Computational Trichromacy Reconstruction: Empowering the Color-Vision Deficient to Recognize Colors Using Augmented Reality
- URL: http://arxiv.org/abs/2408.01895v2
- Date: Thu, 26 Sep 2024 14:57:13 GMT
- Title: Computational Trichromacy Reconstruction: Empowering the Color-Vision Deficient to Recognize Colors Using Augmented Reality
- Authors: Yuhao Zhu, Ethan Chen, Colin Hascup, Yukang Yan, Gaurav Sharma,
- Abstract summary: We propose an assistive technology that helps individuals with Color Vision Deficiencies (CVD) to recognize/name colors.
A dichromat's color perception is a reduced two-dimensional (2D) subset of a normal trichromat's three dimensional color (3D) perception.
Using our proposed system, CVD individuals can interactively induce distinct changes to originally confusing colors via a computational color space transformation.
- Score: 12.77228283953913
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: We propose an assistive technology that helps individuals with Color Vision Deficiencies (CVD) to recognize/name colors. A dichromat's color perception is a reduced two-dimensional (2D) subset of a normal trichromat's three dimensional color (3D) perception, leading to confusion when visual stimuli that appear identical to the dichromat are referred to by different color names. Using our proposed system, CVD individuals can interactively induce distinct perceptual changes to originally confusing colors via a computational color space transformation. By combining their original 2D precepts for colors with the discriminative changes, a three dimensional color space is reconstructed, where the dichromat can learn to resolve color name confusions and accurately recognize colors. Our system is implemented as an Augmented Reality (AR) interface on smartphones, where users interactively control the rotation through swipe gestures and observe the induced color shifts in the camera view or in a displayed image. Through psychophysical experiments and a longitudinal user study, we demonstrate that such rotational color shifts have discriminative power (initially confusing colors become distinct under rotation) and exhibit structured perceptual shifts dichromats can learn with modest training. The AR App is also evaluated in two real-world scenarios (building with lego blocks and interpreting artistic works); users all report positive experience in using the App to recognize object colors that they otherwise could not.
Related papers
- A Computational Framework for Modeling Emergence of Color Vision in the Human Brain [9.10623460958915]
It is a mystery how the brain decodes color vision purely from the optic nerve signals it receives.
We introduce a computational framework for modeling this emergence of human color vision by simulating both the eye and the cortex.
arXiv Detail & Related papers (2024-08-29T21:27:06Z) - THOR2: Leveraging Topological Soft Clustering of Color Space for Human-Inspired Object Recognition in Unseen Environments [1.9950682531209158]
This study presents a 3D shape and color-based descriptor, TOPS2, for point clouds generated from RGB-D images and an accompanying recognition framework, THOR2.
The TOPS2 descriptor embodies object unity, a human cognition mechanism, by retaining the slicing-based topological representation of 3D shape from the TOPS descriptor.
THOR2, trained using synthetic data, demonstrates markedly improved recognition accuracy compared to THOR, its 3D shape-based predecessor.
arXiv Detail & Related papers (2024-08-02T21:24:14Z) - Comparative Analysis Of Color Models For Human Perception And Visual Color Difference [0.0]
The study evaluates color models such as RGB, HSV, HSL, XYZ, CIELAB, and CIELUV to assess their effectiveness in accurately representing how humans perceive color.
In image processing, accurate assessment of color difference is essential for applications ranging from digital design to quality control.
arXiv Detail & Related papers (2024-06-27T20:41:49Z) - Color Equivariant Convolutional Networks [50.655443383582124]
CNNs struggle if there is data imbalance between color variations introduced by accidental recording conditions.
We propose Color Equivariant Convolutions ( CEConvs), a novel deep learning building block that enables shape feature sharing across the color spectrum.
We demonstrate the benefits of CEConvs in terms of downstream performance to various tasks and improved robustness to color changes, including train-test distribution shifts.
arXiv Detail & Related papers (2023-10-30T09:18:49Z) - ColorSense: A Study on Color Vision in Machine Visual Recognition [57.916512479603064]
We collect 110,000 non-trivial human annotations of foreground and background color labels from visual recognition benchmarks.
We validate the use of our datasets by demonstrating that the level of color discrimination has a dominating effect on the performance of machine perception models.
Our findings suggest that object recognition tasks such as classification and localization are susceptible to color vision bias.
arXiv Detail & Related papers (2022-12-16T18:51:41Z) - Name Your Colour For the Task: Artificially Discover Colour Naming via
Colour Quantisation Transformer [62.75343115345667]
We propose a novel colour quantisation transformer, CQFormer, that quantises colour space while maintaining machine recognition on the quantised images.
We observe the consistent evolution pattern between our artificial colour system and basic colour terms across human languages.
Our colour quantisation method also offers an efficient quantisation method that effectively compresses the image storage.
arXiv Detail & Related papers (2022-12-07T03:39:18Z) - Learning to Structure an Image with Few Colors and Beyond [59.34619548026885]
We propose a color quantization network, ColorCNN, which learns to structure an image in limited color spaces by minimizing the classification loss.
We introduce ColorCNN+, which supports multiple color space size configurations, and addresses the previous issues of poor recognition accuracy and undesirable visual fidelity under large color spaces.
For potential applications, we show that ColorCNNs can be used as image compression methods for network recognition.
arXiv Detail & Related papers (2022-08-17T17:59:15Z) - Detecting Recolored Image by Spatial Correlation [60.08643417333974]
Image recoloring is an emerging editing technique that can manipulate the color values of an image to give it a new style.
In this paper, we explore a solution from the perspective of the spatial correlation, which exhibits the generic detection capability for both conventional and deep learning-based recoloring.
Our method achieves the state-of-the-art detection accuracy on multiple benchmark datasets and exhibits well generalization for unknown types of recoloring methods.
arXiv Detail & Related papers (2022-04-23T01:54:06Z) - The Utility of Decorrelating Colour Spaces in Vector Quantised
Variational Autoencoders [1.7792264784100689]
We propose colour space conversion to enforce a network learning structured representations.
We trained several instances of VQ-VAE whose input is an image in one colour space, and its output in another.
arXiv Detail & Related papers (2020-09-30T07:44:01Z) - Semantic-driven Colorization [78.88814849391352]
Recent colorization works implicitly predict the semantic information while learning to colorize black-and-white images.
In this study, we simulate that human-like action to let our network first learn to understand the photo, then colorize it.
arXiv Detail & Related papers (2020-06-13T08:13:30Z) - Nonparametric Data Analysis on the Space of Perceived Colors [0.0]
This article is concerned with perceived colors regarded as random objects on a Resnikoff 3D homogeneous space model.
Two applications to color differentiation in machine vision are illustrated for the proposed statistical methodology.
arXiv Detail & Related papers (2020-04-05T17:43:33Z) - Investigating the Importance of Shape Features, Color Constancy, Color
Spaces and Similarity Measures in Open-Ended 3D Object Recognition [4.437005770487858]
We study the importance of shape information, color constancy, color spaces, and various similarity measures in open-ended 3D object recognition.
Experimental results show that all of the textitcombinations of color and shape yields significant improvements over the textitshape-only and textitcolor-only approaches.
arXiv Detail & Related papers (2020-02-10T14:24:09Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.