Vision Science
- Cone Response & LMS Modeling: Compute cone fundamentals, photoreceptor responses, and LMS transformations to support research on human visual processing and early-stage perception.
- Color Vision Deficiency Simulations: Model protan, deutan, and tritan deficiencies, generate confusion lines, and simulate how scenes or stimuli appear to observers with various types of color vision anomalies.
- Opponent Processing & Neural Pathways: Use DKL and related opponent-color spaces to explore post-receptoral encoding, color opponency mechanisms, and perceptual uniformity in the visual system.
- Spectral Sensitivity & Hazard Functions: Analyze luminous efficiency, blue-light hazard functions, lens aging, macular pigment absorption, and their effects on visual performance and color perception across age groups.
- Psychophysical Experiment Design: Generate controlled stimuli, compute chromatic adaptation states, apply color appearance models, and evaluate perceptual color differences for use in vision experiments and human-factor studies.
- Color Vision Test Development: Design and analyze standardized color‐vision tests viz. Farnsworth–Munsell 100 Hue Test, Farnsworth D-15, Lanthony desaturated D-15, Ishihara pseudo isochromatic plates etc., and related diagnostic tools—by simulating plate appearance under different deficiencies, evaluating hue discrimination thresholds, and validating test accuracy using LMS-based transformations and perceptual difference metrics.
Below are presented a series of simulation results that highlight how color perception changes under different visual conditions. Using the Macbeth ColorChecker—a global standard for evaluating color reproduction—and using Kolor TB™, various visual scenarios that demonstrate how individuals with protanopia, deuteranopia, and tritanopia would perceive the same set of colors.
These simulations allow researchers and designers to:
- visualize perceptual loss: Observe how reds, greens, and blues collapse into indistinguishable channels on achromatic axis depending on the deficiency type.
- compare normal vs. vision deficient: Clearly see shifts in hue, brightness loss, and confusion regions that are otherwise difficult to imagine or quantify.
- assess real-world impact: Understand how critical visual tasks—such as distinguishing color-coded signals, materials, or interfaces—are affected for different observers!
- validate models and hypotheses: Use the rendered results to support psychophysical studies, test perceptual models, and refine visual ergonomics.
- create inclusive designs: Inform product development, UI/UX design, and safety systems by viewing how standard color palettes fail or succeed across visual conditions.
