The spatial region of integration for visual symmetry detection

Dakin, Steven C.; Herbert, Andrew M.

doi:10.1098/rspb.1998.0344

Cited by 85 publications

(71 citation statements)

References 22 publications

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“…These results implicate higher-level mechanisms in the detection of facial symmetry and rule out purely low-level mechanisms (although these could contribute as well). The sensitivity of facial symmetry detection to spatial scale corroborates this conclusion, because low-level symmetry detection mechanisms are insensitive to changes in spatial scale (Dakin & Hess 1997;Dakin & Herbert 1998;Rainville & Kingdom 2002).…”

Section: Methodssupporting

confidence: 67%

“…In addition to normal faces, we showed low, medium and high bandpass filtered faces, made using three separate bandpass filters of 1.4 octave width at half-height, with peak frequencies at 1.4, 10 and 60 cycles per face. Sensitivity to spatial scale would rule out purely low-level mechanisms, which are insensitive to spatial scale (Dakin & Hess 1997;Dakin & Herbert 1998;Rainville & Kingdom 2002). (ii) Stimuli Low, medium and high bandpass filtered versions were made for each of the faces used in experiment 1 (figure 3a).…”

Section: Methodsmentioning

confidence: 99%

“…In experiment 3, we compared symmetry detection for normal and bandpass filtered faces containing either low (1.4 cycles per face), middle (10 cycles per face) or high (60 cycles per face) spatial frequencies. Low-level symmetry detection mechanisms show little effect of spatial scale (Dakin & Hess 1997;Dakin & Herbert 1998;Rainville & Kingdom 2002). Therefore, any sensitivity to spatial scale would implicate higher-level mechanisms.…”

Section: Introductionmentioning

confidence: 99%

“…Alternatively, if facial symmetry is detected by generic, low-level mechanisms (e.g. Dakin & Herbert 1998;Scognomilio et al 2003), then there should be no advantage for normal, upright faces.…”

Section: Introductionmentioning

confidence: 99%

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Higher-level mechanisms detect facial symmetry

et al. 2005

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The role of symmetry detection in early visual processing and the sensitivity of biological visual systems to symmetry across a wide range of organisms suggest that symmetry can be detected by low-level visual mechanisms. However, computational and functional considerations suggest that higher-level mechanisms may also play a role in facial symmetry detection. We tested this hypothesis by examining whether symmetry detection is better for faces than comparable patterns, which share low-level properties with faces. Symmetry detection was better for upright faces than for inverted faces (experiment 1) and contrastreversed faces (experiment 2), implicating high-level mechanisms in facial symmetry detection. In addition, facial symmetry detection was sensitive to spatial scale, unlike low-level symmetry detection mechanisms (experiment 3), and showed greater sensitivity to a 458 deviation from vertical than is found for other aspects of face perception (experiment 4). These results implicate specialized, higher-level mechanisms in the detection of facial symmetry. This specialization may reflect perceptual learning resulting from extensive experience detecting symmetry in faces or evolutionary selection pressures associated with the important role of facial symmetry in mate choice and 'mind-reading' or both.

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Section: Methodssupporting

confidence: 67%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Higher-level mechanisms detect facial symmetry

et al. 2005

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“…It has also been suggested that symmetry detection may be a special case of orientation processing [6,7] in that once the axis of symmetry is determined, both this information and orientation information acquired from other contour classes converge at a common neural site where orientation information is processed irrespective of source. Further, Dakin and others have suggested that symmetry detection shares features with mechanisms responsible for processing orientation [8][9][10]. Symmetry is also thought to be important from an evolutionary perspective.…”

Section: Introductionmentioning

confidence: 99%

Symmetry Detection in Visual Impairment: Behavioral Evidence and Neural Correlates

et al. 2014

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Bilateral symmetry is an extremely salient feature for the human visual system. An interesting issue is whether the perceptual salience of symmetry is rooted in normal visual development. In this review, we discuss empirical work on visual and tactile symmetry detection in normally sighted and visually impaired individuals. On the one hand, available evidence suggests that efficient visual symmetry detection may need normal binocular vision development. On the other hand, converging evidence suggests that symmetry can develop as a principle of haptic perceptual organization in individuals lacking visual experience. Certain features of visual symmetry detection, however, such as the higher salience of the patterns containing a vertical axis of symmetry, do not systematically apply to the haptic modality. The neural correlates (revealed with OPEN ACCESS Symmetry 2014, 6 428 neuroimaging) associated with visual and haptic symmetry detection are also discussed.

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Density‐free test for symmetry verification in images

Migalska

2018

Appl Stoch Models Bus & Ind

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Presence of symmetry is utilized in multiple machine vision systems to help achieve their goals. In numerous scenarios, this goal is to verify that certain symmetry is indeed exhibited by an image. However, we find that there is a shortage of methods for symmetry verification that would be capable of asserting an arbitrary reflectional or rotational symmetry. Using symmetry detectors to merely perform symmetry verification is improvident and not justified. We thus propose a novel statistical test for symmetry verification that fulfills the requirement of versatility. The proposed test is based on the principle that if an image is invariant to some hypothesized transformation, then an averaged image, obtained by averaging pixel intensities of an input image and of its transformed copy, looks exactly the same as an input image. Adopting the viewpoint that images are visual messages that convey some information allows us to expect that the amount of information in both images is the same. On the contrary, an incorrectly chosen transformation shall result in the information content being different. Based on this equality, we construct the test statistic and show that, when samples are large, the test statistic is asymptotically normally distributed. Finally, to verify the validity of the proposed principle and the performance of the method, a meticulous experimental study was performed on a large set of images. The results of this study confirmed the postulated ability of the method to verify an arbitrary symmetry and are demonstrated with several examples.

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The spatial region of integration for visual symmetry detection

Cited by 85 publications

References 22 publications

Higher-level mechanisms detect facial symmetry

Higher-level mechanisms detect facial symmetry

Symmetry Detection in Visual Impairment: Behavioral Evidence and Neural Correlates

Density‐free test for symmetry verification in images

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