Pytlarz - Revision history

imported>Student221: /* Code Base */

2020-11-19T19:02:30Z

Code Base

← Older revision		Revision as of 19:02, 19 November 2020
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	== Code Base ==		== Code Base ==

	The code to run the experiment and analyze results is given below. Some notable functions include:		The code to analyze display measurements, run the experiment, and analyze experiment results is given below. Some notable functions include:

imported>Student221: /* Code Base */

2020-11-19T19:01:46Z

Code Base

← Older revision		Revision as of 19:01, 19 November 2020
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	The code to run the experiment and analyze results is given below. Some notable functions include:		The code to run the experiment and analyze results is given below. Some notable functions include:

	VirtualBackgroundExperiment.mlapp : Matlab App to run the experiment
	AnalyzeExperimentResults.m : Function to combine and analyze experiment results		VirtualBackgroundExperiment.mlapp:
	Measurements/PlotMeasurements.m : Function that plots the measurements of the 2020 13" Macbook Pro		:: Matlab App to run the experiment
	ImageProcessing/ApplyChromaticAdjustment.m : Function that applies the chromatic adjustment to the background image to create the stimuli for the experiment
			AnalyzeExperimentResults.m:
			::Function to combine and analyze experiment results

			Measurements/PlotMeasurements.m:
			::Function that plots the measurements of the 2020 13" Macbook Pro

			ImageProcessing/ApplyChromaticAdjustment.m:
			::Function that applies the chromatic adjustment to the background image to create the stimuli for the experiment


			Code Base: https://office365stanford-my.sharepoint.com/:f:/g/personal/jpytlarz_stanford_edu/Elv-OaT4FtRDpH7SKzwCnTcBTClh-qEpcYZE0ZHbtHC3yw?e=XIvwJ3

	== References ==		== References ==

imported>Student221 at 18:57, 19 November 2020

2020-11-19T18:57:05Z

← Older revision		Revision as of 18:57, 19 November 2020
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	An experiment was conducted that explored the detection threshold for a virtual background with changes in color temperature between the background and the face. We found that at the same color temperature, a virtual background was indistinguishable from a real background. We found the lower threshold to be around 5100K and the upper threshold to be around 12000K. This large threshold may be due to the fact that it is common to have different illumination between the face/background due to light contamination from a computer screen. It shows that matching the illumination between the background and the face can lead to a virtual background that is indistinguishable from a real background.		An experiment was conducted that explored the detection threshold for a virtual background with changes in color temperature between the background and the face. We found that at the same color temperature, a virtual background was indistinguishable from a real background. We found the lower threshold to be around 5100K and the upper threshold to be around 12000K. This large threshold may be due to the fact that it is common to have different illumination between the face/background due to light contamination from a computer screen. It shows that matching the illumination between the background and the face can lead to a virtual background that is indistinguishable from a real background.

			== Code Base ==

			The code to run the experiment and analyze results is given below. Some notable functions include:

			VirtualBackgroundExperiment.mlapp : Matlab App to run the experiment
			AnalyzeExperimentResults.m : Function to combine and analyze experiment results
			Measurements/PlotMeasurements.m : Function that plots the measurements of the 2020 13" Macbook Pro
			ImageProcessing/ApplyChromaticAdjustment.m : Function that applies the chromatic adjustment to the background image to create the stimuli for the experiment

	== References ==		== References ==

imported>Student221: /* Results */

2020-11-19T18:00:34Z

Results

← Older revision		Revision as of 18:00, 19 November 2020
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	== Results ==		== Results ==

	The experiment had ~~six~~ participants. Unfortunately most participants did not posses a 2020 13" Macbook Pro, but for the purposes of this analysis, we will assume as much. Each participant had four results for each color temperature tested from the 8 scenarios and the 32 trials. The results were compiled (with some outlier analysis) to produce percent correct for each color temperature. The results are a series of 1's and 0's where a 1 indicates a correct choice, and a 0 indicates an incorrect choice.		The experiment had 10 participants. Unfortunately most participants did not posses a 2020 13" Macbook Pro, but for the purposes of this analysis, we will assume as much. Each participant had four results for each color temperature tested from the 8 scenarios and the 32 trials. The results were compiled (with some outlier analysis) to produce percent correct for each color temperature. The results are a series of 1's and 0's where a 1 indicates a correct choice, and a 0 indicates an incorrect choice.

