Okkeun Lee - Revision history

imported>Joyce: /* Methods */

2020-12-05T01:34:18Z

Methods

← Older revision		Revision as of 01:34, 5 December 2020
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	== Methods ==		== Methods ==

	In order to determine the CSF for various spatial frequency, we have used the readily available tool from the MATALB called The Image System Engineering Toolbox for Biology (ISETBio). The ISETBio ~~is sold by Imageval Consulting, LLC and~~ is available as an open source project [10]. The toolbox is primarily used to design image sensors as well as for modeling image formation in biological systems and thus used widely for commercial and research purposes [11].		In order to determine the CSF for various spatial frequency, we have used the readily available tool from the MATALB called The Image System Engineering Toolbox for Biology (ISETBio). The ISETBio is available as an open source project [10]. The toolbox is primarily used to design image sensors as well as for modeling image formation in biological systems and thus used widely for commercial and research purposes [11].

	We have used ISETBio first to simulate the cone photoreceptor absorptions, which is the first stage processing in human vision and thus the CSF to some extent is determined by the photoreceptors. One of the factors that still remains unclear is the relation between size of the photoreceptors and the CSF as they are small in the fovea and much larger in the periphery . In order to include the cone mosaic distribution factors into account, we generated two portions of the retina- the center of the fovea, and slightly to the periphery using the “coneMosaic” method in ISETBio. We computed the CSF for six spatial frequencies from 1 to 1.5 in log space. We also varied the integration time in the retina as a factor in order to know how the integration factor affects the CSF. For each of the frequency, we generated a Gabor patch stimulus and obtained the visual response using the toolbox. The stimulus responses between the null and the Gabor patch stimulus was discriminated by using the inference engines from machine learning. In particular, we were interested to compare the performance of both the support vector machines (SVM) and k-nearest neighbours (kNN) algorithm in terms of their role in inferring the CSF.		We have used ISETBio first to simulate the cone photoreceptor absorptions, which is the first stage processing in human vision and thus the CSF to some extent is determined by the photoreceptors. One of the factors that still remains unclear is the relation between size of the photoreceptors and the CSF as they are small in the fovea and much larger in the periphery . In order to include the cone mosaic distribution factors into account, we generated two portions of the retina- the center of the fovea, and slightly to the periphery using the “coneMosaic” method in ISETBio. We computed the CSF for six spatial frequencies from 1 to 1.5 in log space. We also varied the integration time in the retina as a factor in order to know how the integration factor affects the CSF. For each of the frequency, we generated a Gabor patch stimulus and obtained the visual response using the toolbox. The stimulus responses between the null and the Gabor patch stimulus was discriminated by using the inference engines from machine learning. In particular, we were interested to compare the performance of both the support vector machines (SVM) and k-nearest neighbours (kNN) algorithm in terms of their role in inferring the CSF.

imported>Student221: /* Appendix */

2020-12-04T23:38:43Z

Appendix

← Older revision		Revision as of 23:38, 4 December 2020
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	== Appendix ==		== Appendix ==

	The codes for this project can be found ~~at this~~ link [[Code_okkeun.zip]]		The codes for this project can be found on the following link [[Code_okkeun.zip]]

imported>Student221: /* Appendix */

2020-12-04T23:37:51Z

Appendix

← Older revision		Revision as of 23:37, 4 December 2020
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	== Appendix ==		== Appendix ==

	[[~~File:~~Code_okkeun.zip]]		The codes for this project can be found at this link [[Code_okkeun.zip]]

imported>Student221: /* Appendix */

2020-12-04T23:31:18Z

Appendix

← Older revision		Revision as of 23:31, 4 December 2020
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	== Appendix ==		== Appendix ==

	[[File:~~Code~~.zip]]		[[File:Code_okkeun.zip]]

imported>Student221: /* Appendix */

2020-12-04T23:31:02Z

Appendix

← Older revision		Revision as of 23:31, 4 December 2020
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	== Appendix ==		== Appendix ==

	[[File:Code]]		[[File:Code.zip]]

imported>Student221 at 23:30, 4 December 2020

2020-12-04T23:30:12Z

← Older revision		Revision as of 23:30, 4 December 2020
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	[12] Cortes, Corinna; Vapnik, Vladimir N. (1995). "Support-vector networks". Machine Learning. 20 (3): 273–297.		[12] Cortes, Corinna; Vapnik, Vladimir N. (1995). "Support-vector networks". Machine Learning. 20 (3): 273–297.


