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From human attention to computational attention [electronic resource] : a multidisciplinary approach

From human attention to computational attention [electronic resource] : a multidisciplinary approach

Material type
E-Book(소장)
Personal Author
Mancas, Matei.
Title Statement
From human attention to computational attention [electronic resource] : a multidisciplinary approach / Matei Mancas ... [et al.], editors.
Publication, Distribution, etc
New York, NY :   Springer New York :   Imprint: Springer,   2016.  
Physical Medium
1 online resource (viii, 463 p.) : ill. (some col.).
Series Statement
Springer series in cognitive and neural systems,2363-9105, 2363-9113 (electronic) ; volume 10
ISBN
9781493934355
요약
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.
General Note
Title from e-Book title page.  
Content Notes
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.
Bibliography, Etc. Note
Includes bibliographical references and index.
이용가능한 다른형태자료
Issued also as a book.  
Subject Added Entry-Topical Term
Attention. Attention --Computer simulation. Artificial intelligence. Computational neuroscience.
Short cut
URL
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020 ▼a 9781493934355
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050 0 0 ▼a RC455.4.A85
082 0 0 ▼a 153.7/33 ▼2 23
084 ▼a 153.733 ▼2 DDCK
090 ▼a 153.733
245 0 0 ▼a From human attention to computational attention ▼h [electronic resource] : ▼b a multidisciplinary approach / ▼c Matei Mancas ... [et al.], editors.
260 ▼a New York, NY : ▼b Springer New York : ▼b Imprint: Springer, ▼c 2016.
300 ▼a 1 online resource (viii, 463 p.) : ▼b ill. (some col.).
490 1 ▼a Springer series in cognitive and neural systems, ▼x 2363-9105, ▼x 2363-9113 (electronic) ; ▼v volume 10
500 ▼a Title from e-Book title page.
504 ▼a Includes bibliographical references and index.
505 0 ▼a 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.
520 ▼a 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.
530 ▼a Issued also as a book.
538 ▼a Mode of access: World Wide Web.
650 0 ▼a Attention.
650 0 ▼a Attention ▼x Computer simulation.
650 0 ▼a Artificial intelligence.
650 0 ▼a Computational neuroscience.
700 1 ▼a Mancas, Matei.
830 0 ▼a Springer series in cognitive and neural systems ; ▼v v. 10.
856 4 0 ▼u https://oca.korea.ac.kr/link.n2s?url=http://dx.doi.org/10.1007/978-1-4939-3435-5
945 ▼a KLPA
991 ▼a E-Book(소장)

Holdings Information

No. Location Call Number Accession No. Availability Due Date Make a Reservation Service
No. 1 Location Main Library/e-Book Collection/ Call Number CR 153.733 Accession No. E14019723 Availability Loan can not(reference room) Due Date Make a Reservation Service M

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