Quantitative evaluation of color measurements: I. Triaxial stereoscopic scatter plots

Wells, Neil A.

doi:10.1016/s0037-0738(01)00204-4

Cited by 14 publications

(5 citation statements)

References 9 publications

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“…SB2 was the interface of K 1 q 1 (SQ1) and K 1 q 2 (SQ2), and SB3 was the interface of K 1 q 2 (SQ2) and K 1 h . Previous study proposed that the sediment color of K 1 q was dominated by reduction colors (gray, gray‐green, and gray‐yellow), and this formation mainly comprised massive coarse glutenite, gray argillaceous siltstone, and fine sandstone (Figure 16; Giosan, Flood, & Aller, 2002; Giosan, Flood, Grützner, et al 2002; Helmke et al, 2002; Wells, 2002; Wells et al., 2002; Xu et al, 1998). The lithology of the overlying K 1 h and underlying J 2 x comprised brown sandy and maroon mudstones, and the corresponding sediment color was dominated by oxidation colors (e.g., red and purple), all of which suggested changes in the sedimentary environment, when the base‐level changed from rising to fall, the sediment color index decreased accordingly (Helmke et al., 2002).…”

Section: Resultsmentioning

confidence: 97%

Intelligent identification of sequence stratigraphy constrained by multipopulation genetic algorithm and dynamic time warping technique: A case study of Lower Cretaceous Qingshuihe Formation in hinterland of Junggar Basin (NW China)

et al. 2021

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The division and correlation of sequence stratigraphy are one of the essential steps in petroleum exploration. To comprehensively utilize the sequence stratigraphic information implied in multiple wireline well logs and improve the accuracy and reliability of sequence stratigraphic division, based on the edge detection, optimal partition, and natural evolution theories, and realizes the information fusion (formed Highlights1. MPGA is a powerful tool for intelligent sequencestratigraphy division.Map of Central Block of the Junggar Basin. (b) Thickness contour of Lower Cretaceous Qingshuihe Formation (K 1 q). (c) SSE-NNW-trending profile through Central Block showing stratigraphic framework of Junggar Basin (K 1 TG: Lower Cretaceous Tugulu Group; J 2 x:

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Section: Resultsmentioning

confidence: 97%

Intelligent identification of sequence stratigraphy constrained by multipopulation genetic algorithm and dynamic time warping technique: A case study of Lower Cretaceous Qingshuihe Formation in hinterland of Junggar Basin (NW China)

et al. 2021

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“…Colors can also be used to enhance the perception of a variable already displayed by another effect. Colors can be used for "coding categories" or even to represent stereoscopic scatterplots as proposed by Wells [21].…”

Section: Colormentioning

confidence: 99%

Single view vs. multiple views scatterplots

Manuri¹,

Sanna²,

Lamberti³

2019

IJECE

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<span lang="EN-US">Among all the available visualization tools, the scatterplot has been deeply analyzed through the years and many researchers investigated how to improve this tool to face new challenges. The scatterplot visualization diagram is considered one of the most functional among the variety of data visual representations, due to its relative simplicity compared to other multivariable visualization techniques. Even so, one of the most significant and unsolved challenge in data visualization consists in effectively displaying datasets with many attributes or dimensions, such as multidimensional or multivariate ones. The focus of this research is to compare the single view and the multiple views visualization paradigms for displaying multivariable dataset using scatterplots. A multivariable scatterplot has been developed as a web application to provide the single view tool, whereas for the multiple views visualization, the ScatterDice web app has been slightly modified and adopted as a traditional, yet interactive, scatterplot matrix. Finally, a taxonomy of tasks for visualization tools has been chosen to define the use case and the tests to compare the two paradigms.</span>

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“…Freeviewing of side‐by‐side images has been used in a variety of sciences, such as in biochemistry,11 crystallography,12 geology,13 molecular biology,14 molecular modeling,15 and physics,16 to mention only a few. These side‐by‐side images work for grayscale as well as for colored images as can be seen in some of these applications.…”

Section: General Use Of Stereoscopic Displaysmentioning

confidence: 99%

Three‐dimensional stereoscopic plots

Symanzik

2011

WIREs Computational Stats

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Three-dimensional (3-d) stereoscopic plots allow human viewers to interpret printed plots, plots shown on a computer screen, or plots projected to a wall as realistic 3-d images. Our human perception of depth, that is, the third dimension, works because of the fact that each of our eyes sees a slightly different image. When these images are combined in the human brain, we interpret the result as a third dimension that represents depth or distance. In 3-d stereoscopic plots, two slightly different images are created and are presented to the two eyes of the viewer. When done well, a realistic 3-d image is created in our brain. Various techniques exist to create and present the two different images to the human viewer. In this article, we will focus on techniques that have been used extensively in the field of statistics, that is, freeviewing of side-by-side images and anaglyphs. Additional techniques exist and will be presented in the context of Virtual Reality (VR) and CAVE environments.

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Quantitative evaluation of color measurements: I. Triaxial stereoscopic scatter plots

Cited by 14 publications

References 9 publications

Intelligent identification of sequence stratigraphy constrained by multipopulation genetic algorithm and dynamic time warping technique: A case study of Lower Cretaceous Qingshuihe Formation in hinterland of Junggar Basin (NW China)

Intelligent identification of sequence stratigraphy constrained by multipopulation genetic algorithm and dynamic time warping technique: A case study of Lower Cretaceous Qingshuihe Formation in hinterland of Junggar Basin (NW China)

Single view vs. multiple views scatterplots

Three‐dimensional stereoscopic plots

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