			== Appendix ==

			[[File:Code]]

imported>Student221: /* Results */

2020-12-04T22:25:06Z

Results

← Older revision		Revision as of 22:25, 4 December 2020
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	== Results ==		== Results ==

	In our experiments, we vary the integration time of the cones and determined the CSF for the fixed location of the cone mosaic, that is at the fovea. The size of the cones mosaic is set at 149 X 149 with each pixel 2X2 um2 size. Figures 4 and 5 indicate the effect of integration time on the CSF. We observe from the ~~figure~~ that we obtained the desired result for all the integration times except 60 msec for the kNN ~~where~~ the CSF declines towards the higher spatial frequency as expected. ~~For~~ the other ~~integration times~~, ~~our result does not show the proper CSF~~ using the SVM. We suspect ~~there~~ could be some bugs in the use of SVM classifier or the contrast detection. We are in the process of debugging these results. Also, we could not see the exact role of integration times just from the ~~figure~~, which we also would like to improve in the future.		In our experiments, we vary the integration time of the cones and determined the CSF for the fixed location of the cone mosaic, that is at the fovea. The size of the cones mosaic is set at 149 X 149 with each pixel 2X2 um2 size. Figures 4 and 5 indicate the effect of integration time on the CSF. We observe from the Fig. 4 that we obtained the desired result for all the integration times except 60 msec for the kNN. That is, the CSF declines towards the higher spatial frequency as expected ( the overshoots are the spline interpolation artefacts and should be ignored). On the other hand, using the SVM inference engine, we were unable to detect such pattern except for the integration times 10 and 20 msec. We suspect that the potential problem could be some bugs in the use of SVM classifier or the contrast detection. We are in the process of debugging these results. Also, we could not see the exact role of integration times just from the Fig 5. That is, while in kNN it is clear that as the integration time increases the CSF increases and such pattern is not witness in the SVM graph, which we also would like to improve in the future.

	[[File:Fig5_okkeun_kNN.png]]		[[File:Fig5_okkeun_kNN.png]]

imported>Student221: /* Results */

2020-12-04T22:19:20Z

Results

← Older revision		Revision as of 22:19, 4 December 2020
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	== Results ==		== Results ==

	In our experiments, we vary the integration time of the cones and determined the CSF for the fixed location of the cone mosaic, that is at the fovea. The size of the cones mosaic is set at 149 X 149 with each pixel 2X2 um2 size. Figures 4 and 5 indicate the effect of integration time on the CSF. We observe from the figure that we obtained the desired result for the integration times ~~of 10 and 20msec~~ where the CSF declines towards the higher spatial frequency as expected. For the other integration times, our result does not show the proper CSF using the SVM. We suspect there could be some bugs in the use of SVM classifier or the contrast detection. We are in the process of debugging these results. Also, we could not see the exact role of integration times just from the figure, which we also would like to improve in the future.		In our experiments, we vary the integration time of the cones and determined the CSF for the fixed location of the cone mosaic, that is at the fovea. The size of the cones mosaic is set at 149 X 149 with each pixel 2X2 um2 size. Figures 4 and 5 indicate the effect of integration time on the CSF. We observe from the figure that we obtained the desired result for all the integration times except 60 msec for the kNN where the CSF declines towards the higher spatial frequency as expected. For the other integration times, our result does not show the proper CSF using the SVM. We suspect there could be some bugs in the use of SVM classifier or the contrast detection. We are in the process of debugging these results. Also, we could not see the exact role of integration times just from the figure, which we also would like to improve in the future.

	[[File:~~Fig4_okkeun_SVM~~.png]]		[[File:Fig5_okkeun_kNN.png]]

	Fig. 4. Plot of CSF vs. spatial frequency for various integration times using ~~SVM binary~~ classifier		Fig. 4. Plot of CSF vs. spatial frequency for various integration times using kNN classifier

	[[File:~~Fig5_okkeun_kNN~~.png]]		[[File:Fig4_okkeun_SVM.png]]

	Fig. 5. Plot of CSF vs. spatial frequency for various integration times using ~~kNN~~ classifier		Fig. 5. Plot of CSF vs. spatial frequency for various integration times using SVM binary classifier

