A numerical equivalent of the four color map Problem

1936 ◽  
Vol 45 (1) ◽  
pp. 207-213 ◽  
Author(s):  
Hassler Whitney
Keyword(s):  
Color Map

The visualization and assessment of the anterior surface of the cornea using video keratotopography

The Eye ◽  
2020 ◽  
Vol 22 (130) ◽  
pp. 36-43
Author(s):  
Gulnara Andrienko
Keyword(s):  
Contact Lenses ◽  
Corneal Topography ◽  
Topographic Maps ◽  
Specific Situation ◽  
Main Method ◽  
Pathological Conditions ◽  
Quality Of Vision ◽  
Corneal Shape ◽  
Color Map

Corneal topography is the main method for assessing the regularity of the surface of the cornea. Corneal irregularity leads to a deterioration in its refractive properties and a decrease in the quality of vision. Learning the basics of corneal topography will help determine the choice of a color map for a specific situation, as well as understand and analyze the data associated with these maps. This article describes the main types of topographic maps, various patterns of corneal shape in normal and pathological conditions and how to use the data obtained to design and fit contact lenses. Despite the fact that images may vary depending on topographers used, the information presented in this article is universal.

scholarly journals Recreational Harvest of the Florida Bay Scallop: Wakulla County

EDIS ◽  
2018 ◽  
Vol 2018 (3) ◽  
Author(s):  
Savanna Barry
Keyword(s):  
Florida Bay ◽  
Full Color ◽  
Bay Scallop ◽  
Color Map

This handy brochure includes a full-color map identifying access routes to the boat ramps and marinas in Wakulla County, plus the latest information on scallops and scalloping, the recommended equipment you will want to bring, and a few tasty recipes for preparing Florida’s best summertime catch. Written by Savanna Barry and published by the UF/IFAS Extension Florida Sea Grant College Program. https://edis.ifas.ufl.edu/sg149 Revised May 2019.

Acoustic Scenery Recognition Using CWT and Deep Neural Network

2021 ◽  
Author(s):  
Francisco Mondragon ◽  
Jonathan Jimenez ◽  
Mariko Nakano ◽  
Toru Nakashika ◽  
Hector Perez-Meana
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Color Image ◽  
Classification Performance ◽  
Science And Engineering ◽  
Time Frequency ◽  
Frequency Representation ◽  
Public Data ◽  
Recognition Systems ◽  
Color Map

The development of acoustic scenes recognition systems has been a topic of extensive research due to its applications in several fields of science and engineering. This paper proposes an environmental system in which firstly a time-frequency representation is obtained using the Continuous Wavelet Transform (CWT). The time frequency representation is then represented as a color image using the Viridis color map, which is then inserted into a Deep Neural Network (DNN) to carry out the classification task. Evaluation results using several public data bases show that proposed scheme provides a classification performance better than the performance provided by other previously proposed schemes.

Method for Automatic Color-Map Adjustment of Thermal Image by Removing Outlier Temperature

2020 ◽  
Vol 44 (12) ◽  
pp. 897-907
Author(s):  
Seon-Woo Lee ◽  
I-Seul Jeon ◽  
Jun-Soo Lee ◽  
Joo-Hyung Kim
Keyword(s):  
Thermal Image ◽  
Color Map

scholarly journals Comparison of the SRmax, SRave, and color map of strain-elastography in differentiating malignant from benign breast lesions

2018 ◽  
Vol 60 (1) ◽  
pp. 28-34
Author(s):  
Jin Hee Moon ◽  
Sung Hye Koh ◽  
Sun-Young Park ◽  
Ji-Young Hwang ◽  
Ji Young Woo
Keyword(s):  
Statistical Significance ◽  
Roc Curves ◽  
Strain Ratio ◽  
Breast Lesions ◽  
Strain Elastography ◽  
Average Value ◽  
Maximum Value ◽  
Significant Difference ◽  
Malignant Breast ◽  
Color Map

Background The maximum value of the strain ratio (SR) is a newly developed measure in strain-elastography. Purpose To prospectively compare the diagnostic performance of three different measures of strain-elastography, the maximum value of the SR (SRmax), the average value of the SR (SRave), and the color map, for differentiating benign and malignant breast lesions. Material and Methods We obtained the SRmax and SRave of 314 lesions from 290 patients with the tissue to nodule SR and color map using a five-degree scoring system. The diagnostic performances of the SRmax, SRave, and color map were compared after obtaining the area under the receiver operating characteristic (ROC) curves (AUCs) of each parameter. Results The AUC of the SRmax (0.7674) was larger than the AUCs of the SRave (0.7138) and color map (0.6324), with statistical significance ( P = 0.0383 for SRmax vs. SRave, P = 0.0000 for SRmax vs. color map). The AUC of the SRave was larger than that of the color map; however, there was no significant difference. The optimal cut-off point of the SRmax that balanced the sensitivity (91.12%) and specificity (50.81%) was 5.16. Conclusion The SRmax is a more reliable diagnostic tool than the SRave and color map for differentiating benign and malignant breast lesions.

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