Eye Movements Show Optimal Average Anticipation with Natural Dynamic Scenes

Vig, Eleonora; Dörr, Michael; Martinetz, Thomas; Barth, Erhardt

doi:10.1007/s12559-010-9061-4

Cited by 18 publications

(21 citation statements)

References 32 publications

(39 reference statements)

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“…no offset needs to be taken into account. 20 The energy profiles of the attended and non-attended locations are computed on an anisotropic Laplacian pyramid decomposition of the videos (the pyramid having S = 5 spatial and T = 4 temporal levels), in a 5 by 5 degree spatial neighbourhood on all scales (which corresponds to 128×128 pixels on the highest spatial levels). In the periphery, the highest spatial and temporal frequency information is known to contribute little to attentional selection, since high spatio-temporal frequency is discernible only near the fovea.…”

Section: Learning the Contrast Modification Rulesmentioning

confidence: 99%

Learned saliency transformations for gaze guidance

2011

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The saliency of an image or video region indicates how likely it is that the viewer of the image or video fixates that region due to its conspicuity. An intriguing question is how we can change the video region to make it more or less salient. Here, we address this problem by using a machine learning framework to learn from a large set of eye movements collected on real-world dynamic scenes how to alter the saliency level of the video locally. We derive saliency transformation rules by performing spatio-temporal contrast manipulations (on a spatio-temporal Laplacian pyramid) on the particular video region. Our goal is to improve visual communication by designing gaze-contingent interactive displays that change, in real time, the saliency distribution of the scene.

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Section: Learning the Contrast Modification Rulesmentioning

confidence: 99%

Learned saliency transformations for gaze guidance

2011

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“…Attentional synchrony decreases over repeated presentation of the same videos (Dorr et al, 2010) and over prolonged presentation of the same dynamic scene (Carmi & Itti, 2006a;Dorr et al, 2010;Mital et al, 2011), suggesting that increasing familiarity and memory for scene content leads to divergent gaze behavior. Recent evidence of optimal anticipation of moments of high salience in dynamic scenes suggests that viewers formulate and act on predictions about the future of visual events (Vig, Dorr, Martinetz, & Barth, 2011).…”

Section: Introductionmentioning

confidence: 99%

Attentional synchrony and the influence of viewing task on gaze behavior in static and dynamic scenes

2013

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Does viewing task influence gaze during dynamic scene viewing? Research into the factors influencing gaze allocation during free viewing of dynamic scenes has reported that the gaze of multiple viewers clusters around points of high motion (attentional synchrony), suggesting that gaze may be primarily under exogenous control. However, the influence of viewing task on gaze behavior in static scenes and during real-world interaction has been widely demonstrated. To dissociate exogenous from endogenous factors during dynamic scene viewing we tracked participants' eye movements while they (a) freely watched unedited videos of real-world scenes (free viewing) or (b) quickly identified where the video was filmed (spot-the-location). Static scenes were also presented as controls for scene dynamics. Free viewing of dynamic scenes showed greater attentional synchrony, longer fixations, and more gaze to people and areas of high flicker compared with static scenes. These differences were minimized by the viewing task. In comparison with the free viewing of dynamic scenes, during the spot-the-location task fixation durations were shorter, saccade amplitudes were longer, and gaze exhibited less attentional synchrony and was biased away from areas of flicker and people. These results suggest that the viewing task can have a significant influence on gaze during a dynamic scene but that endogenous control is slow to kick in as initial saccades default toward the screen center, areas of high motion and people before shifting to task-relevant features. This default-like viewing behavior returns after the viewing task is completed, confirming that gaze behavior is more predictable during free viewing of dynamic than static scenes but that this may be due to natural correlation between regions of interest (e.g., people) and motion.

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“…However, we have previously shown that the typical reaction time is stimulus dependent, and in natural scenes this average lag is near zero (i.e. no offset needs to be considered) due to the highly predictive nature of salient real-world events [45].…”

Section: B Data Set Labellingmentioning

confidence: 96%

Intrinsic Dimensionality Predicts the Saliency of Natural Dynamic Scenes

Vig

Dörr

Martinetz

et al. 2012

IEEE Trans. Pattern Anal. Mach. Intell.

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Abstract-Since visual attention-based computer vision applications have gained popularity, ever more complex, biologicallyinspired models seem to be needed to predict salient locations (or interest points) in naturalistic scenes. In this paper, we explore how far one can go in predicting eye movements by using only basic signal processing, such as image representations derived from efficient coding principles, and machine learning. To this end, we gradually increase the complexity of a model from simple single-scale saliency maps computed on grayscale videos to spatio-temporal multiscale and multispectral representations. Using a large collection of eye movements on high-resolution videos, supervised learning techniques fine-tune the free parameters whose addition is inevitable with increasing complexity. The proposed model, although very simple, demonstrates significant improvement in predicting salient locations in naturalistic videos over four selected baseline models and two distinct data labelling scenarios.Index Terms-Computational models of vision, video analysis, computer vision, spatio-temporal saliency, eye movement prediction, intrinsic dimension, visual attention, interest point detection.

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Eye Movements Show Optimal Average Anticipation with Natural Dynamic Scenes

Cited by 18 publications

References 32 publications

Learned saliency transformations for gaze guidance

Learned saliency transformations for gaze guidance

Attentional synchrony and the influence of viewing task on gaze behavior in static and dynamic scenes

Intrinsic Dimensionality Predicts the Saliency of Natural Dynamic Scenes

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