Chandra Rajyam - Revision history

imported>Student221: /* Conclusion */

2020-12-05T05:07:47Z

Conclusion

← Older revision		Revision as of 05:07, 5 December 2020
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	== Conclusion ==		== Conclusion ==

	The modules mentioned here have varying degrees of success. For example, the blind-spot distance method is rather inaccurate, whereas the logical pixel density module fares better. The choice of what modules to use will ultimately depends on the circumstances of the experiment being conducted. In fact, a useful next step would be to survey researchers who use Mechanical Turk to better understand what modules they would want. This will help clarify tolerances/features that need to be developed, in turn encouraging better adoption of the library.		The modules mentioned here have varying degrees of success. For example, the blind-spot distance method is rather inaccurate, whereas the logical pixel density module fares better. The choice of what modules to use will ultimately depends on the circumstances of the experiment being conducted. In fact, a useful next step would be to survey researchers who use Mechanical Turk to better understand what modules they would want (adding more modules is important). This will help clarify tolerances/features that need to be developed, in turn encouraging better adoption of the library.

	== References ==		== References ==

imported>Student221 at 05:04, 5 December 2020

2020-12-05T05:04:07Z

← Older revision		Revision as of 05:04, 5 December 2020
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	Other modules are currently in development, allowing experiments to detect hue shift [4], saturating non-linearity [2] and color (“Coca-Cola solution” - C Bechlivanidis & DA Lagnado, pers.comm., 2015). It is important that modules are simple. Observer stress should be kept low to avoid an early dropout of the subject. Collecting more data is a healthy way of dealing with software/hardware diversity. Rigorously calibrating the user environment is tough (i.e. people fidgeting after distance test). It would be nice to test these modules and get data on error (Preliminary tests on distance measurements have been included below). However, there is potential for this project to grow into an open-source library.		Other modules are currently in development, allowing experiments to detect hue shift [4], saturating non-linearity [2] and color (“Coca-Cola solution” - C Bechlivanidis & DA Lagnado, pers.comm., 2015). It is important that modules are simple. Observer stress should be kept low to avoid an early dropout of the subject. Collecting more data is a healthy way of dealing with software/hardware diversity. Rigorously calibrating the user environment is tough (i.e. people fidgeting after distance test). It would be nice to test these modules and get data on error (Preliminary tests on distance measurements have been included below). However, there is potential for this project to grow into an open-source library.


	== Results ==		== Results ==
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	[[File:blindexp.png\|500px]]		[[File:blindexp.png\|500px]]

			== Conclusion ==

			The modules mentioned here have varying degrees of success. For example, the blind-spot distance method is rather inaccurate, whereas the logical pixel density module fares better. The choice of what modules to use will ultimately depends on the circumstances of the experiment being conducted. In fact, a useful next step would be to survey researchers who use Mechanical Turk to better understand what modules they would want. This will help clarify tolerances/features that need to be developed, in turn encouraging better adoption of the library.

	== References ==		== References ==

imported>Student221: /* Experiments */

2020-12-05T03:22:22Z

Experiments

← Older revision		Revision as of 03:22, 5 December 2020
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	Other modules are currently in development, allowing experiments to detect hue shift [4], saturating non-linearity [2] and color (“Coca-Cola solution” - C Bechlivanidis & DA Lagnado, pers.comm., 2015). It is important that modules are simple. Observer stress should be kept low to avoid an early dropout of the subject. Collecting more data is a healthy way of dealing with software/hardware diversity. Rigorously calibrating the user environment is tough (i.e. people fidgeting after distance test). It would be nice to test these modules and get data on error (Preliminary tests on distance measurements have been included below). However, there is potential for this project to grow into an open-source library.		Other modules are currently in development, allowing experiments to detect hue shift [4], saturating non-linearity [2] and color (“Coca-Cola solution” - C Bechlivanidis & DA Lagnado, pers.comm., 2015). It is important that modules are simple. Observer stress should be kept low to avoid an early dropout of the subject. Collecting more data is a healthy way of dealing with software/hardware diversity. Rigorously calibrating the user environment is tough (i.e. people fidgeting after distance test). It would be nice to test these modules and get data on error (Preliminary tests on distance measurements have been included below). However, there is potential for this project to grow into an open-source library.

