Search results for "Colorfulness"

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Images perceived after chromatic or achromatic contrast sensitivity losses.

2010

Purpose. We simulate how subjects with losses in chromatic and achromatic contrast sensitivity perceive colored images by using the spatiochromatic corresponding pair algorithm. Methods. This is a generalized version of the algorithm by Capilla et al. (J Opt Soc Am (A) 2004;21:176 –186) for simulating color perception of color deviant subjects, which incorporates a simple spatial vision model, consisting of a linear filtering stage, with a band-pass achromatic filter and two low-pass chromatic ones, for the red-green and blue-yellow mechanisms. These filters, except for the global scaling, are the subject’s contrast sensitivity functions measured along the cardinal directions of the color s…

Retinal Ganglion CellsBrightnessgenetic structuresColor visionmedia_common.quotation_subjectModels NeurologicalCorresponding pair algorithmColor spaceChromatic and achromatic CSFslaw.inventionContrast SensitivitylawImages simulationContrast (vision)HumansComputer visionChromatic scaleSensitivity (control systems)LightingÓpticaMathematicsmedia_commonbusiness.industryDiabetesColorfulnessGlaucomaOphthalmologyPattern Recognition VisualAchromatic lensArtificial intelligencebusinessColor PerceptionMathematicsOptometryOptometry and vision science : official publication of the American Academy of Optometry
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How Quickly Can We Predict Users’ Ratings on Aesthetic Evaluations of Websites? Employing Machine Learning on Eye-Tracking Data

2020

This study examines how quickly we can predict users’ ratings on visual aesthetics in terms of simplicity, diversity, colorfulness, craftsmanship. To predict users’ ratings, first we capture gaze behavior while looking at high, neutral, and low visually appealing websites, followed by a survey regarding user perceptions on visual aesthetics towards the same websites. We conduct an experiment with 23 experienced users in online shopping, capture gaze behavior and through employing machine learning we examine how fast we can accurately predict their ratings. The findings show that after 25 s we can predict ratings with an error rate ranging from 9% to 11% depending on which facet of visual ae…

business.industryComputer sciencemedia_common.quotation_subject05 social sciencesColorfulness050301 educationWord error rateE-commerceMachine learningcomputer.software_genreGazePerception0502 economics and businessEye tracking050211 marketingSimplicityArtificial intelligencebusiness0503 educationcomputermedia_commonDiversity (business)
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