From Human Attention to Computational Attention : A Multidisciplinary Approach / edited by Matei Mancas, Vincent P. Ferrera, Antoine Coutrot.

Format:

Book

Author/Creator:

Mancas, Matei.

Contributor:

Ferrera, Vincent P.

Coutrot, Antoine.

Series:

Biomedical and Life Sciences Series

Language:

English

Subjects (All):

Neurosciences.

Computer vision.

Signal processing.

Artificial intelligence.

Computational neuroscience.

Neuroscience.

Computer Vision.

Signal, Speech and Image Processing.

Artificial Intelligence.

Computational Neuroscience.

Local Subjects:

Neuroscience.

Computer Vision.

Signal, Speech and Image Processing.

Artificial Intelligence.

Computational Neuroscience.

Physical Description:

1 online resource (409 pages)

Edition:

2nd ed. 2025.

Place of Publication:

Cham : Springer Nature Switzerland : Imprint: Springer, 2025.

Summary:

The new edition of this popular book introduces the study of attention, focusing on attention modeling, and addressing such themes as saliency models, signal detection, and different types of signals, including real-life applications. The first edition was written at a moment when the Deep Learning Neural Network (DNNs) techniques were just at their beginnings in terms of attention. Deep learning has recently become a key factor in attention prediction on images and video, and attention mechanisms have become key factors in deep learning models. The second edition tackles the arrival of DNNs for attention computing in images and video, and also discusses the attention mechanisms within DNNs (attention modules, transformers, grad-cam-based saliency maps, etc.). From Human Attention to Computational Attention 2nd Edition also explores the parallels between the brain structures and the DNN architectures to reveal how biomimetics can improve the model designs. The book is truly multi-disciplinary, collating work from psychology, neuroscience, engineering, and computer science.

Contents:

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.

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

3-031-84300-2

OCLC:

1528362682