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Author: Michael Ying Yang
Publisher: Academic Press
ISBN: 0128173599
Category : Technology & Engineering
Languages : en
Pages : 424
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Book Description
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
Author: Michael Ying Yang
Publisher: Academic Press
ISBN: 0128173599
Category : Technology & Engineering
Languages : en
Pages : 424
Get Book Here
Book Description
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
Author: Boris Schauerte
Publisher: Springer
ISBN: 3319337963
Category : Technology & Engineering
Languages : en
Pages : 220
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Book Description
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.
Author: Dana Kulić
Publisher: Springer
ISBN: 3319501151
Category : Technology & Engineering
Languages : en
Pages : 858
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Book Description
Experimental Robotics XV is the collection of papers presented at the International Symposium on Experimental Robotics, Roppongi, Tokyo, Japan on October 3-6, 2016. 73 scientific papers were selected and presented after peer review. The papers span a broad range of sub-fields in robotics including aerial robots, mobile robots, actuation, grasping, manipulation, planning and control and human-robot interaction, but shared cutting-edge approaches and paradigms to experimental robotics. The readers will find a breadth of new directions of experimental robotics. The International Symposium on Experimental Robotics is a series of bi-annual symposia sponsored by the International Foundation of Robotics Research, whose goal is to provide a forum dedicated to experimental robotics research. Robotics has been widening its scientific scope, deepening its methodologies and expanding its applications. However, the significance of experiments remains and will remain at the center of the discipline. The ISER gatherings are a venue where scientists can gather and talk about robotics based on this central tenet.
Author: Xavier Alameda-Pineda
Publisher: Academic Press
ISBN: 0128146028
Category : Technology & Engineering
Languages : en
Pages : 500
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Book Description
Multimodal Behavioral Analysis in the Wild: Advances and Challenges presents the state-of- the-art in behavioral signal processing using different data modalities, with a special focus on identifying the strengths and limitations of current technologies. The book focuses on audio and video modalities, while also emphasizing emerging modalities, such as accelerometer or proximity data. It covers tasks at different levels of complexity, from low level (speaker detection, sensorimotor links, source separation), through middle level (conversational group detection, addresser and addressee identification), and high level (personality and emotion recognition), providing insights on how to exploit inter-level and intra-level links. This is a valuable resource on the state-of-the- art and future research challenges of multi-modal behavioral analysis in the wild. It is suitable for researchers and graduate students in the fields of computer vision, audio processing, pattern recognition, machine learning and social signal processing. - Gives a comprehensive collection of information on the state-of-the-art, limitations, and challenges associated with extracting behavioral cues from real-world scenarios - Presents numerous applications on how different behavioral cues have been successfully extracted from different data sources - Provides a wide variety of methodologies used to extract behavioral cues from multi-modal data
Author: Rashmi Gupta
Publisher: CRC Press
ISBN: 1000453774
Category : Computers
Languages : en
Pages : 167
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Book Description
Many governments around the world are calling for the use of biometric systems to provide crucial societal functions, consequently making it an urgent area for action. The current performance of some biometric systems in terms of their error rates, robustness, and system security may prove to be inadequate for large-scale applications to process millions of users at a high rate of throughput. This book focuses on fusion in biometric systems. It discusses the present level, the limitations, and proposed methods to improve performance. It describes the fundamental concepts, current research, and security-related issues. The book will present a computational perspective, identify challenges, and cover new problem-solving strategies, offering solved problems and case studies to help with reader comprehension and deep understanding. This book is written for researchers, practitioners, both undergraduate and post-graduate students, and those working in various engineering fields such as Systems Engineering, Computer Science, Information Technology, Electronics, and Communications.
Author: Janina Wildfeuer
Publisher: Frontiers Media SA
ISBN: 2832551963
Category : Language Arts & Disciplines
Languages : en
Pages : 203
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Book Description
Multimodality has most recently been described no longer as a research field or discipline on its own, but rather as a “stage of development within a field” (Bateman 2022a, 49). The realization that (1) many different fields and disciplines now enter their own multimodal phase with new interest in multimodal phenomena and that (2) these disciplines all commit to the development of multimodality research with their own theoretical principles and methodological tools, brings with it not only an immense breadth of potential analytical objects, but also many new meta-methodological issues. “We need to find ways of ‘combining’ insights from the variously imported theoretical and methodological backgrounds brought along by previous non-multimodal stages of any contributing disciplines” (Bateman 2022a, 49). At the same time, the search for a meta-methodology for multimodal analyses is pushed further by the recent trend towards more empirical approaches to multimodal phenomena and the development and use of larger multimodal corpora that just as well require theoretical and methodological refinements. “We need to develop ways of strengthening claims with robustly applicable methods which nevertheless remain firmly anchored theoretically” (Bateman 2022b, 64). For a productive handling of these issues, disciplinary triangulation and finding a ‘common language’ or metalanguage (Maton & Chen 2016) for an ‘integrationist interdisciplinarity’ (van Leeuwen 2005) are the greatest challenges in contemporary multimodality research (Bateman 2022a). Also, there is a need for reconceptualizing the practice of analysis by making available large-scale corpora and broader and more complex empirical setups to fully process the ‘move from theory to data,’ and to substantiate long-lasting theoretical and methodological hypotheses (Pflaeging et al. 2021). For this project, we see these challenges productively as “a multimodal task from the ground up,” as John Bateman (2022b, 64) has phrased it in one of his most recent papers. This Research Topic will address this task by convening the most recent theoretical, methodological, practical, and empirical developments within contemporary multimodality research. The aim is to gain new insights in • the metalanguages or external languages that are currently being developed for multimodal analysis in many different research fields and disciplines, e.g., in pedagogy, literary theory, cultural studies, design, argumentation theory, computer science, and (experimental) psychology; • newest results from data collection methods and multimodal corpus analyses that expand the current quantitative work by, e.g., applying existing theories and methods to larger datasets, or exploring the newest communication technologies. We are particularly interested in seeing how works addressing these aspects contribute to finding ways of productive triangulation and integration for and within a meta-methodology for multimodality research. This Research Topic aims to bring together scholars from a variety of disciplines interested in multimodality research to review, explore, and advance the contributions that John Bateman, as one of the key figures in multimodality research, has made to both theory- and method-building as well as to the driving forward of multimodal empirical and corpus analyses. We welcome contributions that, for example, • critically address the theoretical and methodological advancements that John Bateman has made with regard to the notions of semiotic mode, discourse semantics, genre, textuality, etc.; • apply one of the many approaches that John Bateman has developed for the empirical analysis of multimodal artefacts (e.g., the GeM model for page-based documents, his work on multimodal film and audio-visual analysis, and the discourse semantics and/or annotation approach to visual narratives) to larger corpora or currently newly developing communicative situations; • expand on one of the abovementioned aspects with new ideas and insights from disciplines that have not yet been included in multimodality research.
