Nonparametric Data Analysis on the Space of Perceived Colors
This work addresses color differentiation in machine vision, but it appears incremental as it builds on existing models like Resnikoff's space.
The paper tackles the problem of analyzing perceived colors as random objects on a 3D homogeneous space model, proposing a statistical methodology and applying it to color differentiation in machine vision, with results including two illustrated applications.
Moving around in a 3D world, requires the visual system of a living individual to rely on three channels of image recognition, which is done through three types of retinal cones. Newton, Grasmann, Helmholz and Schr$\ddot{o}$dinger laid down the basic assumptions needed to understand colored vision. Such concepts were furthered by Resnikoff, who imagined the space of perceived colors as a 3D homogeneous space. 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, applied to the Euclidean model for perceived colors.