including sensors and acquisition systems, applicationssuch as movement monitoring or data assimilation, and image anddata processing. It is organized in three main parts. The first part presentstechnological information about remote sensing (choice of satelliteorbit and sensors) and elements of physics related to sensing(optics and microwave propagation). The second part presents imageprocessing algorithms and their specificities for radar or optical,

Machine Learning Systems for Multimodal Affect Recognition

Machine Learning Systems for Multimodal Affect Recognition
  • Author : Markus Kächele
  • Publisher : Springer Nature
  • Release Date : 2019-11-19
  • ISBN : 9783658286743
GET THIS BOOKMachine Learning Systems for Multimodal Affect Recognition

Markus Kächele offers a detailed view on the different steps in the affective computing pipeline, ranging from corpus design and recording over annotation and feature extraction to post-processing, classification of individual modalities and fusion in the context of ensemble classifiers. He focuses on multimodal recognition of discrete and continuous emotional and medical states. As such, specifically the peculiarities that arise during annotation and processing of continuous signals are highlighted. Furthermore, methods are presented that allow personalization of datasets and

Multimodal Signal Processing

Multimodal Signal Processing
  • Author : Jean-Philippe Thiran,Ferran Marqués,Hervé Bourlard
  • Publisher : Academic Press
  • Release Date : 2009-11-11
  • ISBN : 0080888690
GET THIS BOOKMultimodal Signal Processing

Multimodal signal processing is an important research and development field that processes signals and combines information from a variety of modalities – speech, vision, language, text – which significantly enhance the understanding, modelling, and performance of human-computer interaction devices or systems enhancing human-human communication. The overarching theme of this book is the application of signal processing and statistical machine learning techniques to problems arising in this multi-disciplinary field. It describes the capabilities and limitations of current technologies, and discusses the technical challenges

Multimodal Sentiment Analysis

Multimodal Sentiment Analysis
  • Author : Soujanya Poria,Amir Hussain,Erik Cambria
  • Publisher : Springer
  • Release Date : 2018-10-24
  • ISBN : 9783319950204
GET THIS BOOKMultimodal Sentiment Analysis

This latest volume in the series, Socio-Affective Computing, presents a set of novel approaches to analyze opinionated videos and to extract sentiments and emotions. Textual sentiment analysis framework as discussed in this book contains a novel way of doing sentiment analysis by merging linguistics with machine learning. Fusing textual information with audio and visual cues is found to be extremely useful which improves text, audio and visual based unimodal sentiment analyzer. This volume covers the three main topics of: textual