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Modeling Visual Aesthetics, Emotion, and Artistic Style

Title
Modeling Visual Aesthetics, Emotion, and Artistic Style [electronic resource] / edited by James Z. Wang, Reginald B. Adams, Jr.
ISBN
9783031502699
Edition
1st ed. 2024.
Publication
Cham : Springer International Publishing : Imprint: Springer, 2024.
Physical Description
1 online resource (XXXI, 396 p.) 103 illus., 79 illus. in color.
Local Notes
Access is available to the Yale community.
Access and use
Access restricted by licensing agreement.
Summary
Modeling Visual Aesthetics, Emotion, and Artistic Style offers a comprehensive exploration of the increasingly significant topic of the complex interplay between human perception and digital technology. It embodies the cumulative knowledge and efforts of a wide array of active researchers and practitioners from diverse fields including computer vision, affective computing, robotics, psychology, data mining, machine learning, art history, and movement analysis. This volume seeks to address the profound and challenging research questions related to the computational modeling and analysis of visual aesthetics, emotions, and artistic style, vital components of the human experience that are increasingly relevant in our digitally connected world. The book's vast scope encompasses a broad range of topics. The initial chapters lay a strong foundation with background knowledge on emotion models and machine learning, which then transitions into exploring social visual perception in humans and its technological applications. Readers will uncover the psychological and neurological foundations of social and emotional perception from faces and bodies. Subsequent sections broaden this understanding to include technology's role in detecting discrete and subtle emotional expressions, examining facial neutrality, and including research contexts that involve children as well as adults. Furthermore, the book illuminates the dynamic intersection of art and technology, the language of photography, the relationship between breath-driven robotic performances and human dance, and the application of machine learning in analyzing artistic styles. This book sets itself apart with its unique multidisciplinary approach, encouraging collaboration across related domains. Packed with comprehensive tutorials, theoretical reviews, novel methodologies, empirical investigations, and comparative analyses, the book offers a rich combination of knowledge and methodologies. The book's focus on cutting-edge research not only presents the latest developments in the field but also illuminates potential paths that can lead to significant advancements in computer and robotic applications.
Variant and related titles
Springer ENIN.
Other formats
Printed edition:
Printed edition:
Printed edition:
Format
Books / Online
Language
English
Added to Catalog
April 10, 2024
Contents
Models of Human Emotion and Artificial Emotional Intelligence
A Concise Introduction to Machine Learning
Facing a Perceptual Crossroads: Mixed Messages and Shared Meanings in Social Visual Perception
Social Vision of the Body In Motion: Interactions Between the Perceiver and the Perceived
Visual Perception of Threat: Structure, Dynamics, and Individual Differences
From Pixels to Power: Critical Feminist Questions for the Ethics of Computer Vision
High-Speed Joint Learning of Action Units and Facial Expressions
ExpressionFlow: A Microexpression Descriptor for Efficient Recognition
Emotion in the Neutral Face: Applications for Computer Vision and Aesthetics
Multi-stream Temporal Networks for Emotion Recognition in Children and In the Wild
The Formal Language of Photography: A Primer
Breathing With Robots: Notating Performer Strategy, Alongside Choreographer Intent and Audience Observation, In Breath-driven Robotic Dance Performance
Humanist-in-the-Loop: Machine Learning and the Analysis of Style in the Visual Arts
The Inter-relationship between Photographic Aesthetics and Technical Quality
Image Restoration for Beautification
Image Affect Modeling: An Industrial Perspective
Emotional Expression as a Means of Communicating Virtual Human Personalities
Modeling Emotion Perception from Body Movements for Human-Machine Interactions using Laban Movement Analysis
Demographic Differences and Biases in Affect Evoked by Visual Features
Deep Network-based Computational Transfer of Artistic Style in Art Analysis
Balance of Unity and Variety in Fine Art Paintings: A Computational Study.
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