Cover Image

From Human Attention to Computational Attention A Multidisciplinary Approach /

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 r...

Full description

Corporate Author: SpringerLink (Online service)
Other Authors: Mancas, Matei. (Editor, http://id.loc.gov/vocabulary/relators/edt), Ferrera, Vincent P. (Editor, http://id.loc.gov/vocabulary/relators/edt), Riche, Nicolas. (Editor, http://id.loc.gov/vocabulary/relators/edt), Taylor, John G. (Editor, http://id.loc.gov/vocabulary/relators/edt)
Language:English
Published: New York, NY : Springer New York : Imprint: Springer, 2016.
Edition:1st ed. 2016.
Series:Springer Series in Cognitive and Neural Systems, 10
Subjects:
Neurosciences.
Optical data processing.
Signal processing.
Image processing.
Speech processing systems.
Artificial intelligence.
Human physiology.
Image Processing and Computer Vision.
Signal, Image and Speech Processing.
Artificial Intelligence.
Human Physiology.
Online Access:https://doi.org/10.1007/978-1-4939-3435-5
Table of Contents:
  • 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.

Similar Items