Integrating Inhomogeneous Processing and Proto-object Formation in a Computational Model of Visual Attention

Wischnewski, Marco; Steil, Jochen J.; Kehrer, Lothar; Schneider, Werner X.

doi:10.1007/978-3-642-10403-9_10

Cited by 11 publications

(20 citation statements)

References 18 publications

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“…The weight determines the degree of priority of these objects and their features in perceptual processing. Our model adds the assumption that the proto-object with the highest weight will be the target of the next saccade [10,69]. Following TVA, attentional weights depend on bottom-up influences such as the sensory evidence for visual features and on topdown influences such as the current task [7].…”

Section: Discussionmentioning

confidence: 99%

“…Figure 2 illustrates the corresponding part of the model, a more detailed description has been published by Wischnewski et. al [69].…”

Section: Static Features and Proto-objectsmentioning

confidence: 97%

“…This fusion effect in the periphery simulates the so-called ''global effect'' [20] in eye movement control: saccadic eye movements to two nearby objects in the periphery tend to land on the center of gravity of these objects. In our model, this corresponds to the landing of the gaze on a single proto-object, which is formed from the homogeneous answer of the low-resolution peripheral filter that covers both objects (see details in [69]). In human vision, the ''global effect'' appears only under time pressure.…”

Section: Efficient Processing and The ''Global Effect''mentioning

confidence: 99%

“…To cover the relevant frequency range, we use eight layers of these maps, each with a different parameter setting (see [69] for details). In total, we obtain 64 feature maps.…”

Section: Inhomogeneous Retinal/v1 Feature Processingmentioning

confidence: 99%

“…We add the assumption that weights determine also where-to-look-next. The proto-object with the highest weight will be the target of the next saccade [10,69]. Attentional weights depend on bottom-up influences such as the sensory evidence for visual features and on top-down influences such as the current task.…”

Section: Introductionmentioning

confidence: 99%

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Where to Look Next? Combining Static and Dynamic Proto-objects in a TVA-based Model of Visual Attention

et al. 2010

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To decide ''Where to look next ?'' is a central function of the attention system of humans, animals and robots. Control of attention depends on three factors, that is, low-level static and dynamic visual features of the environment (bottom-up), medium-level visual features of proto-objects and the task (top-down). We present a novel integrated computational model that includes all these factors in a coherent architecture based on findings and constraints from the primate visual system. The model combines spatially inhomogeneous processing of static features, spatio-temporal motion features and task-dependent priority control in the form of the first computational implementation of saliency computation as specified by the ''Theory of Visual Attention'' (TVA,[7]). Importantly, static and dynamic processing streams are fused at the level of visual proto-objects, that is, ellipsoidal visual units that have the additional medium-level features of position, size, shape and orientation of the principal axis. Proto-objects serve as input to the TVA process that combines top-down and bottom-up information for computing attentional priorities so that relatively complex search tasks can be implemented. To this end, separately computed static and dynamic proto-objects are filtered and subsequently merged into one combined map of proto-objects. For each proto-object, attentional priorities in the form of attentional weights are computed according to TVA. The target of the next saccade is the center of gravity of the proto-object with the highest weight according to the task. We illustrate the approach by applying it to several real world image sequences and show that it is robust to parameter variations.

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Section: Discussionmentioning

confidence: 99%

“…Figure 2 illustrates the corresponding part of the model, a more detailed description has been published by Wischnewski et. al [69].…”

Section: Static Features and Proto-objectsmentioning

confidence: 97%

Section: Efficient Processing and The ''Global Effect''mentioning

confidence: 99%

“…To cover the relevant frequency range, we use eight layers of these maps, each with a different parameter setting (see [69] for details). In total, we obtain 64 feature maps.…”

Section: Inhomogeneous Retinal/v1 Feature Processingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Where to Look Next? Combining Static and Dynamic Proto-objects in a TVA-based Model of Visual Attention

et al. 2010

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Task relevance predicts gaze in videos of real moving scenes

et al. 2011

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Low-level stimulus salience and task relevance together determine the human fixation priority assigned to scene locations (Fecteau and Munoz in Trends Cogn Sci 10(8):382-390, 2006). However, surprisingly little is known about the contribution of task relevance to eye movements during real-world visual search where stimuli are in constant motion and where the 'target' for the visual search is abstract and semantic in nature. Here, we investigate this issue when participants continuously search an array of four closed-circuit television (CCTV) screens for suspicious events. We recorded eye movements whilst participants watched real CCTV footage and moved a joystick to continuously indicate perceived suspiciousness. We find that when multiple areas of a display compete for attention, gaze is allocated according to relative levels of reported suspiciousness. Furthermore, this measure of task relevance accounted for twice the amount of variance in gaze likelihood as the amount of low-level visual changes over time in the video stimuli.

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Clutter perception is invariant to image size

Zelinsky

2015

Vision Research

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Two experiments evaluated the effect of retinal image size on the proto-object model of visual clutter perception. Experiment 1 had 20 participants order 90 small images of random-category real-world scenes from least to most cluttered. Aggregating these individual rankings into a single median clutter ranking and comparing it to a previously reported clutter ranking of larger versions of the identical scenes yielded a Spearman’s ρ = .953 (p < .001), suggesting that relative clutter perception is largely invariant to image size. We then applied the proto-object model of clutter perception to these smaller images and obtained a clutter estimate for each. Correlating these estimates with the median behavioral ranking yielded a Spearman’s ρ = .852 (p < .001), which we showed in a comparative analysis to be better than six other methods of estimating clutter. Experiment 2 intermixed large and small versions of the Experiment 1 scenes and had participants (n=18) again rank them for clutter. We found that median clutter rankings of these size-intermixed images were essentially the same as the small and large median rankings from Experiment 1, suggesting size invariance in absolute clutter perception. Moreover, the proto-object model again successfully captured this result. We conclude that both relative and absolute clutter perception is invariant to retinal image size. We further speculate that clutter perception is mediated by proto-objects—a preattentive level of visual representation between features and objects—and that using the proto-object model we may be able to glimpse into this pre-attentive world.

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Integrating Inhomogeneous Processing and Proto-object Formation in a Computational Model of Visual Attention

Cited by 11 publications

References 18 publications

Where to Look Next? Combining Static and Dynamic Proto-objects in a TVA-based Model of Visual Attention

Where to Look Next? Combining Static and Dynamic Proto-objects in a TVA-based Model of Visual Attention

Task relevance predicts gaze in videos of real moving scenes

Clutter perception is invariant to image size

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