Interactions between luminance and color signals: Effects on shape

Clery, Stéphane; Bloj, Marina; Harris, Julie M.

doi:10.1167/13.5.16

Cited by 15 publications

(14 citation statements)

References 74 publications

(120 reference statements)

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“…Given the variability in previous investigations examining the relationship between the perception of spatial, chromatic and achromatic information (e.g. Clery et al, ; Miquilini et al, ; Shapley & Hawken, ; Shevell & Kingdom, ), we recommend such combined thresholds be confirmed using contextualized behavioural experiments. For more information on the mechanism of the RNL clustering as well as the combination and weighting of chromatic and achromatic thresholds see the Supplemental Material.…”

Section: Methodsmentioning

confidence: 97%

Quantitative Colour Pattern Analysis (QCPA): A comprehensive framework for the analysis of colour patterns in nature

et al. 2019

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1. To understand the function of colour signals in nature, we require robust quantitative analytical frameworks to enable us to estimate how animal and plant colour patterns appear against their natural background as viewed by ecologically relevant species. Due to the quantitative limitations of existing methods, colour and pattern are rarely analysed in conjunction with one another, despite a large body of literature and decades of research on the importance of spatio-chromatic colour pattern analyses. Furthermore, key physiological limitations of animal visual systems such as spatial acuity, spectral sensitivities, photoreceptor abundances and receptor noise levels are rarely considered together in colour pattern analyses.2. Here, we present a novel analytical framework, called the Quantitative Colour Pattern Analysis (QCPA). We have overcome many quantitative and qualitative limitations of existing colour pattern analyses by combining calibrated digital photography and visual modelling. We have integrated and updated existing spatio-chromatic colour pattern analyses, including adjacency, visual contrast and boundary strength analysis, to be implemented using calibrated digital photography through the Multispectral Image Analysis and Calibration (MICA) Toolbox.3. This combination of calibrated photography and spatio-chromatic colour pattern analyses is enabled by the inclusion of psychophysical colour and luminance discrimination thresholds for image segmentation, which we call 'Receptor Noise Limited Clustering', used here for the first time. Furthermore, QCPA provides a novel psycho-physiological approach to the modelling of spatial acuity using convolution in the spatial or frequency domains, followed by 'Receptor Noise Limited Ranked Filtering' to eliminate intermediate edge artefacts and recover sharp boundaries following smoothing. We also present a new type of colour pattern analysis, the 'local edge intensity analysis' as well as a range of novel psycho-physiological approaches to the visualization of spatio-chromatic data. QCPA combines novel and existing pattern analysis frameworks into what we hope is a unified, free and open source toolbox and introduces a range of novel analyticalThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Methodsmentioning

confidence: 97%

Quantitative Colour Pattern Analysis (QCPA): A comprehensive framework for the analysis of colour patterns in nature

et al. 2019

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“…2,8,9,[44][45][46][47][48] In addition, it has been reported that higher luminance contrast generally induces larger perceived depth. 45,[50][51][52][53][54][55][56][57][58] Thus, the improvement by the luminance gradient might have resulted from visual interpretation of the luminance gradient as shading along with the higher luminance contrast.…”

Section: Effects Of the Spbr-inherent Luminance Gradientmentioning

confidence: 99%

“…As mentioned above, higher luminance contrast generally produces larger perceived depth. 45,[50][51][52][53][54][55][56][57][58] This lower luminance contrast within the isosurface with the SPBR-inherent luminance gradient was caused by introducing the bright luminance gradient on the dark hillside with the large slant (Fig. 3).…”

Section: Effects Of the Spbr-inherent Luminance Gradientmentioning

confidence: 99%

Quantitative evaluation of perceived depth of transparently-visualized medical 3D data presented with a multi-view 3D display

Sakano

Kitaura

Hasegawa

et al. 2018

Int. J. Model. Simul. Sci. Comput.

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Transparent visualization is used in many fields because it can visualize not only the frontal object but also other important objects behind it. Although in many situations, it would be very important for the 3D structures of the visualized transparent images to be perceived as they are simulated, little is known quantitatively as to how such transparent 3D structures are perceived. To address this question, in the present study, we conducted a psychophysical experiment in which the observers reported the perceived depth magnitude of a transparent object in medical images, presented with a multi-view 3D display. For the visualization, we employed a stochastic point-based rendering (SPBR) method, which was developed recently as a technique for efficient transparent-rendering. Perceived depth of the transparent object was smaller than the simulated depth. We found, however, that such depth underestimation can be alleviated to some extent by (1) applying luminance gradient inherent in the SPBR method, (2) employing high opacities, and (3) introducing binocular disparity and motion parallax produced by a multi-view 3D display.

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“…Brightness is a key component of primate colour vision; our brain binds together both chromatic and achromatic (brightness) information when interpreting colourful images (Burns and Shepp 1988;Croner and Albright 1999;Clery et al 2013). Brightness is defined as the attribute of a visual sensation where a stimulus is perceived to be more or less intense, based on the achromatic modulation of photoreceptors by the stimulus (Wyszecki and Stiles 1982).…”

Section: Introductionmentioning

confidence: 99%

Why colour is complex: Evidence that bees perceive neither brightness nor green contrast in colour signal processing

Garcia

Dyer

2018

FACETS

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Honey bees ( Apis mellifera Linnaeus, 1758) potentially rely on a variety of visual cues when searching for flowers in the environment. Both chromatic and achromatic (brightness) components of flower signals have typically been considered simultaneously to understand how flower colours have evolved. However, it is unclear whether honey bees actually use brightness information in their colour perception. We investigated whether free-flying honey bees can process brightness cues in achromatic stimuli when presented at a large visual angle of 28° to ensure colour processing. We found that green contrast (modulation of the green receptor against the background) and brightness contrast (modulation of all three receptors against the background) did not have a significant effect on the proportion of correct choices made by bees, indicating that they did not appear to use brightness cues in a colour processing context. Our findings also reveal that, even at a small visual angle, honeybees do not reliably process single targets solely based on achromatic information, at least considering values up to 60% modulation of brightness. We discuss these findings in relation to proposed models of bee colour processing. Therefore, caution should be taken when interpreting elemental components of complex flower colours as perceived by different animals.

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Interactions between luminance and color signals: Effects on shape

Cited by 15 publications

References 74 publications

Quantitative Colour Pattern Analysis (QCPA): A comprehensive framework for the analysis of colour patterns in nature

Quantitative Colour Pattern Analysis (QCPA): A comprehensive framework for the analysis of colour patterns in nature

Quantitative evaluation of perceived depth of transparently-visualized medical 3D data presented with a multi-view 3D display

Why colour is complex: Evidence that bees perceive neither brightness nor green contrast in colour signal processing

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