Modelling, Measuring and Compensating Color Weak Vision
We use methods from Riemann geometry to investigate transformations between the color spaces of color-normal and color weak observers. The two main applications are the simulation of the perception of a color weak observer for a color normal observer and the compensation of color images in a way that a color weak observer has approximately the same perception as a color normal observer. The metrics in the color spaces of interest are characterized with the help of ellipsoids defined by the just-noticable-differences between color which are measured with the help of color-matching experiments. The constructed mappings are isometries of Riemann spaces that preserve the perceived color-differences for both observers. Among the two approaches to build such an isometry, we introduce normal coordinates in Riemann spaces as a tool to construct a global color-weak compensation map. Compared to previously used methods this method is free from approximation errors due to local linearizations and it avoids the problem of shifting locations of the origin of the local coordinate system. We analyse the variations of the Riemann metrics for different observers obtained from new color matching experiments and describe three variations of the basic method. The performance of the methods is evaluated with the help of semantic differential (SD) tests.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
CUD-NET: Color Universal Design Neural Filter for the Color Weakness
Information on images should be visually understood to anyone, including the color weakness. However, it is not recognizable if color that seems distorted to the color weakness meets an adjacent object. We suggest CUD-NE…
WISH: Weakly Supervised Instance Segmentation using Heterogeneous Labels
Instance segmentation traditionally relies on dense pixel-level annotations, making it costly and labor-intensive. To alleviate this burden, weakly supervised instance segmentation utilizes cost-effective weak labels…
Instance SegmentationSegmentationSemantic SegmentationWeakly-supervised instance segmentationPoint spread function modelling for astronomical telescopes: a review focused on weak gravitational lensing studies
The accurate modelling of the Point Spread Function (PSF) is of paramount importance in astronomical observations, as it allows for the correction of distortions and blurring caused by the telescope and atmosphere. PSF m…
Emerging from Water: Underwater Image Color Correction Based on Weakly Supervised Color Transfer
Underwater vision suffers from severe effects due to selective attenuation and scattering when light propagates through water. Such degradation not only affects the quality of underwater images but limits the ability of …
SSIMGSRender: Deduplicated Occupancy Prediction via Weakly Supervised 3D Gaussian Splatting
3D occupancy perception is gaining increasing attention due to its capability to offer detailed and precise environment representations. Previous weakly-supervised NeRF methods balance efficiency and accuracy, with mIoU …
3D ReconstructionNeRF