Visual Attention Saccadic Models Learn to Emulate Gaze Patterns From Childhood to Adulthood

Meur, Olivier Le; Coutrot, Antoine; Li, Zhi; Rama, Paolo; Roch, Adrien Le; Helo, Andrea

doi:10.1109/tip.2017.2722238

Cited by 41 publications

(21 citation statements)

References 66 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…For instance, such a model can predict the probability of a sequence (here, a scanpath) belonging to a certain experimental group (e.g., Voisin, Yoon, Tourassi, Morin-Ducote, & Hudson, 2013). It can be used to generate scanpaths (e.g., saccadic models: Le Meur & Coutrot, 2016a;Le Meur et al, 2017a), and it can also prove useful in studying bottomup and top-down visual attention processes (Rai, Le Callet, & Cheung, 2016;Coutrot et al, 2018). In this section we take on the task of classifying the types (three classes; Model Type 1), types and sizes (nine classes; Type 2) and sizes of scotomas (four classes;…”

Section: Classifier Modelsmentioning

confidence: 99%

Predicting artificial visual field losses: A gaze-based inference study

et al. 2019

View full text Add to dashboard Cite

Visual field defects are a worldwide concern, and the proportion of the population experiencing vision loss is ever increasing. Macular degeneration and glaucoma are among the four leading causes of permanent vision loss. Identifying and characterizing visual field losses from gaze alone could prove crucial in the future for screening tests, rehabilitation therapies, and monitoring. In this experiment, 54 participants took part in a free-viewing task of visual scenes while experiencing artificial scotomas (central and peripheral) of varying radii in a gaze-contingent paradigm. We studied the importance of a set of gaze features as predictors to best differentiate between artificial scotoma conditions. Linear mixed models were utilized to measure differences between scotoma conditions. Correlation and factorial analyses revealed redundancies in our data. Finally, hidden Markov models and recurrent neural networks were implemented as classifiers in order to measure the predictive usefulness of gaze features. The results show separate saccade direction biases depending on scotoma type. We demonstrate that the saccade relative angle, amplitude, and peak velocity of saccades are the best features on the basis of which to distinguish between artificial scotomas in a free-viewing task. Finally, we discuss the usefulness of our protocol and analyses as a gaze-feature identifier tool that discriminates between artificial scotomas of different types and sizes.

show abstract

Section: Classifier Modelsmentioning

confidence: 99%

Predicting artificial visual field losses: A gaze-based inference study

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Depending on the task observers have to perform, the gaze deployment is significantly altered. Beyond the task at hand, top-down influences are also related to observers' experience as well as their own characteristics such as age [4,5] and their cultural experiences [6].…”

Section: Introductionmentioning

confidence: 99%

Can we accurately predict where we look at paintings?

Meur¹,

Pen²,

Cozot³

2020

PLoS ONE

Self Cite

View full text Add to dashboard Cite

The objective of this study is to investigate and to simulate the gaze deployment of observers on paintings. For that purpose, we built a large eye tracking dataset composed of 150 paintings belonging to 5 art movements. We observed that the gaze deployment over the proposed paintings was very similar to the gaze deployment over natural scenes. Therefore, we evaluate existing saliency models and propose a new one which significantly outperforms the most recent deep-based saliency models. Thanks to this new saliency model, we can predict very accurately what are the salient areas of a painting. This opens new avenues for many image-based applications such as animation of paintings or transformation of a still painting into a video clip.

show abstract

“…New methods and approaches are required to detect anomaly in UAV footages and to ease the decision-making. Among them, we believe that computational models of visual attention could be used to simulate operators' behaviors [25]. Eventually, thanks to predictions, operators' workloads can be reduced by eliminating unnecessary footages segments.…”

Section: Related Workmentioning

confidence: 99%

EyeTrackUAV2: A Large-Scale Binocular Eye-Tracking Dataset for UAV Videos

Perrin

Krassanakis

Zhang

et al. 2020

Drones

Self Cite

View full text Add to dashboard Cite

The fast and tremendous evolution of the unmanned aerial vehicle (UAV) imagery gives place to the multiplication of applications in various fields such as military and civilian surveillance, delivery services, and wildlife monitoring. Combining UAV imagery with study of dynamic salience further extends the number of future applications. Indeed, considerations of visual attention open the door to new avenues in a number of scientific fields such as compression, retargeting, and decision-making tools. To conduct saliency studies, we identified the need for new large-scale eye-tracking datasets for visual salience in UAV content. Therefore, we address this need by introducing the dataset EyeTrackUAV2. It consists of the collection of precise binocular gaze information (1000 Hz) over 43 videos (RGB, 30 fps, 1280 × 720 or 720 × 480). Thirty participants observed stimuli under both free viewing and task conditions. Fixations and saccades were then computed with the dispersion-threshold identification (I-DT) algorithm, while gaze density maps were calculated by filtering eye positions with a Gaussian kernel. An analysis of collected gaze positions provides recommendations for visual salience ground-truth generation. It also sheds light upon variations of saliency biases in UAV videos when opposed to conventional content, especially regarding the center bias.

show abstract

Visual Attention Saccadic Models Learn to Emulate Gaze Patterns From Childhood to Adulthood

Cited by 41 publications

References 66 publications

Predicting artificial visual field losses: A gaze-based inference study

Predicting artificial visual field losses: A gaze-based inference study

Can we accurately predict where we look at paintings?

EyeTrackUAV2: A Large-Scale Binocular Eye-Tracking Dataset for UAV Videos

Contact Info

Product

Resources

About