	== Conclusions ==		== Conclusions ==

imported>Student221: /* Results */

2020-12-04T22:16:21Z

Results

← Older revision		Revision as of 22:16, 4 December 2020
Line 38:		Line 38:
	== Results ==		== Results ==

	In our experiments, we vary the integration time of the cones and determined the CSF for the fixed location of the cone mosaic, that is at the fovea. The size of the cones mosaic is set at 149 X 149 with each pixel 2X2 um2 size. ~~Figure indicates~~ the effect of integration time on the CSF. We observe from the figure that we obtained the desired result for the integration times of 10 and 20msec where the CSF declines towards the higher spatial frequency as expected. For the other integration times, our result does not show the proper CSF using the SVM. We suspect there could be some bugs in the use of SVM classifier or the contrast detection. We are in the process of debugging these results. Also, we could not see the exact role of integration times just from the figure, which we also would like to improve in the future.		In our experiments, we vary the integration time of the cones and determined the CSF for the fixed location of the cone mosaic, that is at the fovea. The size of the cones mosaic is set at 149 X 149 with each pixel 2X2 um2 size. Figures 4 and 5 indicate the effect of integration time on the CSF. We observe from the figure that we obtained the desired result for the integration times of 10 and 20msec where the CSF declines towards the higher spatial frequency as expected. For the other integration times, our result does not show the proper CSF using the SVM. We suspect there could be some bugs in the use of SVM classifier or the contrast detection. We are in the process of debugging these results. Also, we could not see the exact role of integration times just from the figure, which we also would like to improve in the future.

	[[File:Fig4_okkeun_SVM.png]]		[[File:Fig4_okkeun_SVM.png]]

imported>Student221: /* Methods */

2020-12-04T22:13:28Z

Methods

← Older revision		Revision as of 22:13, 4 December 2020
Line 25:		Line 25:
	== Methods ==		== Methods ==

	In order to determine the CSF for various spatial frequency, we have used the readily available tool from the MATALB called The Image System Engineering Toolbox for Biology (ISETBio). The ISETBio is sold by Imageval Consulting, LLC and available as an open source project [10]. The toolbox is primarily used to design image sensors as well as for modeling image formation in biological systems and thus used widely for commercial and research purposes [11].		In order to determine the CSF for various spatial frequency, we have used the readily available tool from the MATALB called The Image System Engineering Toolbox for Biology (ISETBio). The ISETBio is sold by Imageval Consulting, LLC and is available as an open source project [10]. The toolbox is primarily used to design image sensors as well as for modeling image formation in biological systems and thus used widely for commercial and research purposes [11].

	We have used ISETBio first to simulate the cone photoreceptor absorptions, which is the first stage processing in human vision and thus the CSF to some extent is determined by the photoreceptors. One of the factors that still remains unclear is the relation between size of the photoreceptors and the CSF as they are small in the fovea and much larger in the periphery . In order to include the cone mosaic distribution factors into account, we generated two portions of the retina- the center of the fovea, and slightly to the periphery using the “coneMosaic” method in ISETBio. We computed the CSF for six spatial frequencies from 1 to 1.5 in log space. We also varied the integration time in the retina as a factor in order to know how the integration factor affects the CSF. For each of the frequency, we generated a Gabor patch stimulus and obtained the visual response using the toolbox. The stimulus responses between the null and the Gabor patch stimulus was discriminated by using the inference engines from machine learning. In particular, we were interested to compare the performance of both the support vector machines (SVM) and k-nearest neighbours (kNN) algorithm in terms of their role in inferring the CSF.		We have used ISETBio first to simulate the cone photoreceptor absorptions, which is the first stage processing in human vision and thus the CSF to some extent is determined by the photoreceptors. One of the factors that still remains unclear is the relation between size of the photoreceptors and the CSF as they are small in the fovea and much larger in the periphery . In order to include the cone mosaic distribution factors into account, we generated two portions of the retina- the center of the fovea, and slightly to the periphery using the “coneMosaic” method in ISETBio. We computed the CSF for six spatial frequencies from 1 to 1.5 in log space. We also varied the integration time in the retina as a factor in order to know how the integration factor affects the CSF. For each of the frequency, we generated a Gabor patch stimulus and obtained the visual response using the toolbox. The stimulus responses between the null and the Gabor patch stimulus was discriminated by using the inference engines from machine learning. In particular, we were interested to compare the performance of both the support vector machines (SVM) and k-nearest neighbours (kNN) algorithm in terms of their role in inferring the CSF.