Visual search for a target against a 1/fβ continuous textured background

Clarke, Alasdair D. F.; Green, Patrick Richey; Chantler, Mike J.; Emrith, Khemraj

doi:10.1016/j.visres.2008.06.019

Cited by 13 publications

(16 citation statements)

References 40 publications

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“…(2006a) from situations in which there are no distractor objects and observers search for a discontinuity in a textured background (Clarke, Green, Chantler, & Emrith, 2008).…”

Section: University Of Illinois At Urbana-champaign Champaign Illinoismentioning

confidence: 99%

Training and transfer of training in the search for camouflaged targets

Boot

Neider

Kramer

2009

Attention, Perception, & Psychophysics

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950How do humans go about finding a search target when, through design (e.g., a military tank in the desert) or evolution (e.g., a lion in a savanna), it blends in with the background? Visual search has been extensively studied and a great deal has been learned about the guidance of attention during search, including how attention is controlled by bottom-up factors (e.g., Proulx, 2007;Theeuwes, 1992Theeuwes, , 2004Yantis & Jonides, 1984), top-down guidance (e.g., Bacon & Egeth, 1994;Chen & Zelinsky, 2006;Folk, Remington, & Johnston, 1992), and various memory mechanisms (e.g., Boot, McCarley, Kramer, & Peterson, 2004;Brockmole & Henderson, 2006;Chun & Jiang, 1998;Klein & MacInnes, 1999;Neider & Zelinsky, 2006b). Much of what we know about search has been derived from experiments in which target and distractors are easy to segment from the background (see Wolfe, 1994, for a review). However, many real-world search situations contain items that need to be segmented from the background to some degree to allow for efficient visual search. This is especially true in the search for camouflaged targets.As outlined by Wolfe, Oliva, Horowitz, Butcher, and Bompas (2002), the visual system faces a number of potentially important challenges when target and distractor objects are placed within the context of a visually complex background. These problems include more difficult preattentive segmentation of objects from the background and the creation of "illusory objects" by noise in the background. Additionally, if segmentation is imperfect, attentional selection may be more difficult and time consuming. Imperfect segmentation might also result in noisy object representations being passed along to higher visual stages, such as those responsible for object recognition. Through a series of cleverly designed search experiments, Wolfe and colleagues found that the main cost of background complexity was associated with delayed information accrual affecting later target recognition stages. However, in extreme cases of item-background similarity, item-by-item selection was slowed. Importantly to the present context, background complexity had a negative impact on search performance in all experiments, highlighting the difficulties associated with complex searches in which the search items are not easily distinguished from the background.To follow up on the findings of Wolfe et al. (2002), Neider and Zelinsky (2006a) directly examined the effect of target-background similarity. Neider and Zelinsky (2006b) had participants search through displays containing many children's toys and asked them to indicate whether or not a specific toy was present. Camouflage displays were created by taking a square-shaped portion of the target image and repeating it again and again to create a background. Search items were then superimposed on this background (Figure 1). This created a search task in which the features How do observers become proficient at finding camouflaged targets? Does gaining proficiency in locating a target in one camouflage search...

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“…(2006a) from situations in which there are no distractor objects and observers search for a discontinuity in a textured background (Clarke, Green, Chantler, & Emrith, 2008).…”

Section: University Of Illinois At Urbana-champaign Champaign Illinoismentioning

confidence: 99%

Training and transfer of training in the search for camouflaged targets

Boot

Neider

Kramer

2009

Attention, Perception, & Psychophysics

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“…Previous work on modeling visual search can be divided into two classes: search among sets of search items; and search in more naturalistic, continuous stimuli (typically photographs). The search task we consider in this study is that of a single target on a textured surface (Clarke, Green, Chantler, & Emrith, 2008). These stimuli have several advantageous properties: they offer a more naturalistic search task than sets of abstract geometric items; yet they are also fully parameterized and created pseudo-randomly so we can create many trials of equivalent difficulty, something that is difficult to do with photographic stimuli.…”

Section: Introductionmentioning

confidence: 99%

Modeling visual search on a rough surface

2009

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The LNL (linear, non-linear, linear) model has previously been successfully applied to the problem of texture segmentation. In this study we investigate the extent to which a simple LNL model can simulate human performance in a search task involving a target on a textured surface. Two different classes of surface are considered: 1/f(beta)-noise and near-regular textures. We find that in both cases the search performance of the model does not differ significantly from that of people, over a wide range of task difficulties.

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“…al. [Clarke et al, 2008] [Padilla et al, 2008] [Clarke et al, 2009] showed that the higher the level of background texture noise of a scene, the higher the level of difficulty of the search task. They represented the background surface as a height map by parameterizing an isotropic and random-phase noise 1/f β (being "β" the frequency roll-off magnitude factor of the inverse discrete Fourier transform of the height map and "σ RM S " the deviation of the roughness noise height).…”

Section: # Of Stimulimentioning

confidence: 99%

Psychophysical evaluation of individual low-level feature influences on visual attention

et al. 2019

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In this study we provide the analysis of eye movement behavior elicited by low-level feature distinctiveness with a dataset of synthetically-generated image patterns. Design of visual stimuli was inspired by the ones used in previous psychophysical experiments, namely in free-viewing and visual searching tasks, to provide a total of 15 types of stimuli, divided according to the task and feature to be analyzed. Our interest is to analyze the influences of low-level feature contrast between a salient region and the rest of distractors, providing fixation localization characteristics and reaction time of landing inside the salient region. Eye-tracking data was collected from 34 participants during the viewing of a 230 images dataset. Results show that saliency is predominantly and distinctively influenced by: 1. feature type, 2. feature contrast, 3. temporality of fixations, 4. task difficulty and 5. center bias. This experimentation proposes a new psychophysical basis for saliency model evaluation using synthetic images.

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Visual search for a target against a 1/fβ continuous textured background

Cited by 13 publications

References 40 publications

Training and transfer of training in the search for camouflaged targets

Training and transfer of training in the search for camouflaged targets

Modeling visual search on a rough surface

Psychophysical evaluation of individual low-level feature influences on visual attention

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