YirongMengman - Revision history

imported>Student221: /* Method Validation */

2020-11-26T18:55:24Z

Method Validation

← Older revision		Revision as of 18:55, 26 November 2020
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	=== Method Validation ===		=== Method Validation ===
	We first try different integration time of cones and figure out a proper integration time for our further experiment. The parameters we use are listed in Table 1, besides the integration time. The location of the cone mosaic is at the fovea (0 degree). We have <math>149149</math> cones with size <math>2.02.0 um^2</math> each. The result is plot in Figure 5.		We first try different integration time of cones and figure out a proper integration time for our further experiment. The parameters we use are listed in Table 1, besides the integration time. The location of the cone mosaic is at the fovea (0 degree). We have <math>149149</math> cones with size <math>2.02.0 um^2</math> each. The result is plot in Figure 5.
	[[File:integrationTime1.png\|~~400x400px~~ \|center \| thumb \| Fig. 5: 0 degree CSF with different integration time]]		[[File:integrationTime1.png\|500x500px \|center \| thumb \| Fig. 5: 0 degree CSF with different integration time]]

	We can observe that the CSF curves with different integration time present similar trends and are almost parallel to each other. We would like to choose <math>10 ms</math> as our standard integration time in the further experiments.		We can observe that the CSF curves with different integration time present similar trends and are almost parallel to each other. We would like to choose <math>10 ms</math> as our standard integration time in the further experiments.

imported>Student221: /* Method Validation */

2020-11-26T18:55:09Z

Method Validation

← Older revision		Revision as of 18:55, 26 November 2020
Line 74:		Line 74:
	=== Method Validation ===		=== Method Validation ===
	We first try different integration time of cones and figure out a proper integration time for our further experiment. The parameters we use are listed in Table 1, besides the integration time. The location of the cone mosaic is at the fovea (0 degree). We have <math>149149</math> cones with size <math>2.02.0 um^2</math> each. The result is plot in Figure 5.		We first try different integration time of cones and figure out a proper integration time for our further experiment. The parameters we use are listed in Table 1, besides the integration time. The location of the cone mosaic is at the fovea (0 degree). We have <math>149149</math> cones with size <math>2.02.0 um^2</math> each. The result is plot in Figure 5.
	[[File:integrationTime1.png\|~~500x500px~~ \|center \| thumb \| Fig. 5: 0 degree CSF with different integration time]]		[[File:integrationTime1.png\|400x400px \|center \| thumb \| Fig. 5: 0 degree CSF with different integration time]]

	We can observe that the CSF curves with different integration time present similar trends and are almost parallel to each other. We would like to choose <math>10 ms</math> as our standard integration time in the further experiments.		We can observe that the CSF curves with different integration time present similar trends and are almost parallel to each other. We would like to choose <math>10 ms</math> as our standard integration time in the further experiments.

imported>Student221: /* Effects of Cone Distribution */

2020-11-26T18:54:21Z

Effects of Cone Distribution

← Older revision		Revision as of 18:54, 26 November 2020
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	After validation of our method, we start generating CSF from fovea to periphery. We use two different cone mosaic distributions with the same cone types density ratio, <math>L:M:S = 0.6:0.3:0.1</math>. The cone distributions are presented in Figure 7.		After validation of our method, we start generating CSF from fovea to periphery. We use two different cone mosaic distributions with the same cone types density ratio, <math>L:M:S = 0.6:0.3:0.1</math>. The cone distributions are presented in Figure 7.

	[[File:CM_dist.png\|~~1600x800px~~ \|center \| thumb \| Fig. 7: Cone distribution generated with random seed 1 (left) and random seed 2 (right)]]		[[File:CM_dist.png\|1000x500px \|center \| thumb \| Fig. 7: Cone distribution generated with random seed 1 (left) and random seed 2 (right)]]

	We compare the CSF curves of these two different cone mosaic distributions. The general parameters are listed in Table 1. The corresponding aperture size and number of cones in each mosaic view is listed in Table 2.		We compare the CSF curves of these two different cone mosaic distributions. The general parameters are listed in Table 1. The corresponding aperture size and number of cones in each mosaic view is listed in Table 2.