	For analysis, the average correct responses were computed for each CCT level tested. The results for the two background types were combined for a single percent correct detection for each CCT. The results are plotted below in red with "standard error" error bars. A 50% threshold line is also listed in black. This level represents an observer "guessing" (ie. if the observer could not tell which image had the fake background). A best-fit gaussian is shown in blue.		For analysis, the average correct responses were computed for each CCT level tested. The results for the two background types were combined for a single percent correct detection for each CCT. The results are plotted below in red with "standard error" error bars. A 50% threshold line is also listed in black. This level represents an observer "guessing" (ie. if the observer could not tell which image had the fake background). A best-fit gaussian is shown in blue.

imported>Student221: /* Stimuli */

2020-11-19T17:59:22Z

Stimuli

← Older revision		Revision as of 17:59, 19 November 2020
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	The adjustments for the images were completed for the background image. The face remained the same as the original. The adjustments and CCT values are given above. The translation between CCT and xy are computed via the use of a look-up-table (LUT) [3]. An xy plot of the adjustment points is also shown below with the white dashed line representing Rec709/sRGB. We have 8 tests points including D65 (at 6500K). The LUT was shifted to perfectly align D65 to standardized [0.3127 0.3290] chromaticity coordinates.		The adjustments for the images were completed for the background image. The face remained the same as the original. The adjustments and CCT values are given above. Equal spacing is equivalent to roughly 1/CCT [12]. The translation between CCT and xy are computed via the use of a look-up-table (LUT) [3]. An xy plot of the adjustment points is also shown below with the white dashed line representing Rec709/sRGB. We have 8 tests points including D65 (at 6500K). The LUT was shifted to perfectly align D65 to standardized [0.3127 0.3290] chromaticity coordinates.

imported>Student221: /* References */

2020-11-19T17:58:20Z

References

← Older revision		Revision as of 17:58, 19 November 2020
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	[11] Moroney, Nathan; Fairchild, Mark D.; Hunt, Robert W.G.; Li, Changjun; Luo, M. Ronnier; Newman, Todd (November 12, 2002). "The CIECAM02 Color Appearance Model". IS&T/SID Tenth Color Imaging Conference. Scottsdale, Arizona: The Society for Imaging Science and Technology. ISBN 0-89208-241-0.		[11] Moroney, Nathan; Fairchild, Mark D.; Hunt, Robert W.G.; Li, Changjun; Luo, M. Ronnier; Newman, Todd (November 12, 2002). "The CIECAM02 Color Appearance Model". IS&T/SID Tenth Color Imaging Conference. Scottsdale, Arizona: The Society for Imaging Science and Technology. ISBN 0-89208-241-0.

			[12] Tominaga, Shoji. Wandell, Brian A., Natural Scene-Illuminant Estimation Using the Sensor Correlation. PROCEEDINGS OF THE IEEE, VOL. 90, NO. 1, JANUARY 2002.

imported>Student221: /* Stimuli */

2020-11-19T17:24:14Z

Stimuli

← Older revision		Revision as of 17:24, 19 November 2020
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	Once I had the base renderings and the color transforms defined from the display measurements, I created a database of virtual background pairings while adjusting the color temperature of the background. It was assumed that the desired white point was D65 and the chromatic adaptation was derived from here. The Hunt-Pointer-Estevez LMS space was used to apply the Von Kries transform for chromatic adaptation. The <math> \rho, \beta, \gamma </math> are the LMS values of the white of the target and of D65. Then adaptation is accomplished by a matrix multiplication in LMS. The inverse transform M^-1 is used to go back to XYZ and then to display space.		Once I had the base renderings and the color transforms defined from the display measurements, I created a database of virtual background pairings while adjusting the color temperature of the background. It was assumed that the desired white point was D65 and the chromatic adaptation was derived from here. The Hunt-Pointer-Estevez [11] LMS space was used to apply the Von Kries transform for chromatic adaptation. The <math> \rho, \beta, \gamma </math> are the LMS values of the white of the target and of D65. Then adaptation is accomplished by a matrix multiplication in LMS. The inverse transform M^-1 is used to go back to XYZ and then to display space.

