Journal: Sensors (Basel, Switzerland)

Article Title: Affective Colormap Design for Accurate Visual Comprehension in Industrial Tomography †

doi: 10.3390/s21144766

Figure Lengend Snippet: The 11 colormaps we studied with their hues and lightness characteristics, followed by each colormap’s underlying design strategy .

Article Snippet: Second, we chose continuous colormaps simply because of their ability to retain complete information of MWT as well as respecting the default setting in MATLAB.

Techniques:

Journal: Sensors (Basel, Switzerland)

Article Title: Affective Colormap Design for Accurate Visual Comprehension in Industrial Tomography †

doi: 10.3390/s21144766

Figure Lengend Snippet: Top-down image processing pipeline (arrow): Each of the 11 colormaps (1st row) is applied to the same MWT image resulting in a new image (2nd row) and yielding corresponding segmented images (3rd row). Due to limited space, we randomly chose one MWT image from our total of eight. The goal of segmentation was to visualize the blue parts in the colormap parula .

Article Snippet: Second, we chose continuous colormaps simply because of their ability to retain complete information of MWT as well as respecting the default setting in MATLAB.

Techniques:

Journal: Sensors (Basel, Switzerland)

Article Title: Affective Colormap Design for Accurate Visual Comprehension in Industrial Tomography †

doi: 10.3390/s21144766

Figure Lengend Snippet: The quantitative evaluation of the 11 colormaps over 8 samples . The first subfigure: Jaccard index (the higher value, the better performance); Middle subfigure: Dice coefficient (the higher value, the better performance); Third subfigure: false positive (the lower value, the better performance).

Article Snippet: Second, we chose continuous colormaps simply because of their ability to retain complete information of MWT as well as respecting the default setting in MATLAB.

Techniques:

Journal: Sensors (Basel, Switzerland)

Article Title: Affective Colormap Design for Accurate Visual Comprehension in Industrial Tomography †

doi: 10.3390/s21144766

Figure Lengend Snippet: The specification of the user study carried out, including the stimuli and the anticipated results.

Article Snippet: Second, we chose continuous colormaps simply because of their ability to retain complete information of MWT as well as respecting the default setting in MATLAB.

Techniques:

Journal: Sensors (Basel, Switzerland)

Article Title: Affective Colormap Design for Accurate Visual Comprehension in Industrial Tomography †

doi: 10.3390/s21144766

Figure Lengend Snippet: The individual distribution of the affect for the 11 colormaps in the dimension of valence.

Article Snippet: Second, we chose continuous colormaps simply because of their ability to retain complete information of MWT as well as respecting the default setting in MATLAB.

Techniques:

Journal: Sensors (Basel, Switzerland)

Article Title: Affective Colormap Design for Accurate Visual Comprehension in Industrial Tomography †

doi: 10.3390/s21144766

Figure Lengend Snippet: The individual distribution of the affect for the 11 colormaps in the dimension of arousal.

Article Snippet: Second, we chose continuous colormaps simply because of their ability to retain complete information of MWT as well as respecting the default setting in MATLAB.

Techniques:

Journal: Sensors (Basel, Switzerland)

Article Title: Affective Colormap Design for Accurate Visual Comprehension in Industrial Tomography †

doi: 10.3390/s21144766

Figure Lengend Snippet: The synthetic distribution of the 11 colomaps regarding the affect evoked in the valence–arousal coordinate system. The white dots represent the exact locations of the colormaps.

Article Snippet: Second, we chose continuous colormaps simply because of their ability to retain complete information of MWT as well as respecting the default setting in MATLAB.

Techniques:

Journal: Sensors (Basel, Switzerland)

Article Title: Affective Colormap Design for Accurate Visual Comprehension in Industrial Tomography †

doi: 10.3390/s21144766

Figure Lengend Snippet: The overall accuracy rating results of the 11 colormaps by the 73 participants (rating scale: very high accuracy, high accuracy, intermediate accuracy, low accuracy and very low accuracy).

Article Snippet: Second, we chose continuous colormaps simply because of their ability to retain complete information of MWT as well as respecting the default setting in MATLAB.