imported>Student221: /* Method Validation */

2020-11-26T18:53:41Z

Method Validation

← Older revision		Revision as of 18:53, 26 November 2020
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	Second, we use GRABIT[11] to obtain the CSF curves generated in [6] using ISETBio. Then we can compare the results of our method with that of the literature to validate our method. The parameters we used are listed in Table 1. We have <math>149149</math> cones with size <math>2.02.0 um^2</math> each. The result is presented in Figure 6. We use spline interpolation to generate the smooth curve connecting each data points.		Second, we use GRABIT[11] to obtain the CSF curves generated in [6] using ISETBio. Then we can compare the results of our method with that of the literature to validate our method. The parameters we used are listed in Table 1. We have <math>149149</math> cones with size <math>2.02.0 um^2</math> each. The result is presented in Figure 6. We use spline interpolation to generate the smooth curve connecting each data points.
	[[File:GRABIT_img.png\|1000x500px \|center \| thumb \| Fig. 6: 0 degree CSF, compare with CSFs in [6], the ratio between 0 degree CSF generated from Hypothesis test and Bank's 87 ideal observer]]		[[File:GRABIT_img.png\|1000x500px \|center \| thumb \| Fig. 6: 0 degree CSF, compare with CSFs in [6], the ratio between 0 degree CSF generated from Hypothesis test and Bank's 87 ideal observer]]
	[[File:parameter1.png\|~~925x375px~~ \|center \| thumb \| Table. 1: General parameters in Cone Mosaic creation]]		[[File:parameter1.png\|925x275px \|center \| thumb \| Table. 1: General parameters in Cone Mosaic creation]]

	We can observe that the CSF curve obtained by our Hypothesis test method has the same trend as that of the literature. However, from the ratio between 0 degree CSF generated from Hypothesis test and Bank's 87 ideal observer, we can find with increasing spatial frequency, the contrast sensitivity obtained using our method increases more than that of the literature. One reason is that in the literature, they use Gabor filter to process the test stimulus such that in each view there are same numbers of harmonic pattern cycles for different spatial frequency. In other words, for higher spatial frequency, the stimulus signal occupies less field of view, which makes it more difficult for people to detect.		We can observe that the CSF curve obtained by our Hypothesis test method has the same trend as that of the literature. However, from the ratio between 0 degree CSF generated from Hypothesis test and Bank's 87 ideal observer, we can find with increasing spatial frequency, the contrast sensitivity obtained using our method increases more than that of the literature. One reason is that in the literature, they use Gabor filter to process the test stimulus such that in each view there are same numbers of harmonic pattern cycles for different spatial frequency. In other words, for higher spatial frequency, the stimulus signal occupies less field of view, which makes it more difficult for people to detect.

imported>Student221: /* Method Validation */

2020-11-26T18:53:31Z

Method Validation

← Older revision		Revision as of 18:53, 26 November 2020
Line 80:		Line 80:
	Second, we use GRABIT[11] to obtain the CSF curves generated in [6] using ISETBio. Then we can compare the results of our method with that of the literature to validate our method. The parameters we used are listed in Table 1. We have <math>149149</math> cones with size <math>2.02.0 um^2</math> each. The result is presented in Figure 6. We use spline interpolation to generate the smooth curve connecting each data points.		Second, we use GRABIT[11] to obtain the CSF curves generated in [6] using ISETBio. Then we can compare the results of our method with that of the literature to validate our method. The parameters we used are listed in Table 1. We have <math>149149</math> cones with size <math>2.02.0 um^2</math> each. The result is presented in Figure 6. We use spline interpolation to generate the smooth curve connecting each data points.
	[[File:GRABIT_img.png\|1000x500px \|center \| thumb \| Fig. 6: 0 degree CSF, compare with CSFs in [6], the ratio between 0 degree CSF generated from Hypothesis test and Bank's 87 ideal observer]]		[[File:GRABIT_img.png\|1000x500px \|center \| thumb \| Fig. 6: 0 degree CSF, compare with CSFs in [6], the ratio between 0 degree CSF generated from Hypothesis test and Bank's 87 ideal observer]]
	[[File:parameter1.png\|~~925x475px~~ \|center \| thumb \| Table. 1: General parameters in Cone Mosaic creation]]		[[File:parameter1.png\|925x375px \|center \| thumb \| Table. 1: General parameters in Cone Mosaic creation]]