	<center> <math>M_{HPE} = \begin{bmatrix}0.4002400 & 0.7076000 &-0.0808100\\		<center> <math>M_{HPE} = \begin{bmatrix}0.4002400 & 0.7076000 &-0.0808100\\

imported>Student221: /* References */

2020-11-19T17:24:00Z

References

← Older revision		Revision as of 17:24, 19 November 2020
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	[10] Radonjić, A., Ding, X., Krieger, A., Aston, S., Hurlbert, A. C., & Brainard, D. H. (2018). Illumination discrimination in the absence of a fixed surface-reflectance layout. Journal of Vision, 18(5):11, 1–27, https://doi.org/10.1167/18.5.11		[10] Radonjić, A., Ding, X., Krieger, A., Aston, S., Hurlbert, A. C., & Brainard, D. H. (2018). Illumination discrimination in the absence of a fixed surface-reflectance layout. Journal of Vision, 18(5):11, 1–27, https://doi.org/10.1167/18.5.11

			[11] Moroney, Nathan; Fairchild, Mark D.; Hunt, Robert W.G.; Li, Changjun; Luo, M. Ronnier; Newman, Todd (November 12, 2002). "The CIECAM02 Color Appearance Model". IS&T/SID Tenth Color Imaging Conference. Scottsdale, Arizona: The Society for Imaging Science and Technology. ISBN 0-89208-241-0.

imported>Student221: /* Stimuli */

2020-11-19T17:04:44Z

Stimuli

← Older revision		Revision as of 17:04, 19 November 2020
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	Once I had the base renderings and the color transforms defined from the display measurements, I created a database of virtual background pairings while adjusting the color temperature of the background. It was assumed that the desired white point was D65 and the chromatic adaptation was derived from here. The ~~Von Kries~~ LMS space was used for chromatic adaptation. The <math> \rho, \beta, \gamma </math> are the LMS values of the white of the target and of D65. Then adaptation is accomplished by a matrix multiplication in LMS. The inverse transform M^-1 is used to go back to XYZ and then to display space.		Once I had the base renderings and the color transforms defined from the display measurements, I created a database of virtual background pairings while adjusting the color temperature of the background. It was assumed that the desired white point was D65 and the chromatic adaptation was derived from here. The Hunt-Pointer-Estevez LMS space was used to apply the Von Kries transform for chromatic adaptation. The <math> \rho, \beta, \gamma </math> are the LMS values of the white of the target and of D65. Then adaptation is accomplished by a matrix multiplication in LMS. The inverse transform M^-1 is used to go back to XYZ and then to display space.

	<center> <math>M_{~~VonKries~~} = \begin{bmatrix}0.4002400 & 0.7076000 &-0.0808100\\		<center> <math>M_{HPE} = \begin{bmatrix}0.4002400 & 0.7076000 &-0.0808100\\
	-0.2263000 &1.1653200 & 0.0457000\\		-0.2263000 &1.1653200 & 0.0457000\\
	0.0000000 & 0.0000000 &0.9182200 \end{bmatrix}</math>		0.0000000 & 0.0000000 &0.9182200 \end{bmatrix}</math>


	<math> A = \begin{bmatrix} \rho_{new}/\rho_{D65}&0&0 \\		<math> A_{VonKries} = \begin{bmatrix} \rho_{new}/\rho_{D65}&0&0 \\
	0&\gamma_{new}/\gamma_{D65}&0 \\		0&\gamma_{new}/\gamma_{D65}&0 \\
	0&0&\beta_{new}/\beta_{D65} \end{bmatrix}</math>		0&0&\beta_{new}/\beta_{D65} \end{bmatrix}</math>

imported>Student221 at 22:34, 18 November 2020

2020-11-18T22:34:17Z

← Older revision		Revision as of 22:34, 18 November 2020
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	=Virtual Background Detection=		=Virtual Background Detection=
			by Jaclyn Pytlarz



	== Introduction/Background ==		== Introduction/Background ==