Techniques:

Journal: Sensors (Basel, Switzerland)

Article Title: Affective Colormap Design for Accurate Visual Comprehension in Industrial Tomography †

doi: 10.3390/s21144766

Figure Lengend Snippet: The holistic accuracy rankings (high to low) of the 11 colormaps obtained from study part 2.

Article Snippet: Second, we chose continuous colormaps simply because of their ability to retain complete information of MWT as well as respecting the default setting in MATLAB.

Techniques:

Journal: Sensors (Basel, Switzerland)

Article Title: Affective Colormap Design for Accurate Visual Comprehension in Industrial Tomography †

doi: 10.3390/s21144766

Figure Lengend Snippet: The analytic results for the three baseline colormaps autumn , viridis and parula . The X-axis represents the positive–exciting (P-E) and other quadrants (OTH) from the valence–arousal model. The Y-axis shows the number of participants who rated the designated colormap. Right: the number of people who rated the baseline colormap as desirable in the crowdsourced study in Part II. Wrong: the number of people who rated the baseline colormap as undesirable in the crowdsourced study in Part II.

Article Snippet: Second, we chose continuous colormaps simply because of their ability to retain complete information of MWT as well as respecting the default setting in MATLAB.

Techniques:

Journal: Sensors (Basel, Switzerland)

Article Title: Affective Colormap Design for Accurate Visual Comprehension in Industrial Tomography †

doi: 10.3390/s21144766

Figure Lengend Snippet: Top-down image processing pipeline (arrow): Each of the 11 colormaps (1st row) is applied to the same MWT image resulting in a new image (2nd row) and yielding corresponding segmented images (3rd row). Due to limited space, we randomly chose one MWT image from our total of eight. The goal of segmentation was to visualize the blue parts in the colormap parula .

Article Snippet: These reconstructed images possess an intrinsic continuous colormap when being handled in MATLAB, which is denoted as parula .

Techniques:

Journal: Sensors (Basel, Switzerland)

Article Title: Affective Colormap Design for Accurate Visual Comprehension in Industrial Tomography †

doi: 10.3390/s21144766

Figure Lengend Snippet: The specification of the user study carried out, including the stimuli and the anticipated results.

Article Snippet: These reconstructed images possess an intrinsic continuous colormap when being handled in MATLAB, which is denoted as parula .

Techniques:

Journal: Sensors (Basel, Switzerland)

Article Title: Affective Colormap Design for Accurate Visual Comprehension in Industrial Tomography †

doi: 10.3390/s21144766

Figure Lengend Snippet: Summary of the mean values and standard deviations of the colormap accuracy rated in crowdsourced study Part II with 99.5% CI.

Article Snippet: These reconstructed images possess an intrinsic continuous colormap when being handled in MATLAB, which is denoted as parula .

Techniques:

Journal: Sensors (Basel, Switzerland)

Article Title: Affective Colormap Design for Accurate Visual Comprehension in Industrial Tomography †

doi: 10.3390/s21144766

Figure Lengend Snippet: Mean values with standard errors (99.5% CI) of the colormap accuracy rated by participants.

Article Snippet: These reconstructed images possess an intrinsic continuous colormap when being handled in MATLAB, which is denoted as parula .

Techniques:

Journal: Sensors (Basel, Switzerland)

Article Title: Affective Colormap Design for Accurate Visual Comprehension in Industrial Tomography †

doi: 10.3390/s21144766

Figure Lengend Snippet: The analytic results for the three baseline colormaps autumn , viridis and parula . The X-axis represents the positive–exciting (P-E) and other quadrants (OTH) from the valence–arousal model. The Y-axis shows the number of participants who rated the designated colormap. Right: the number of people who rated the baseline colormap as desirable in the crowdsourced study in Part II. Wrong: the number of people who rated the baseline colormap as undesirable in the crowdsourced study in Part II.

Article Snippet: These reconstructed images possess an intrinsic continuous colormap when being handled in MATLAB, which is denoted as parula .

Techniques: