A PHP Error was encountered

Severity: Warning

Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests

Filename: helpers/my_audit_helper.php

Line Number: 176

Backtrace:

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 1034
Function: getPubMedXML

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3152
Function: GetPubMedArticleOutput_2016

File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global

File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword

File: /var/www/html/index.php
Line: 316
Function: require_once

Does dichromatic color simulation predict color identification error rates? | LitMetric

Purpose: Several algorithms are available to transform colored digital images into simulated dichromatic color perception. These algorithms can be very illustrative of the problems dichromats experience in discriminating colors. The purpose of this study was to determine whether one type of transformation could provide a quantitative account of error rates in identifying colors displayed on a computer monitor.

Methods: The experimental task required observers to identify the color of small rectangles displayed on a computer monitor within a black background. There were eight colors. The number of errors for each color was recorded. Four deuteranopes and five protanopes participated. Color differences were calculated using normal trichromatic and dichromatic values. The dichromatic color differences were calculated using the procedure developed by Brettel et al. [J Opt Soc Am (A) 1997;14:2647-55].

Results: The relationship between error rates and color differences calculated in either color space was fit by an exponential decay function. However, the fit provided by the dichromatic color differences was no better than that using color differences calculated in trichromatic color space and both regressions could only account for approximately 30% of the variance in the data.

Conclusions: Correlations between the error rates in identifying colors for dichromats and color differences were low-to-moderate whether the color differences were based on normal trichromatic color space or a dichromatic transformation. This finding suggests that it may be sufficient to calculate the color difference only in color-normal space to determine whether the colors will be confused by a person with a congenital color vision defect. Although computer algorithms are useful in illustrating color discrimination problems experienced by dichromats, they may not offer any advantage over typical trichromatic color spaces in predicting performance in color identification. The lack of any advantage may be due to how dichromats use brightness information to identify colors.

Download full-text PDF

Source
http://dx.doi.org/10.1097/OPX.0b013e31821bfb68DOI Listing

Publication Analysis

Top Keywords

color differences
28
color
19
dichromatic color
16
differences calculated
16
error rates
12
color space
12
trichromatic color
12
color identification
8
rates identifying
8
identifying colors
8

Similar Publications

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!