Author: Colin W. K. Chen
Publisher: Springer Nature
ISBN: 9464635312
Category : Electronic books
Languages : en
Pages : 285
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Book Description
Author: Vijayan Sugumaran
Publisher: Springer Nature
ISBN: 3031054849
Category : Technology & Engineering
Languages : en
Pages : 1132
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Book Description
This book provides comprehensive coverage of the latest advances and trends in information technology, science and engineering. Specifically, it addresses a number of broad themes, including multi-modal informatics, data mining, agent-based and multi-agent systems for health and education informatics, which inspire the development of intelligent information technologies. The book covers a wide range of topics such as AI applications and innovations in health and education informatics; data and knowledge management; multi-modal application management; and web/social media mining for multi-modal informatics. Outlining promising future research directions, the book is a valuable resource for students, researchers and professionals and a useful reference guide for newcomers to the field. This book is a compilation of the papers presented in the 4th International Conference on Multi-modal Information Analytics, held online, on April 23, 2022.
Author: Aleš Leonardis
Publisher: Springer Nature
ISBN: 3031729862
Category :
Languages : en
Pages : 570
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Book Description
Author: Shih-Fu Chang
Publisher: Morgan & Claypool
ISBN: 1970001062
Category : Computers
Languages : en
Pages : 492
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Book Description
The field of multimedia is unique in offering a rich and dynamic forum for researchers from “traditional” fields to collaborate and develop new solutions and knowledge that transcend the boundaries of individual disciplines. Despite the prolific research activities and outcomes, however, few efforts have been made to develop books that serve as an introduction to the rich spectrum of topics covered by this broad field. A few books are available that either focus on specific subfields or basic background in multimedia. Tutorial-style materials covering the active topics being pursued by the leading researchers at frontiers of the field are currently lacking. In 2015, ACM SIGMM, the special interest group on multimedia, launched a new initiative to address this void by selecting and inviting 12 rising-star speakers from different subfields of multimedia research to deliver plenary tutorial-style talks at the ACM Multimedia conference for 2015. Each speaker discussed the challenges and state-of-the-art developments of their prospective research areas in a general manner to the broad community. The covered topics were comprehensive, including multimedia content understanding, multimodal human-human and human-computer interaction, multimedia social media, and multimedia system architecture and deployment. Following the very positive responses to these talks, the speakers were invited to expand the content covered in their talks into chapters that can be used as reference material for researchers, students, and practitioners. Each chapter discusses the problems, technical challenges, state-of-the-art approaches and performances, open issues, and promising direction for future work. Collectively, the chapters provide an excellent sampling of major topics addressed by the community as a whole. This book, capturing some of the outcomes of such efforts, is well positioned to fill the aforementioned needs in providing tutorial-style reference materials for frontier topics in multimedia. At the same time, the speed and sophistication required of data processing have grown. In addition to simple queries, complex algorithms like machine learning and graph analysis are becoming common. And in addition to batch processing, streaming analysis of real-time data is required to let organizations take timely action. Future computing platforms will need to not only scale out traditional workloads, but support these new applications too. This book, a revised version of the 2014 ACM Dissertation Award winning dissertation, proposes an architecture for cluster computing systems that can tackle emerging data processing workloads at scale. Whereas early cluster computing systems, like MapReduce, handled batch processing, our architecture also enables streaming and interactive queries, while keeping MapReduce's scalability and fault tolerance. And whereas most deployed systems only support simple one-pass computations (e.g., SQL queries), ours also extends to the multi-pass algorithms required for complex analytics like machine learning. Finally, unlike the specialized systems proposed for some of these workloads, our architecture allows these computations to be combined, enabling rich new applications that intermix, for example, streaming and batch processing. We achieve these results through a simple extension to MapReduce that adds primitives for data sharing, called Resilient Distributed Datasets (RDDs). We show that this is enough to capture a wide range of workloads. We implement RDDs in the open source Spark system, which we evaluate using synthetic and real workloads. Spark matches or exceeds the performance of specialized systems in many domains, while offering stronger fault tolerance properties and allowing these workloads to be combined. Finally, we examine the generality of RDDs from both a theoretical modeling perspective and a systems perspective. This version of the dissertation makes corrections throughout the text and adds a new section on the evolution of Apache Spark in industry since 2014. In addition, editing, formatting, and links for the references have been added.
