Computational Models of Visual Attention

2014 ◽  
pp. 54-76 ◽  
Author(s):  
Rajarshi Pal
Keyword(s):  
Computer Vision ◽  
Visual Attention ◽  
Human Brain ◽  
Computational Modeling ◽  
Computational Models ◽  
Sensory Information ◽  
Processing Capacity ◽  
Human Beings ◽  
Psychological Phenomenon ◽  
External Stimuli

Even the enormous processing capacity of the human brain is not enough to handle all the visual sensory information that falls upon the retina. Still human beings can efficiently respond to the external stimuli. Selective attention plays an important role here. It helps to select only the pertinent portions of the scene being viewed for further processing at the deeper brain. Computational modeling of this neuro-psychological phenomenon has the potential to enrich many computer vision tasks. Enormous amounts of research involving psychovisual experiments and computational models of attention have been and are being carried out all within the past few decades. This article compiles a good volume of these research efforts. It also discusses various aspects related to computational modeling of attention–such as, choice of features, evaluation of these models, and so forth.

Computational Models of Visual Attention

2018 ◽  
pp. 1-26
Author(s):  
Rajarshi Pal
Keyword(s):  
Computer Vision ◽  
Visual Attention ◽  
Human Brain ◽  
Computational Modeling ◽  
Computational Models ◽  
Sensory Information ◽  
Processing Capacity ◽  
Human Beings ◽  
Psychological Phenomenon ◽  
External Stimuli

Even the enormous processing capacity of the human brain is not enough to handle all the visual sensory information that falls upon the retina. Still human beings can efficiently respond to the external stimuli. Selective attention plays an important role here. It helps to select only the pertinent portions of the scene being viewed for further processing at the deeper brain. Computational modeling of this neuro-psychological phenomenon has the potential to enrich many computer vision tasks. Enormous amounts of research involving psychovisual experiments and computational models of attention have been and are being carried out all within the past few decades. This article compiles a good volume of these research efforts. It also discusses various aspects related to computational modeling of attention–such as, choice of features, evaluation of these models, and so forth.

scholarly journals Computational Models of Face Perception

2017 ◽  
Vol 26 (3) ◽  
pp. 263-269 ◽  
Author(s):  
Aleix M. Martinez
Keyword(s):  
Computer Vision ◽  
Inverse Problem ◽  
Facial Expression ◽  
Computational Modeling ◽  
Face Perception ◽  
Computational Models ◽  
Emotional State ◽  
Current Understanding ◽  
Important Means ◽  
The Brain

Faces are one of the most important means of communication for humans. For example, a short glance at a person’s face provides information about his or her identity and emotional state. What are the computations the brain uses to acquire this information so accurately and seemingly effortlessly? This article summarizes current research on computational modeling, a technique used to answer this question. Specifically, my research tests the hypothesis that this algorithm is tasked with solving the inverse problem of production. For example, to recognize identity, our brain needs to identify shape and shading features that are invariant to facial expression, pose, and illumination. Similarly, to recognize emotion, the brain needs to identify shape and shading features that are invariant to identity, pose, and illumination. If one defines the physics equations that render an image under different identities, expressions, poses, and illuminations, then gaining invariance to these factors can be readily resolved by computing the inverse of this rendering function. I describe our current understanding of the algorithms used by our brains to resolve this inverse problem. I also discuss how these results are driving research in computer vision to design computer systems that are as accurate, robust, and efficient as humans.

Applications of Visual Attention in Image Processing, Computer Vision, and Graphics

2018 ◽  
pp. 1662-1685
Author(s):  
Rajarshi Pal
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Visual Attention ◽  
Visual Information ◽  
Computational Models ◽  
External Environment ◽  
Real Life ◽  
Visual Content ◽  
Selective Visual Attention ◽  
Speed Up

Selective visual attention is an amazing capability of primate visual system to restrict the focus to few interesting objects (or portions) in a scene. Thus, primates are able to pay attention to the required visual content amidst myriads of other visual information. It enables them to interact with the external environment in real time through reduction of computational load in their brain. This inspires image and computer vision scientists to derive computational models of visual attention and to use them in varieties of applications in real-life, mainly to speed up the processing through reduction of computational burden which often characterizes image processing and vision tasks. This chapter discusses a wide variety of such applications of visual attention models in image processing, computer vision and graphics.