	We can observe that the CSF curve obtained by our Hypothesis test method has the same trend as that of the literature. However, from the ratio between 0 degree CSF generated from Hypothesis test and Bank's 87 ideal observer, we can find with increasing spatial frequency, the contrast sensitivity obtained using our method increases more than that of the literature. One reason is that in the literature, they use Gabor filter to process the test stimulus such that in each view there are same numbers of harmonic pattern cycles for different spatial frequency. In other words, for higher spatial frequency, the stimulus signal occupies less field of view, which makes it more difficult for people to detect.		We can observe that the CSF curve obtained by our Hypothesis test method has the same trend as that of the literature. However, from the ratio between 0 degree CSF generated from Hypothesis test and Bank's 87 ideal observer, we can find with increasing spatial frequency, the contrast sensitivity obtained using our method increases more than that of the literature. One reason is that in the literature, they use Gabor filter to process the test stimulus such that in each view there are same numbers of harmonic pattern cycles for different spatial frequency. In other words, for higher spatial frequency, the stimulus signal occupies less field of view, which makes it more difficult for people to detect.

imported>Student221: /* Method Validation */

2020-11-26T18:53:17Z

Method Validation

← Older revision		Revision as of 18:53, 26 November 2020
Line 80:		Line 80:
	Second, we use GRABIT[11] to obtain the CSF curves generated in [6] using ISETBio. Then we can compare the results of our method with that of the literature to validate our method. The parameters we used are listed in Table 1. We have <math>149149</math> cones with size <math>2.02.0 um^2</math> each. The result is presented in Figure 6. We use spline interpolation to generate the smooth curve connecting each data points.		Second, we use GRABIT[11] to obtain the CSF curves generated in [6] using ISETBio. Then we can compare the results of our method with that of the literature to validate our method. The parameters we used are listed in Table 1. We have <math>149149</math> cones with size <math>2.02.0 um^2</math> each. The result is presented in Figure 6. We use spline interpolation to generate the smooth curve connecting each data points.
	[[File:GRABIT_img.png\|1000x500px \|center \| thumb \| Fig. 6: 0 degree CSF, compare with CSFs in [6], the ratio between 0 degree CSF generated from Hypothesis test and Bank's 87 ideal observer]]		[[File:GRABIT_img.png\|1000x500px \|center \| thumb \| Fig. 6: 0 degree CSF, compare with CSFs in [6], the ratio between 0 degree CSF generated from Hypothesis test and Bank's 87 ideal observer]]
	[[File:parameter1.png\|~~950x475px~~ \|center \| thumb \| Table. 1: General parameters in Cone Mosaic creation]]		[[File:parameter1.png\|925x475px \|center \| thumb \| Table. 1: General parameters in Cone Mosaic creation]]

	We can observe that the CSF curve obtained by our Hypothesis test method has the same trend as that of the literature. However, from the ratio between 0 degree CSF generated from Hypothesis test and Bank's 87 ideal observer, we can find with increasing spatial frequency, the contrast sensitivity obtained using our method increases more than that of the literature. One reason is that in the literature, they use Gabor filter to process the test stimulus such that in each view there are same numbers of harmonic pattern cycles for different spatial frequency. In other words, for higher spatial frequency, the stimulus signal occupies less field of view, which makes it more difficult for people to detect.		We can observe that the CSF curve obtained by our Hypothesis test method has the same trend as that of the literature. However, from the ratio between 0 degree CSF generated from Hypothesis test and Bank's 87 ideal observer, we can find with increasing spatial frequency, the contrast sensitivity obtained using our method increases more than that of the literature. One reason is that in the literature, they use Gabor filter to process the test stimulus such that in each view there are same numbers of harmonic pattern cycles for different spatial frequency. In other words, for higher spatial frequency, the stimulus signal occupies less field of view, which makes it more difficult for people to detect.

