Stereoscopic Method For Highlighting The Variation Of Colors

This image made with our Alpha Transparency 3D and Crossview 3D Algorithms.

Stereoscopic Method For Highlighting The Variation Of Colors

Highlight and identify the variation of an unknown color from known colors for any binocular color combination.

Photo Width: pixels

Choose a target color.

Known Colors:
Color:

— OR —

Use our color chart or color table to find a color.

  •  Gamma correction is used for contrast enhancement (0 -.255), (No decimal before number).
  •  Threshold is used for segmenting objects (.0 -.255 or 1.0 - 255.0), (Decimal before number).
  •  Binocular color combination ratio creates a binocular combination of two colors which mixes
     them to produce, highlight, or identify the variation of a third color and present that color,
     independently, one to each eye.

  • We recommend .50 (.0-.255), (Decimal before number).

Gamma correction: Threshold: Binocular color combination ratio:

Choose a photo that contains your target color.

From our Bing™ Wallpaper Archive,

URL:	Or Upload:

The algorithm will try to highlight and identify your unknown color from known colors if possible.

Choose the eye you believe is your dominate eye.

Eye Preference / Ocular Dominance:
• Left Dominant Eye • Right Dominant Eye

  • Photos must be .jpg, or .png, not exceed 5MB, and max width by height is (w)3840px (x) (h)3840px.
  • To capture the exact shade of the unknown color from your cellphone or camera a digital photo taken must be
     12 megapixels or greater and be a sharp zoom index of 1.0x or in that range for best results.

  • Larger width numbers, the larger the photo.
  • Width can be any number between 1 and 3840.
  • NoEffect highlights or identifies no colors with no Binocular color combination.
  • Transparent highlights and identifies colors with no Binocular color combination.
  • Colors highlights and identifies colors on the Ocular Dominance side of the photo.
  • RGBY (Red-Green & Blue-Yellow) might change existing colors a the pixel level.
  • Highlighting or identifying colors is based on high photo pixel bit depth and photo quality.
  • Some matched colors may not be highlighted or identified based on pixel bit depth and photo quality.

How the algorithm can help your eyesight.

  • The algorithm can test from picturesque data for visual acuity, color deficiency, contrast sensitivity,
     nearsightedness, farsightedness, blurry vision caused from digital eye strain and daily caffeine use.

  • The algorithm can be used to highlight, identify, and interpret any unknown color from known colors from hi-res
     picturesque data and identify that unknown color from known colors.

  • To make an accurate color identification the algorithm will highlight the variation of the unknown color first
     and try to match the color from known colors to identify.

  Example from a real-user who highlighted the variation of the color red for her right dominant eye.
  • The algorithm may highlight the variation other colors in image data that may contain your chosen color.
  • In this photo her sweater, lips, hair, skin contain the color red.

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