Intelligent Scene Modeling And Human Computer Interaction
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Intelligent Scene Modeling and Human-Computer Interaction
Author: Nadia Magnenat Thalmann
language: en
Publisher: Springer Nature
Release Date: 2021-06-08
This edited book is one of the first to describe how Autonomous Virtual Humans and Social Robots can interact with real people and be aware of the surrounding world using machine learning and AI. It includes: · Many algorithms related to the awareness of the surrounding world such as the recognition of objects, the interpretation of various sources of data provided by cameras, microphones, and wearable sensors · Deep Learning Methods to provide solutions to Visual Attention, Quality Perception, and Visual Material Recognition · How Face Recognition and Speech Synthesis will replace the traditional mouse and keyboard interfaces · Semantic modeling and rendering and shows how these domains play an important role in Virtual and Augmented Reality Applications. Intelligent Scene Modeling and Human-Computer Interaction explains how to understand the composition and build very complex scenes and emphasizes the semantic methods needed to have an intelligent interaction with them. It offers readers a unique opportunity to comprehend the rapid changes and continuous development in the fields of Intelligent Scene Modeling.
Intelligent Scene Modelling Information Systems
Author: Georgios Miaoulis
language: en
Publisher: Springer Science & Business Media
Release Date: 2009-03-06
This book is dedicated to and contains the latest research in intelligent scene modelling information systems. Declarative scene modeling techniques are presented, as well as their implementation in an intelligent information system.
Human-Computer Interaction and Augmented Intelligence
This book explores the transformative roles of human-computer interaction (HCI) and augmented intelligence (AI) in shaping intelligent systems. HCI focuses on designing interactive systems that enhance human-technology relationships, while AI empowers users with adaptive, data-driven tools that complement decision-making. Together, these fields drive innovation, creating systems that are efficient, intuitive, and inclusive, addressing diverse user needs across various domains. Central to this work is the paradigm of interactive machine learning (IML), which builds on HCI and AI principles to create adaptive systems capable of evolving in real-time. The book highlights the application of IML in educational software, demonstrating how dynamic, personalized, and responsive learning environments can enhance student engagement and success. It provides detailed case studies and practical examples that showcase how IML aligns educational content, feedback, and interactions with learner behaviors and preferences. Additionally, it includes numerous Python code implementations and actionable design strategies, making these concepts accessible to practitioners and researchers alike. Key topics include leveraging cognitive and communication styles to shape adaptive systems, integrating learning models to enhance personalization, and addressing ethical considerations such as data privacy and algorithmic fairness. Readers will also discover discussions on creating personalized tutoring systems, collaborative platforms, and immersive environments that redefine educational technology. This book is a valuable resource for researchers, software developers, educators, instructional designers, and technologists at the intersection of human-computer interaction, augmented intelligence, and educational innovation. With its comprehensive framework and practical insights, it offers the tools to design adaptive, inclusive, and impactful learning systems for the future.