imported>Student221: /* Method Validation */

2020-11-26T18:53:09Z

Method Validation

← Older revision		Revision as of 18:53, 26 November 2020
Line 80:		Line 80:
	Second, we use GRABIT[11] to obtain the CSF curves generated in [6] using ISETBio. Then we can compare the results of our method with that of the literature to validate our method. The parameters we used are listed in Table 1. We have <math>149149</math> cones with size <math>2.02.0 um^2</math> each. The result is presented in Figure 6. We use spline interpolation to generate the smooth curve connecting each data points.		Second, we use GRABIT[11] to obtain the CSF curves generated in [6] using ISETBio. Then we can compare the results of our method with that of the literature to validate our method. The parameters we used are listed in Table 1. We have <math>149149</math> cones with size <math>2.02.0 um^2</math> each. The result is presented in Figure 6. We use spline interpolation to generate the smooth curve connecting each data points.
	[[File:GRABIT_img.png\|1000x500px \|center \| thumb \| Fig. 6: 0 degree CSF, compare with CSFs in [6], the ratio between 0 degree CSF generated from Hypothesis test and Bank's 87 ideal observer]]		[[File:GRABIT_img.png\|1000x500px \|center \| thumb \| Fig. 6: 0 degree CSF, compare with CSFs in [6], the ratio between 0 degree CSF generated from Hypothesis test and Bank's 87 ideal observer]]
	[[File:parameter1.png\|~~900x450px~~ \|center \| thumb \| Table. 1: General parameters in Cone Mosaic creation]]		[[File:parameter1.png\|950x475px \|center \| thumb \| Table. 1: General parameters in Cone Mosaic creation]]

	We can observe that the CSF curve obtained by our Hypothesis test method has the same trend as that of the literature. However, from the ratio between 0 degree CSF generated from Hypothesis test and Bank's 87 ideal observer, we can find with increasing spatial frequency, the contrast sensitivity obtained using our method increases more than that of the literature. One reason is that in the literature, they use Gabor filter to process the test stimulus such that in each view there are same numbers of harmonic pattern cycles for different spatial frequency. In other words, for higher spatial frequency, the stimulus signal occupies less field of view, which makes it more difficult for people to detect.		We can observe that the CSF curve obtained by our Hypothesis test method has the same trend as that of the literature. However, from the ratio between 0 degree CSF generated from Hypothesis test and Bank's 87 ideal observer, we can find with increasing spatial frequency, the contrast sensitivity obtained using our method increases more than that of the literature. One reason is that in the literature, they use Gabor filter to process the test stimulus such that in each view there are same numbers of harmonic pattern cycles for different spatial frequency. In other words, for higher spatial frequency, the stimulus signal occupies less field of view, which makes it more difficult for people to detect.

imported>Student221: /* Method Validation */

2020-11-26T18:52:56Z

Method Validation

← Older revision		Revision as of 18:52, 26 November 2020
Line 80:		Line 80:
	Second, we use GRABIT[11] to obtain the CSF curves generated in [6] using ISETBio. Then we can compare the results of our method with that of the literature to validate our method. The parameters we used are listed in Table 1. We have <math>149149</math> cones with size <math>2.02.0 um^2</math> each. The result is presented in Figure 6. We use spline interpolation to generate the smooth curve connecting each data points.		Second, we use GRABIT[11] to obtain the CSF curves generated in [6] using ISETBio. Then we can compare the results of our method with that of the literature to validate our method. The parameters we used are listed in Table 1. We have <math>149149</math> cones with size <math>2.02.0 um^2</math> each. The result is presented in Figure 6. We use spline interpolation to generate the smooth curve connecting each data points.
	[[File:GRABIT_img.png\|1000x500px \|center \| thumb \| Fig. 6: 0 degree CSF, compare with CSFs in [6], the ratio between 0 degree CSF generated from Hypothesis test and Bank's 87 ideal observer]]		[[File:GRABIT_img.png\|1000x500px \|center \| thumb \| Fig. 6: 0 degree CSF, compare with CSFs in [6], the ratio between 0 degree CSF generated from Hypothesis test and Bank's 87 ideal observer]]
	[[File:parameter1.png\|~~1000x500px~~ \|center \| thumb \| Table. 1: General parameters in Cone Mosaic creation]]		[[File:parameter1.png\|900x450px \|center \| thumb \| Table. 1: General parameters in Cone Mosaic creation]]