	== ~~Experiments~~ ==		== Results ==

	We run a similar experiment to the one from [3] to understand the accuracy behind the blind spot distance detection. We asked 10 participants to run the program on a 13” (2019 MacBook) and 24” (p2419h Dell monitor). We measure seating distance, defined by “the distance between the center of the chair and the center of the display”, and ask them to use the computer as they usually would. We see that this method is suitable for rough estimates of distance, but an alternative test should be explored for more accuracy. The results found are recorded in the table here:		We run a similar experiment to the one from [3] to understand the accuracy behind the blind spot distance detection. We asked 10 participants to run the program on a 13” (2019 MacBook) and 24” (p2419h Dell monitor). We measure seating distance, defined by “the distance between the center of the chair and the center of the display”, and ask them to use the computer as they usually would. We see that this method is suitable for rough estimates of distance, but an alternative test should be explored for more accuracy. The results found are recorded in the table here:

imported>Student221 at 03:20, 5 December 2020

2020-12-05T03:20:55Z

← Older revision		Revision as of 03:20, 5 December 2020
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	[[File:blindexp.png\|500px]]		[[File:blindexp.png\|500px]]

	~~== Appendix I ==~~

	Code from this project is available here: [https://drive.google.com/file/d/1GLo_Zi1s8q3brni6tGInFt7hTQoYUosq/view?usp=sharing Google Drive]. A Stanford email is needed to access it. Modules will be moved to Github later.

	== References ==		== References ==
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	[4] I. Sprow, Z. Baranczuk, T. Stamm, and P. Zolliker, “Web-based psychometric evaluation of image quality,” Image Quality and System Performance VI, 2009.		[4] I. Sprow, Z. Baranczuk, T. Stamm, and P. Zolliker, “Web-based psychometric evaluation of image quality,” Image Quality and System Performance VI, 2009.

			== Appendix I ==

			Code from this project is available here: [https://drive.google.com/file/d/1GLo_Zi1s8q3brni6tGInFt7hTQoYUosq/view?usp=sharing Google Drive]. A Stanford email is needed to access it. Modules will be moved to Github later.

imported>Student221: /* Conclusions */

2020-12-05T03:20:33Z

Conclusions

← Older revision		Revision as of 03:20, 5 December 2020
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	[[File:blindexp.png\|500px]]		[[File:blindexp.png\|500px]]

	== ~~Conclusions~~ ==		== Appendix I ==

	Code from this project is available here: [https://drive.google.com/file/d/1GLo_Zi1s8q3brni6tGInFt7hTQoYUosq/view?usp=sharing Google Drive]. A Stanford email is needed to access it. Modules will be moved to Github later.		Code from this project is available here: [https://drive.google.com/file/d/1GLo_Zi1s8q3brni6tGInFt7hTQoYUosq/view?usp=sharing Google Drive]. A Stanford email is needed to access it. Modules will be moved to Github later.

imported>Student221: /* Experiments */

2020-12-05T01:24:49Z

Experiments

← Older revision		Revision as of 01:24, 5 December 2020
Line 54:		Line 54:
	== Experiments ==		== Experiments ==

	We run a similar experiment to the one from [3] to understand the accuracy behind the blind spot distance detection. We asked 10 participants to run the program on a 13” (2019 MacBook) and 24” (p2419h Dell monitor). We measure seating distance, defined by “the distance between the center of the chair and the center of the display”, and ask them to use the computer as they usually would. The results found are recorded in the table here:		We run a similar experiment to the one from [3] to understand the accuracy behind the blind spot distance detection. We asked 10 participants to run the program on a 13” (2019 MacBook) and 24” (p2419h Dell monitor). We measure seating distance, defined by “the distance between the center of the chair and the center of the display”, and ask them to use the computer as they usually would. We see that this method is suitable for rough estimates of distance, but an alternative test should be explored for more accuracy. The results found are recorded in the table here:

