Digital video colourmaps for checking the legibility of displays by dichromats

Viénot, Françoise; Brettel, Hans; Mollon, J. D.

doi:10.1002/(sici)1520-6378(199908)24:4<243::aid-col5>3.0.co;2-3

Cited by 137 publications

(79 citation statements)

References 8 publications

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“…Therefore, the correspondence between the XYZ color system and the spectral sensitivities of LMS cone responses on the retina are expressed by equation (2) [18]. In this experiment, the characteristics of protanopia, which is a major type of color deficiency, was adopted to represent color vision deficiency, because human faces colors contain a lot of red.…”

Section: Simulated Color Vision Deficiencymentioning

confidence: 99%

Effect of Changes in Face Color on Emotion Perception in Color Vision Deficiency

Sejima

Takimoto

Sato

et al. 2016

Transactions of Japan Society of Kansei Engineering

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Abstract:Face color plays an important role in enhancing empathy in human face-to-face communication, and supports recognition of human emotions such as delight and sorrow. However, these effects cannot be applied to people with a color vision deficiency, who differ in color vision compared to people with normal color vision. Therefore, it is essential to investigate differences in the role of face color in perceiving human emotions. This paper focuses on face color in color vision deficiency, and the experiments evaluate the influence of face color on perception in people with normal color vision and a color vision deficiency. In addition, we evaluated differences in emotion perception between people with normal color vision and people with a color vision deficiency. The results demonstrate that orange improves perception of delight.

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Section: Simulated Color Vision Deficiencymentioning

confidence: 99%

Effect of Changes in Face Color on Emotion Perception in Color Vision Deficiency

Sejima

Takimoto

Sato

et al. 2016

Transactions of Japan Society of Kansei Engineering

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“…Here, we use the model proposed by Viénot et al [6]. This model is based upon their experimental result and its expression is a simple transformation in the LMS system.…”

Section: Viénot's Modelmentioning

confidence: 99%

Color Image Enhancement for Dichromats by Additive Image Noise

Wakimoto

Kanazawa

Ohta

2013

IPSJ Transactions on Computer Vision and Applications

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We present a method for enhancing the color recognition ability of dichromats. Whereas trichromats (usual people) recognize all colors in a 3-D color space, dichromats only recognize colors on a degenerate 2-D space in it. Our method compensates for the lost information along the degenerate direction in the color space with the amount of noise in the image. Dichromats recognize the lost color information as noisy textures, while the original color information for trichromats is preserved. Our method is applicable not only to artificial figures such as graphs but also to natural photographs. We show the effectiveness of our method by experiments.

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“…17 Then the LMS color space is obtained by apply a 3 × 3 matrix transformation on the three XYZ components corresponding to the Simth-Pokorny matrix. 18…”

Section: Color Space Transformationmentioning

confidence: 99%