2D and 3D Visual Attention for Computer Vision

2016 ◽  
pp. 1-44
Author(s):  
Vincent Ricordel ◽  
Junle Wang ◽  
Matthieu Perreira Da Silva ◽  
Patrick Le Callet
Keyword(s):  
Computer Vision ◽  
Visual Perception ◽  
Visual Attention ◽  
Visual System ◽  
Computational Modeling ◽  
Depth Perception ◽  
Human Visual System ◽  
Visual Importance ◽  
2D And 3D ◽  
The Impact

Visual attention is one of the most important mechanisms deployed in the human visual system (HVS) to reduce the amount of information that our brain needs to process. An increasing amount of efforts has been dedicated to the study of visual attention, and this chapter proposes to clarify the advances achieved in computational modeling of visual attention. First the concepts of visual attention, including the links between visual salience and visual importance, are detailed. The main characteristics of the HVS involved in the process of visual perception are also explained. Next we focus on eye-tracking, because of its role in the evaluation of the performance of the models. A complete state of the art in computational modeling of visual attention is then presented. The research works that extend some visual attention models to 3D by taking into account of the impact of depth perception are finally explained and compared.

Applications of Visual Attention in Image Processing, Computer Vision, and Graphics

2016 ◽  
pp. 45-68
Author(s):  
Rajarshi Pal
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Visual Attention ◽  
Visual Information ◽  
Computational Models ◽  
External Environment ◽  
Real Life ◽  
Visual Content ◽  
Selective Visual Attention ◽  
Speed Up

Selective visual attention is an amazing capability of primate visual system to restrict the focus to few interesting objects (or portions) in a scene. Thus, primates are able to pay attention to the required visual content amidst myriads of other visual information. It enables them to interact with the external environment in real time through reduction of computational load in their brain. This inspires image and computer vision scientists to derive computational models of visual attention and to use them in varieties of applications in real-life, mainly to speed up the processing through reduction of computational burden which often characterizes image processing and vision tasks. This chapter discusses a wide variety of such applications of visual attention models in image processing, computer vision and graphics.

2D and 3D Visual Attention for Computer Vision

3D Printing ◽  
2017 ◽  
pp. 75-118
Author(s):  
Vincent Ricordel ◽  
Junle Wang ◽  
Matthieu Perreira Da Silva ◽  
Patrick Le Callet
Keyword(s):  
Computer Vision ◽  
Visual Perception ◽  
Visual Attention ◽  
Visual System ◽  
Computational Modeling ◽  
Depth Perception ◽  
Human Visual System ◽  
Visual Importance ◽  
2D And 3D ◽  
The Impact

Visual attention is one of the most important mechanisms deployed in the human visual system (HVS) to reduce the amount of information that our brain needs to process. An increasing amount of efforts has been dedicated to the study of visual attention, and this chapter proposes to clarify the advances achieved in computational modeling of visual attention. First the concepts of visual attention, including the links between visual salience and visual importance, are detailed. The main characteristics of the HVS involved in the process of visual perception are also explained. Next we focus on eye-tracking, because of its role in the evaluation of the performance of the models. A complete state of the art in computational modeling of visual attention is then presented. The research works that extend some visual attention models to 3D by taking into account of the impact of depth perception are finally explained and compared.

A Rapture of the Nerds? A Comparison between Transhumanist Eschatology and Christian Parousia

2019 ◽  
Vol 24 (2) ◽  
pp. 343-367
Author(s):  
Roberto Paura
Keyword(s):  
Human Brain ◽  
Point Theory ◽  
The Body ◽  
Christian Theology ◽  
Human Beings ◽  
Human Species ◽  
The Future ◽  
The Creation ◽  
The Universe

Transhumanism is one of the main “ideologies of the future” that has emerged in recent decades. Its program for the enhancement of the human species during this century pursues the ultimate goal of immortality, through the creation of human brain emulations. Therefore, transhumanism offers its fol- lowers an explicit eschatology, a vision of the ultimate future of our civilization that in some cases coincides with the ultimate future of the universe, as in Frank Tipler’s Omega Point theory. The essay aims to analyze the points of comparison and opposition between transhumanist and Christian eschatologies, in particular considering the “incarnationist” view of Parousia. After an introduction concern- ing the problems posed by new scientific and cosmological theories to traditional Christian eschatology, causing the debate between “incarnationists” and “escha- tologists,” the article analyzes the transhumanist idea of mind-uploading through the possibility of making emulations of the human brain and perfect simulations of the reality we live in. In the last section the problems raised by these theories are analyzed from the point of Christian theology, in particular the proposal of a transhuman species through the emulation of the body and mind of human beings. The possibility of a transhumanist eschatology in line with the incarnationist view of Parousia is refused.

