東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Human perception of visual informati...

Ionescu, Bogdan.

Linked to FindBook

Google Book

Amazon

博客來

Human perception of visual information = psychological and computational perspectives /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Human perception of visual information/ edited by Bogdan Ionescu, Wilma A. Bainbridge, Naila Murray.
Reminder of title:	psychological and computational perspectives /
other author:	Ionescu, Bogdan.
Published:	Cham :Springer International Publishing : : 2022.,
Description:	ix, 292 p. :ill., digital ;24 cm.
[NT 15003449]:	Preface -- Chapter 1 -- The Ingredients of Scenes That Affect Object Search and Perception -- Chapter 2 -- Exploring Deep Fusion Ensembling for Automatic Visual Interestingness Prediction -- Chapter 3 -- Affective Perception: The Power is in the Picture -- Chapter 4 -- Computational Emotion Analysis From Images: Recent Advances and Future Directions -- Chapter 5 -- The Interplay Of Objective And Subjective Factors In Empirical Aesthetics -- Chapter 6 -- Advances and Challenges in Computational Image Aesthetics -- Chapter 7 -- Shared Memories Driven by the Intrinsic Memorability of Items -- Chapter 8 -- Memorability: an Image-computable Measure of Information Utility -- Chapter 9 -- The Influence of Low -- and Mid-Level Visual Features on the Perception of Streetscape Qualities -- Chapter 10 -- Who Sees What? Examining Urban Impressions in Global South Cities.
Contained By:	Springer Nature eBook
Subject:	Optical pattern recognition. -
Online resource:	https://doi.org/10.1007/978-3-030-81465-6
ISBN:	9783030814656

Human perception of visual information = psychological and computational perspectives /
Human perception of visual informationpsychological and computational perspectives /[electronic resource] :edited by Bogdan Ionescu, Wilma A. Bainbridge, Naila Murray. - Cham :Springer International Publishing :2022. - ix, 292 p. :ill., digital ;24 cm.

Preface -- Chapter 1 -- The Ingredients of Scenes That Affect Object Search and Perception -- Chapter 2 -- Exploring Deep Fusion Ensembling for Automatic Visual Interestingness Prediction -- Chapter 3 -- Affective Perception: The Power is in the Picture -- Chapter 4 -- Computational Emotion Analysis From Images: Recent Advances and Future Directions -- Chapter 5 -- The Interplay Of Objective And Subjective Factors In Empirical Aesthetics -- Chapter 6 -- Advances and Challenges in Computational Image Aesthetics -- Chapter 7 -- Shared Memories Driven by the Intrinsic Memorability of Items -- Chapter 8 -- Memorability: an Image-computable Measure of Information Utility -- Chapter 9 -- The Influence of Low -- and Mid-Level Visual Features on the Perception of Streetscape Qualities -- Chapter 10 -- Who Sees What? Examining Urban Impressions in Global South Cities.

Recent years have witnessed important advancements in our understanding of the psychological underpinnings of subjective properties of visual information, such as aesthetics, memorability, or induced emotions. Concurrently, computational models of objective visual properties such as semantic labelling and geometric relationships have made significant breakthroughs using the latest achievements in machine learning and large-scale data collection. There has also been limited but important work exploiting these breakthroughs to improve computational modelling of subjective visual properties. The time is ripe to explore how advances in both of these fields of study can be mutually enriching and lead to further progress. This book combines perspectives from psychology and machine learning to showcase a new, unified understanding of how images and videos influence high-level visual perception - particularly interestingness, affective values and emotions, aesthetic values, memorability, novelty, complexity, visual composition and stylistic attributes, and creativity. These human-based metrics are interesting for a very broad range of current applications, ranging from content retrieval and search, storytelling, to targeted advertising, education and learning, and content filtering. Work already exists in the literature that studies the psychological aspects of these notions or investigates potential correlations between two or more of these human concepts. Attempts at building computational models capable of predicting such notions can also be found, using state-of-the-art machine learning techniques. Nevertheless their performance proves that there is still room for improvement, as the tasks are by nature highly challenging and multifaceted, requiring thought on both the psychological implications of the human concepts, as well as their translation to machines.

