Neither here nor there: localizing conflicting visual attributes

McGraw, Paul V.; Whitaker, David; Badcock, David R.; Skillen, Jennifer

doi:10.1167/3.4.2

Cited by 24 publications

(13 citation statements)

References 46 publications

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“…Previous work on edge localization reveals that luminance, texture and color cues are all combined to determine the location of edges (McGraw et al, 2003;Rivest & Cavanagh, 1996), and similarly we find that in our task these multiple sources of information are combined as well. However, in our task we did not consider quantitative models for how disparate cues may be combined optimally, as has been done in a variety of perceptual tasks (Knill & Saunders, 2003;Landy & Kojima, 2001).…”

Section: Comparison With Previous Worksupporting

confidence: 73%

“…One useful set of two-dimensional cues for inferring three-dimensional scene organization are the boundaries and junctions formed by the occlusions of distinct surfaces (Guzman, 1969;Nakayama, He, & Shimojo, 1995;Todd, 2004), as illustrated in Figure 1. In natural images, occlusion boundaries are defined by multiple cues, including local texture, color, and luminance differences, all of which are integrated perceptually (McGraw, Whitaker, Badcock, & Skillen, 2003;Rivest & Cavanagh, 1996). Although numerous machine vision studies have developed algorithms for detecting occlusions and junctions in natural images (Hoiem, Efros, & Hebert, 2011;Konishi, Yuille, Coughlin, & Zhu, 2003;Martin, Fowlkes, & Malik, 2004;Perona, 1992), relatively little work in visual psychophysics has directly studied natural occlusion detection (McDermott, 2004) or used natural occlusions in perceptual tasks (Fowlkes, Martin, & Malik, 2007).…”

Section: Introductionmentioning

confidence: 99%

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Detecting natural occlusion boundaries using local cues

2012

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Occlusion boundaries and junctions provide important cues for inferring three-dimensional scene organization from two-dimensional images. Although several investigators in machine vision have developed algorithms for detecting occlusions and other edges in natural images, relatively few psychophysics or neurophysiology studies have investigated what features are used by the visual system to detect natural occlusions. In this study, we addressed this question using a psychophysical experiment where subjects discriminated image patches containing occlusions from patches containing surfaces. Image patches were drawn from a novel occlusion database containing labeled occlusion boundaries and textured surfaces in a variety of natural scenes. Consistent with related previous work, we found that relatively large image patches were needed to attain reliable performance, suggesting that human subjects integrate complex information over a large spatial region to detect natural occlusions. By defining machine observers using a set of previously studied features measured from natural occlusions and surfaces, we demonstrate that simple features defined at the spatial scale of the image patch are insufficient to account for human performance in the task. To define machine observers using a more biologically plausible multiscale feature set, we trained standard linear and neural network classifiers on the rectified outputs of a Gabor filter bank applied to the image patches. We found that simple linear classifiers could not match human performance, while a neural network classifier combining filter information across location and spatial scale compared well. These results demonstrate the importance of combining a variety of cues defined at multiple spatial scales for detecting natural occlusions.

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Section: Comparison With Previous Worksupporting

confidence: 73%

Section: Introductionmentioning

confidence: 99%

Detecting natural occlusion boundaries using local cues

2012

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“…Rather, it is more plausible that flexible functional networks would form among the areas required to play a role in the completion of the task at hand. The characteristics of the behavioral performance would then be dependent on the representational idiosyncrasies of those areas involved (McGraw et al 2003). …”

Section: Resultsmentioning

confidence: 99%

Perception, Action, and Roelofs Effect: A Mere Illusion of Dissociation

Dassonville

Bala

2004

PLoS Biol

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A prominent and influential hypothesis of vision suggests the existence of two separate visual systems within the brain, one creating our perception of the world and another guiding our actions within it. The induced Roelofs effect has been described as providing strong evidence for this perception/action dissociation: When a small visual target is surrounded by a large frame positioned so that the frame's center is offset from the observer's midline, the perceived location of the target is shifted in the direction opposite the frame's offset. In spite of this perceptual mislocalization, however, the observer can accurately guide movements to the target location. Thus, perception is prone to the illusion while actions seem immune. Here we demonstrate that the Roelofs illusion is caused by a frame-induced transient distortion of the observer's apparent midline. We further demonstrate that actions guided to targets within this same distorted egocentric reference frame are fully expected to be accurate, since the errors of target localization will exactly cancel the errors of motor guidance. These findings provide a mechanistic explanation for the various perceptual and motor effects of the induced Roelofs illusion without requiring the existence of separate neural systems for perception and action. Given this, the behavioral dissociation that accompanies the Roelofs effect cannot be considered evidence of a dissociation of perception and action. This indicates a general need to re-evaluate the broad class of evidence purported to support this hypothesized dissociation.

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“…McGraw et al 32 used a combination of a smooth luminance blob and a second-order texture blob (black and white dots windowed by a smooth blob) in a Vernier task. Different, asymmetric blobs were used for the two cues, so that the perceived location of the luminance blob alone was shifted relative to that for the second-order texture blob.…”

Section: Localizationmentioning

confidence: 99%

Ideal cue combination for localizing texture-defined edges

2001

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Many visual tasks can be carried out by using several sources of information. The most accurate estimates of scene properties require the observer to utilize all available information and to combine the information sources in an optimal manner. Two experiments are described that required the observers to judge the relative locations of two texture-defined edges (a vernier task). The edges were signaled by a change across the edge of two texture properties [either frequency and orientation (Experiment 1) or contrast and orientation (Experiment 2)]. The reliability of each cue was controlled by varying the distance over which the change (in frequency, orientation, or contrast) occurred-a kind of "texture blur." In some conditions, the position of the edge signaled by one cue was shifted relative to the other ("perturbation analysis"). An ideal-observer model, previously used in studies of depth perception and color constancy, was fitted to the data. Although the fit can be rejected relative to some more elaborate models, especially given the large quantity of data, this model does account for most trends in the data. A second, suboptimal model that switches between the available cues from trial to trial does a poor job of accounting for the data.

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Neither here nor there: localizing conflicting visual attributes

Cited by 24 publications

References 46 publications

Detecting natural occlusion boundaries using local cues

Detecting natural occlusion boundaries using local cues

Perception, Action, and Roelofs Effect: A Mere Illusion of Dissociation

Ideal cue combination for localizing texture-defined edges

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