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.
Cuprins
Foreword, V. Cutsuridis.- 1 Why modeling attention in computers?, M. Mancas, V. Ferrera, N. Riche.- 2 What is attention?, M. Mancas.- 3 How to measure attention?, M. Mancas, V. Ferrera.- 4 Where: Human attention networks and their dysfunctions after brain damage, T. Seidel Malkinson, P. Bartolomeo.- 5 Attention and Signal Detection: A Practical Guide, V. Ferrera.- 6 Effects of Attention in Visual Cortex: Linking Single Neuron Physiology to Visual Detection and Discrimination, V. Ferrera.- 7 Modeling attention in engineering, M. Mancas.- 8 Bottom-Up Visual Attention for Still Images: a Global View, F. Stentiford.- 9 Bottom-up saliency models for still images: a practical review, N. Riche and M. Mancas.- 10 Bottom-up saliency models for videos: a practical review, N. Riche and M. Mancas.- 11 Databases for saliency models evaluation, N. Riche.- 12 Metrics for saliency models validation, N. Riche.- 13 Study of parameters affecting visual saliency assessment, N. Riche.- 14 Saliency models evaluation, N. Riche.- 15 Object-based Attention: cognitive and computational perspectives, A. Belardinelli.- 16 Multimodal saliency models for videos, Antoine Coutrot, Nathalie Guyader.- 17 Towards 3D visual saliency modelling, J. Leroy, N. Riche.- 18 Applications of saliency models, M. Mancas, O. Le Meur.- 19 Attentive Content-Based Image Retrieval, D. Awad, V. Courboulay, A. Revel.- 20 Saliency and Attention for Video Quality Assessment, D. Culibrk.- 21 Attentive Robots, S. Frintrop.- 22 Attention modeling: what are the next steps?, M. Mancas, V. Ferrera, N. Riche.- Index
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