Characterization of Spatial Frequency Channels Underlying Disparity Sensitivity by Factor Analysis of Population Data

Reynaud, Alexandre; Hess, Robert F.

doi:10.3389/fncom.2017.00063

Cited by 18 publications

(21 citation statements)

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“…The pattern of human disparity sensitivity that we observe is well captured by our biologically-motivated model of disparity processing that critically incorporates the log-polar retinocortical transformation. It is generally accepted that our visual system processes disparity PLOS COMPUTATIONAL BIOLOGY along at least two [7][8][9][10][11] or more [12] channels that are selective for depth changes at different disparity spatial scales. These disparity spatial scales in turn may rely on distinct sets of luminance spatial channels [13][14][15][16] (as well as second-order channels [49][50][51]).…”

Section: Plos Computational Biologymentioning

confidence: 99%

“…It has been proposed that the visual system may process disparity at different disparity spatial scales along separate channels [5], analogous to the channels selective for luminance differences at different luminance spatial frequencies [6]. Using a variety of paradigms to investigate both absolute and relative disparity processing, several authors have provided evidence for at least two [7][8][9][10][11] or more [12] disparity spatial channels for disparity processing, which in turn may rely on distinct sets of luminance spatial channels [13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

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Near-optimal combination of disparity across a log-polar scaled visual field

et al. 2020

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The human visual system is foveated: we can see fine spatial details in central vision, whereas resolution is poor in our peripheral visual field, and this loss of resolution follows an approximately logarithmic decrease. Additionally, our brain organizes visual input in polar coordinates. Therefore, the image projection occurring between retina and primary visual cortex can be mathematically described by the log-polar transform. Here, we test and model how this space-variant visual processing affects how we process binocular disparity, a key component of human depth perception. We observe that the fovea preferentially processes disparities at fine spatial scales, whereas the visual periphery is tuned for coarse spatial scales, in line with the naturally occurring distributions of depths and disparities in the real-world. We further show that the visual system integrates disparity information across the visual field, in a near-optimal fashion. We develop a foveated, log-polar model that mimics the processing of depth information in primary visual cortex and that can process disparity directly in the cortical domain representation. This model takes real images as input and recreates the observed topography of human disparity sensitivity. Our findings support the notion that our foveated, binocular visual system has been moulded by the statistics of our visual environment.

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Section: Plos Computational Biologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Near-optimal combination of disparity across a log-polar scaled visual field

et al. 2020

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“…Despite this correlation between IOC sensitivity and disparity sensitivity across a number of different dimensions, the fact that the IOC frequency tuning cutoff is never as high as the disparity tuning cutoff would indicate that these two modalities are influenced by a common mechanism but that disparity processing may require an extra processing level. Interestingly, we recently observed (Reynaud & Hess, 2017 ) that in this spatial frequency range, disparity processing is mediated by two channels: a low and a high spatial frequency one; the low spatial frequency one showing similar tuning as the IOC sensitivity. Therefore, we speculate that this low spatial frequency channel could be common to the two mechanisms whereas the high spatial frequency one could be specific to disparity processing.…”

Section: Discussionmentioning

confidence: 80%

Interocular correlation sensitivity and its relationship with stereopsis

Reynaud

Hess

2018

Journal of Vision

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Stereoscopic vision uses the disparity between the images received by the two eyes to derive three-dimensional estimates. Here, we were interested in providing a measure of the strength of binocular vision alternate to disparity processing. In particular, we wanted to assess the spatial dependence of sensitivity to detect interocular correlation (IOC). Thus we designed dichoptic stimuli composed of bandpass textures whose IOC is sinusoidally modulated at different correlation frequencies and compared sensitivity to these stimuli to that of analogous stimuli modulated in disparity. We observed that the IOC sensitivity is low pass/band pass and increases with stimulus duration and contrast in a similar way to that of disparity sensitivity. IOC sensitivity is only weakly, though significantly, correlated with disparity sensitivity in the population. It could provide an alternate measure of binocular sensitivity.

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“…The pattern of human disparity sensitivity that we observe is well captured by our 136 biologically-motivated model of disparity processing that critically incorporates the 137 log-polar retino-cortical transformation. It is generally accepted that our visual system 138 processes disparity along at least two [2,[4][5][6][7][8][9][10] or more [11][12][13] channels that are 139 selective for depth changes at different spatial scales. A key insight provided by our 140 work is that depth-selective channels emerge directly from the log-polar, retino-cortical 141 transform, since log-polar spatial sampling acts as an "horizontal" multi-scale analysis, 142 i.e.…”

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confidence: 99%

“…• The algorithm presented in [42] did not contain neural noise, which is instead 521 present in the human visual system [28] and was thus incorporated into the 522 current model 523 • In [42] a multi-scale approach was adopted with 11 sub-octave scales in order to 524 recover a large range of disparities (common in computer vision) by using Gabor 525 filters with peak frequency of 0.26 cycles/pixel. In the current model, only 2 scales 526 were employed, since several authors have proposed two spatial frequency channels 527 for disparity processing in humans [4][5][6][7][8][9][10]…”

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confidence: 99%

Near-optimal combination of disparity across a log-polar scaled visual field

Maiello

Chessa

Bex

et al. 2019

Preprint

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AbstractThe human visual system is foveated: we can see fine spatial details in central vision, whereas resolution is poor in our peripheral visual field, and this loss of resolution follows an approximately logarithmic decrease. Additionally, our brain organizes visual input in polar coordinates. Therefore, the image projection occurring between retina and primary visual cortex can be mathematically described by the log-polar transform. Here, we test and model how this space-variant visual processing affects how we process binocular disparity, a key component of human depth perception. We observe that the fovea preferentially processes disparities at fine spatial scales, whereas the visual periphery is tuned for coarse spatial scales, in line with the naturally occurring distributions of depths and disparities in the real-world. We further show that the visual field integrates disparity information across the visual field, in a near-optimal fashion. We develop a foveated, log-polar model that mimics the processing of depth information in primary visual cortex and that can process disparity directly in the cortical domain representation. This model takes real images as input and recreates the observed topography of disparity sensitivity in man. Our findings support the notion that our foveated, binocular visual system has been moulded by the statistics of our visual environment.Author summaryWe investigate how humans perceive depth from binocular disparity at different spatial scales and across different regions of the visual field. We show that small changes in disparity-defined depth are detected best in central vision, whereas peripheral vision best captures the coarser structure of the environment. We also demonstrate that depth information extracted from different regions of the visual field is combined into a unified depth percept. We then construct an image-computable model of disparity processing that takes into account how our brain organizes the visual input at our retinae. The model operates directly in cortical image space, and neatly accounts for human depth perception across the visual field.

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Characterization of Spatial Frequency Channels Underlying Disparity Sensitivity by Factor Analysis of Population Data

Cited by 18 publications

References 47 publications

Near-optimal combination of disparity across a log-polar scaled visual field

Near-optimal combination of disparity across a log-polar scaled visual field

Interocular correlation sensitivity and its relationship with stereopsis

Near-optimal combination of disparity across a log-polar scaled visual field

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