Computing Possible Futures

2019 ◽  
Author(s):  
William B. Rouse
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Computational Modeling ◽  
Mental Model ◽  
Data Analytics ◽  
Computational Models ◽  
Senior Managers ◽  
Interactive Visualizations ◽  
Use Of Models ◽  
Better Than

This book discusses the use of models and interactive visualizations to explore designs of systems and policies in determining whether such designs would be effective. Executives and senior managers are very interested in what “data analytics” can do for them and, quite recently, what the prospects are for artificial intelligence and machine learning. They want to understand and then invest wisely. They are reasonably skeptical, having experienced overselling and under-delivery. They ask about reasonable and realistic expectations. Their concern is with the futurity of decisions they are currently entertaining. They cannot fully address this concern empirically. Thus, they need some way to make predictions. The problem is that one rarely can predict exactly what will happen, only what might happen. To overcome this limitation, executives can be provided predictions of possible futures and the conditions under which each scenario is likely to emerge. Models can help them to understand these possible futures. Most executives find such candor refreshing, perhaps even liberating. Their job becomes one of imagining and designing a portfolio of possible futures, assisted by interactive computational models. Understanding and managing uncertainty is central to their job. Indeed, doing this better than competitors is a hallmark of success. This book is intended to help them understand what fundamentally needs to be done, why it needs to be done, and how to do it. The hope is that readers will discuss this book and develop a “shared mental model” of computational modeling in the process, which will greatly enhance their chances of success.

scholarly journals Long- and Short-Term Conductance Control of Artificial Polymer Wire Synapses

Polymers ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 312
Author(s):  
Naruki Hagiwara ◽  
Shoma Sekizaki ◽  
Yuji Kuwahara ◽  
Tetsuya Asai ◽  
Megumi Akai-Kasaya
Keyword(s):  
Artificial Intelligence ◽  
Human Brain ◽  
High Speed ◽  
Conductive Polymer ◽  
Long Term Potentiation ◽  
Short Term ◽  
Synaptic Functions ◽  
Term Potentiation ◽  
Artificial Brain ◽  
External Stimuli

Networks in the human brain are extremely complex and sophisticated. The abstract model of the human brain has been used in software development, specifically in artificial intelligence. Despite the remarkable outcomes achieved using artificial intelligence, the approach consumes a huge amount of computational resources. A possible solution to this issue is the development of processing circuits that physically resemble an artificial brain, which can offer low-energy loss and high-speed processing. This study demonstrated the synaptic functions of conductive polymer wires linking arbitrary electrodes in solution. By controlling the conductance of the wires, synaptic functions such as long-term potentiation and short-term plasticity were achieved, which are similar to the manner in which a synapse changes the strength of its connections. This novel organic artificial synapse can be used to construct information-processing circuits by wiring from scratch and learning efficiently in response to external stimuli.

scholarly journals Glimpse: A Gaze-Based Measure of Temporal Salience

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3099
Author(s):  
V. Javier Traver ◽  
Judith Zorío ◽  
Luis A. Leiva
Keyword(s):  
Visual Attention ◽  
Computational Models ◽  
Temporal Evolution ◽  
Temporal Consistency ◽  
Visual Salience ◽  
Temporal Dimension ◽  
Spatial Perspective ◽  
Spatio Temporal ◽  
Scoring Algorithms ◽  
Over Time

Temporal salience considers how visual attention varies over time. Although visual salience has been widely studied from a spatial perspective, its temporal dimension has been mostly ignored, despite arguably being of utmost importance to understand the temporal evolution of attention on dynamic contents. To address this gap, we proposed Glimpse, a novel measure to compute temporal salience based on the observer-spatio-temporal consistency of raw gaze data. The measure is conceptually simple, training free, and provides a semantically meaningful quantification of visual attention over time. As an extension, we explored scoring algorithms to estimate temporal salience from spatial salience maps predicted with existing computational models. However, these approaches generally fall short when compared with our proposed gaze-based measure. Glimpse could serve as the basis for several downstream tasks such as segmentation or summarization of videos. Glimpse’s software and data are publicly available.

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