	We can observe that the CSF curve obtained by our Hypothesis test method has the same trend as that of the literature. However, from the ratio between 0 degree CSF generated from Hypothesis test and Bank's 87 ideal observer, we can find with increasing spatial frequency, the contrast sensitivity obtained using our method increases more than that of the literature. One reason is that in the literature, they use Gabor filter to process the test stimulus such that in each view there are same numbers of harmonic pattern cycles for different spatial frequency. In other words, for higher spatial frequency, the stimulus signal occupies less field of view, which makes it more difficult for people to detect.		We can observe that the CSF curve obtained by our Hypothesis test method has the same trend as that of the literature. However, from the ratio between 0 degree CSF generated from Hypothesis test and Bank's 87 ideal observer, we can find with increasing spatial frequency, the contrast sensitivity obtained using our method increases more than that of the literature. One reason is that in the literature, they use Gabor filter to process the test stimulus such that in each view there are same numbers of harmonic pattern cycles for different spatial frequency. In other words, for higher spatial frequency, the stimulus signal occupies less field of view, which makes it more difficult for people to detect.

imported>Student221: /* Method Validation */

2020-11-26T18:52:44Z

Method Validation

← Older revision		Revision as of 18:52, 26 November 2020
Line 80:		Line 80:
	Second, we use GRABIT[11] to obtain the CSF curves generated in [6] using ISETBio. Then we can compare the results of our method with that of the literature to validate our method. The parameters we used are listed in Table 1. We have <math>149149</math> cones with size <math>2.02.0 um^2</math> each. The result is presented in Figure 6. We use spline interpolation to generate the smooth curve connecting each data points.		Second, we use GRABIT[11] to obtain the CSF curves generated in [6] using ISETBio. Then we can compare the results of our method with that of the literature to validate our method. The parameters we used are listed in Table 1. We have <math>149149</math> cones with size <math>2.02.0 um^2</math> each. The result is presented in Figure 6. We use spline interpolation to generate the smooth curve connecting each data points.
	[[File:GRABIT_img.png\|1000x500px \|center \| thumb \| Fig. 6: 0 degree CSF, compare with CSFs in [6], the ratio between 0 degree CSF generated from Hypothesis test and Bank's 87 ideal observer]]		[[File:GRABIT_img.png\|1000x500px \|center \| thumb \| Fig. 6: 0 degree CSF, compare with CSFs in [6], the ratio between 0 degree CSF generated from Hypothesis test and Bank's 87 ideal observer]]
	[[File:parameter1.png\|~~500x250px~~ \|center \| thumb \| Table. 1: General parameters in Cone Mosaic creation]]		[[File:parameter1.png\|1000x500px \|center \| thumb \| Table. 1: General parameters in Cone Mosaic creation]]

	We can observe that the CSF curve obtained by our Hypothesis test method has the same trend as that of the literature. However, from the ratio between 0 degree CSF generated from Hypothesis test and Bank's 87 ideal observer, we can find with increasing spatial frequency, the contrast sensitivity obtained using our method increases more than that of the literature. One reason is that in the literature, they use Gabor filter to process the test stimulus such that in each view there are same numbers of harmonic pattern cycles for different spatial frequency. In other words, for higher spatial frequency, the stimulus signal occupies less field of view, which makes it more difficult for people to detect.		We can observe that the CSF curve obtained by our Hypothesis test method has the same trend as that of the literature. However, from the ratio between 0 degree CSF generated from Hypothesis test and Bank's 87 ideal observer, we can find with increasing spatial frequency, the contrast sensitivity obtained using our method increases more than that of the literature. One reason is that in the literature, they use Gabor filter to process the test stimulus such that in each view there are same numbers of harmonic pattern cycles for different spatial frequency. In other words, for higher spatial frequency, the stimulus signal occupies less field of view, which makes it more difficult for people to detect.

imported>Student221: /* Code and results file data */

2020-11-26T18:52:16Z

Code and results file data

← Older revision		Revision as of 18:52, 26 November 2020
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	== Appendix ==		== Appendix ==

	=== Code and ~~results~~ file data ===		=== Code and Results file data ===

	All the code and results file data can be download here: [https://drive.google.com/file/d/1yw4sLrPOdzNFZ6pPHTFCFTSywkHiozKk/view?usp=sharing CSFModel.zip]		All the code and results file data can be download here: [https://drive.google.com/file/d/1yw4sLrPOdzNFZ6pPHTFCFTSywkHiozKk/view?usp=sharing CSFModel.zip]