ISBN: 9783030814656

Standard No.: 10.1007/978-3-030-81465-6doiSubjects--Topical Terms:

665526
Optical pattern recognition.

LC Class. No.: TA1650 / .H85 2022

Dewey Class. No.: 006.42

Human perception of visual information = psychological and computational perspectives /
LDR:03804nmm a2200325 a 4500 001 2296916
003 DE-He213
005 20211217155230.0
006 m d
007 cr nn 008maaau
008 230324s2022 sz s 0 eng d
020 $a 9783030814656 $q (electronic bk.)
020 $a 9783030814649 $q (paper)
024 7 $a 10.1007/978-3-030-81465-6 $2 doi
035 $a 978-3-030-81465-6
040 $a GP $c GP
041 0 $a eng
050 4 $a TA1650 $b .H85 2022
072 7 $a UYQV $2 bicssc
072 7 $a COM016000 $2 bisacsh
072 7 $a UYQV $2 thema
082 0 4 $a 006.42 $2 23
090 $a TA1650 $b .H918 2022
245 0 0 $a Human perception of visual information $h [electronic resource] : $b psychological and computational perspectives / $c edited by Bogdan Ionescu, Wilma A. Bainbridge, Naila Murray.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2022.
300 $a ix, 292 p. : $b ill., digital ; $c 24 cm.
505 0 $a Preface -- Chapter 1 -- The Ingredients of Scenes That Affect Object Search and Perception -- Chapter 2 -- Exploring Deep Fusion Ensembling for Automatic Visual Interestingness Prediction -- Chapter 3 -- Affective Perception: The Power is in the Picture -- Chapter 4 -- Computational Emotion Analysis From Images: Recent Advances and Future Directions -- Chapter 5 -- The Interplay Of Objective And Subjective Factors In Empirical Aesthetics -- Chapter 6 -- Advances and Challenges in Computational Image Aesthetics -- Chapter 7 -- Shared Memories Driven by the Intrinsic Memorability of Items -- Chapter 8 -- Memorability: an Image-computable Measure of Information Utility -- Chapter 9 -- The Influence of Low -- and Mid-Level Visual Features on the Perception of Streetscape Qualities -- Chapter 10 -- Who Sees What? Examining Urban Impressions in Global South Cities.
520 $a Recent years have witnessed important advancements in our understanding of the psychological underpinnings of subjective properties of visual information, such as aesthetics, memorability, or induced emotions. Concurrently, computational models of objective visual properties such as semantic labelling and geometric relationships have made significant breakthroughs using the latest achievements in machine learning and large-scale data collection. There has also been limited but important work exploiting these breakthroughs to improve computational modelling of subjective visual properties. The time is ripe to explore how advances in both of these fields of study can be mutually enriching and lead to further progress. This book combines perspectives from psychology and machine learning to showcase a new, unified understanding of how images and videos influence high-level visual perception - particularly interestingness, affective values and emotions, aesthetic values, memorability, novelty, complexity, visual composition and stylistic attributes, and creativity. These human-based metrics are interesting for a very broad range of current applications, ranging from content retrieval and search, storytelling, to targeted advertising, education and learning, and content filtering. Work already exists in the literature that studies the psychological aspects of these notions or investigates potential correlations between two or more of these human concepts. Attempts at building computational models capable of predicting such notions can also be found, using state-of-the-art machine learning techniques. Nevertheless their performance proves that there is still room for improvement, as the tasks are by nature highly challenging and multifaceted, requiring thought on both the psychological implications of the human concepts, as well as their translation to machines.
650 0 $a Optical pattern recognition. $3 665526
650 0 $a Computer vision. $3 540671
650 0 $a Machine learning. $3 533906
650 1 4 $a Computer Imaging, Vision, Pattern Recognition and Graphics. $3 890871
650 2 4 $a Signal, Image and Speech Processing. $3 891073
650 2 4 $a Cognitive Psychology. $3 891220
650 2 4 $a User Interfaces and Human Computer Interaction. $3 892554
700 1 $a Ionescu, Bogdan. $3 2059230
700 1 $a Bainbridge, Wilma A. $3 3591971
700 1 $a Murray, Naila. $3 3591972
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-3-030-81465-6
950 $a Computer Science (SpringerNature-11645)