Multimodal Computational Attention for Scene Understanding
Author | : Boris Schauerte |
Publisher | : |
Total Pages | : |
Release | : 2014 |
Genre | : |
ISBN | : |
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Author | : Boris Schauerte |
Publisher | : |
Total Pages | : |
Release | : 2014 |
Genre | : |
ISBN | : |
Author | : Boris Schauerte |
Publisher | : Springer |
Total Pages | : 220 |
Release | : 2016-05-11 |
Genre | : Technology & Engineering |
ISBN | : 3319337963 |
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 | : Michael Yang |
Publisher | : Academic Press |
Total Pages | : 422 |
Release | : 2019-07-16 |
Genre | : Computers |
ISBN | : 0128173599 |
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 | : Grotz, Markus |
Publisher | : KIT Scientific Publishing |
Total Pages | : 202 |
Release | : 2021-12-21 |
Genre | : Computers |
ISBN | : 3731511010 |
Visual perception is one of the most important sources of information for both humans and robots. A particular challenge is the acquisition and interpretation of complex unstructured scenes. This work contributes to active vision for humanoid robots. A semantic model of the scene is created, which is extended by successively changing the robot's view in order to explore interaction possibilities of the scene.
Author | : Matei Mancas |
Publisher | : Springer |
Total Pages | : 456 |
Release | : 2016-06-29 |
Genre | : Medical |
ISBN | : 149393435X |
This both accessible and exhaustive book will help to improve modeling of attention and to inspire innovations in industry. It introduces the study of attention and focuses on attention modeling, addressing such themes as saliency models, signal detection and different types of signals, as well as real-life applications. The book is truly multi-disciplinary, collating work from psychology, neuroscience, engineering and computer science, amongst other disciplines. What is attention? We all pay attention every single moment of our lives. Attention is how the brain selects and prioritizes information. The study of attention has become incredibly complex and divided: this timely volume assists the reader by drawing together work on the computational aspects of attention from across the disciplines. Those working in the field as engineers will benefit from this book’s introduction to the psychological and biological approaches to attention, and neuroscientists can learn about engineering work on attention. The work features practical reviews and chapters that are quick and easy to read, as well as chapters which present deeper, more complex knowledge. Everyone whose work relates to human perception, to image, audio and video processing will find something of value in this book, from students to researchers and those in industry.
Author | : Zoya Bylinskii |
Publisher | : |
Total Pages | : 192 |
Release | : 2018 |
Genre | : |
ISBN | : |
Multimodal documents occur in a variety of forms, as graphs in technical reports, diagrams in textbooks, and graphic designs in bulletins. Humans can efficiently process the visual and textual information contained within to make decisions on topics including business, healthcare, and science. Building the computational tools to understand multimodal documents can have important applications for web search, information retrieval, captioning and summarization, and automated design. This thesis makes contributions on two fronts: (i) to the development of data collection methods for measuring how humans perceive multimodal documents (i.e., where they look, what they find important), and (ii) to the development of computer vision tools for automatically parsing and making predictions about multimodal documents (i.e., the subject matter they are about). Specifically, the crowdsourced attention data captured from our novel user interfaces is used to train neural network models to predict where people look in graphic designs and information visualizations, with demonstrated applications to thumbnailing, design retargeting, and interactive feedback within graphic design tools. Separately, our models for detecting visual elements and parsing text elements in infographics (information graphics) are used for topic prediction and to present a system for automatic summarization. This thesis makes contributions at the interface of human and computer vision, with applications to human-computer interfaces and design.
Author | : G. Hommel |
Publisher | : Springer Science & Business Media |
Total Pages | : 192 |
Release | : 2006-10-03 |
Genre | : Technology & Engineering |
ISBN | : 1402040431 |
The International Workshop on “Human Interaction with Machines” is the sixth in a successful series of workshops that were established by Shanghai Jiao Tong University and Technische Universität Berlin. The goal of those workshops is to bring together researchers from both universities in order to present research results to an international community. The series of workshops started in 1990 with the International Workshop on “Artificial Intelligence” and was continued with the International Workshop on “Advanced Software Technology” in 1994. Both workshops have been hosted by Shanghai Jiaotong University. In 1998 the third wo- shop took place in Berlin. This International Workshop on “Communi- tion Based Systems” was essentially based on results from the Graduiertenkolleg on Communication Based Systems that was funded by the German Research Society (DFG) from 1991 to 2000. The fourth Int- national Workshop on “Robotics and its Applications” was held in Sha- hai in 2000. The fifth International Workshop on “The Internet Challenge: Technology and Applications” was hosted by TU Berlin in 2002.
Author | : Hua Xu |
Publisher | : Springer Nature |
Total Pages | : 278 |
Release | : 2023-11-26 |
Genre | : Technology & Engineering |
ISBN | : 9819957761 |
The natural interaction ability between human and machine mainly involves human-machine dialogue ability, multi-modal sentiment analysis ability, human-machine cooperation ability, and so on. To enable intelligent computers to have multi-modal sentiment analysis ability, it is necessary to equip them with a strong multi-modal sentiment analysis ability during the process of human-computer interaction. This is one of the key technologies for efficient and intelligent human-computer interaction. This book focuses on the research and practical applications of multi-modal sentiment analysis for human-computer natural interaction, particularly in the areas of multi-modal information feature representation, feature fusion, and sentiment classification. Multi-modal sentiment analysis for natural interaction is a comprehensive research field that involves the integration of natural language processing, computer vision, machine learning, pattern recognition, algorithm, robot intelligent system, human-computer interaction, etc. Currently, research on multi-modal sentiment analysis in natural interaction is developing rapidly. This book can be used as a professional textbook in the fields of natural interaction, intelligent question answering (customer service), natural language processing, human-computer interaction, etc. It can also serve as an important reference book for the development of systems and products in intelligent robots, natural language processing, human-computer interaction, and related fields.
Author | : Simone Frintrop |
Publisher | : Springer Science & Business Media |
Total Pages | : 219 |
Release | : 2006-04-06 |
Genre | : Computers |
ISBN | : 3540327592 |
This monograph presents a complete computational system for visual attention and object detection. VOCUS (Visual Object detection with a Computational attention System) represents a major step forward on integrating data-driven and model-driven information into a single framework. Additionally, the volume contains an extensive review of the literature on visual attention, detailed evaluations of VOCUS in different settings, and applications of the system.
Author | : Pijush Samui |
Publisher | : Academic Press |
Total Pages | : 660 |
Release | : 2017-07-18 |
Genre | : Technology & Engineering |
ISBN | : 0128113197 |
Handbook of Neural Computation explores neural computation applications, ranging from conventional fields of mechanical and civil engineering, to electronics, electrical engineering and computer science. This book covers the numerous applications of artificial and deep neural networks and their uses in learning machines, including image and speech recognition, natural language processing and risk analysis. Edited by renowned authorities in this field, this work is comprised of articles from reputable industry and academic scholars and experts from around the world. Each contributor presents a specific research issue with its recent and future trends. As the demand rises in the engineering and medical industries for neural networks and other machine learning methods to solve different types of operations, such as data prediction, classification of images, analysis of big data, and intelligent decision-making, this book provides readers with the latest, cutting-edge research in one comprehensive text. Features high-quality research articles on multivariate adaptive regression splines, the minimax probability machine, and more Discusses machine learning techniques, including classification, clustering, regression, web mining, information retrieval and natural language processing Covers supervised, unsupervised, reinforced, ensemble, and nature-inspired learning methods