	[[File:blindexp.png\|500px]]		[[File:blindexp.png\|500px]]

imported>Student221: /* Conclusions */

2020-12-04T23:19:58Z

Conclusions

← Older revision		Revision as of 23:19, 4 December 2020
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	== Conclusions ==		== Conclusions ==

	Code from this project is available here: [https://drive.google.com/file/d/1GLo_Zi1s8q3brni6tGInFt7hTQoYUosq/view?usp=sharing][Google Drive]. A Stanford email is needed to access it. Modules will be moved to Github later.		Code from this project is available here: [https://drive.google.com/file/d/1GLo_Zi1s8q3brni6tGInFt7hTQoYUosq/view?usp=sharing Google Drive]. A Stanford email is needed to access it. Modules will be moved to Github later.

	== References ==		== References ==

imported>Student221: /* Conclusions */

2020-12-04T23:19:06Z

Conclusions

← Older revision		Revision as of 23:19, 4 December 2020
Line 59:		Line 59:

	== Conclusions ==		== Conclusions ==

			Code from this project is available here: [https://drive.google.com/file/d/1GLo_Zi1s8q3brni6tGInFt7hTQoYUosq/view?usp=sharing][Google Drive]. A Stanford email is needed to access it. Modules will be moved to Github later.

	== References ==		== References ==

imported>Student221 at 22:24, 4 December 2020

2020-12-04T22:24:24Z

← Older revision		Revision as of 22:24, 4 December 2020
Line 55:		Line 55:

	We run a similar experiment to the one from [3] to understand the accuracy behind the blind spot distance detection. We asked 10 participants to run the program on a 13” (2019 MacBook) and 24” (p2419h Dell monitor). We measure seating distance, defined by “the distance between the center of the chair and the center of the display”, and ask them to use the computer as they usually would. The results found are recorded in the table here:		We run a similar experiment to the one from [3] to understand the accuracy behind the blind spot distance detection. We asked 10 participants to run the program on a 13” (2019 MacBook) and 24” (p2419h Dell monitor). We measure seating distance, defined by “the distance between the center of the chair and the center of the display”, and ask them to use the computer as they usually would. The results found are recorded in the table here:

			[[File:blindexp.png\|500px]]

	== Conclusions ==		== Conclusions ==

imported>Student221: /* Results */

2020-12-04T22:23:53Z

Results

← Older revision		Revision as of 22:23, 4 December 2020
Line 52:		Line 52:
	Other modules are currently in development, allowing experiments to detect hue shift [4], saturating non-linearity [2] and color (“Coca-Cola solution” - C Bechlivanidis & DA Lagnado, pers.comm., 2015). It is important that modules are simple. Observer stress should be kept low to avoid an early dropout of the subject. Collecting more data is a healthy way of dealing with software/hardware diversity. Rigorously calibrating the user environment is tough (i.e. people fidgeting after distance test). It would be nice to test these modules and get data on error (Preliminary tests on distance measurements have been included below). However, there is potential for this project to grow into an open-source library.		Other modules are currently in development, allowing experiments to detect hue shift [4], saturating non-linearity [2] and color (“Coca-Cola solution” - C Bechlivanidis & DA Lagnado, pers.comm., 2015). It is important that modules are simple. Observer stress should be kept low to avoid an early dropout of the subject. Collecting more data is a healthy way of dealing with software/hardware diversity. Rigorously calibrating the user environment is tough (i.e. people fidgeting after distance test). It would be nice to test these modules and get data on error (Preliminary tests on distance measurements have been included below). However, there is potential for this project to grow into an open-source library.

	== ~~Results~~ ==		== Experiments ==

			We run a similar experiment to the one from [3] to understand the accuracy behind the blind spot distance detection. We asked 10 participants to run the program on a 13” (2019 MacBook) and 24” (p2419h Dell monitor). We measure seating distance, defined by “the distance between the center of the chair and the center of the display”, and ask them to use the computer as they usually would. The results found are recorded in the table here:

	== Conclusions ==		== Conclusions ==