Hypercube Waveband Registration - Revision history

imported>Psych2012: /* Appendix II */

2012-03-23T23:36:07Z

Appendix II

← Older revision		Revision as of 23:36, 23 March 2012
Line 119:		Line 119:
	A majority of the project was carried out by Steve Dai, with contributions from Qiyuan Tian on the understanding of the algorithm, testing methodology, and related discussions.		A majority of the project was carried out by Steve Dai, with contributions from Qiyuan Tian on the understanding of the algorithm, testing methodology, and related discussions.

	Link to my group's other project: [http://white.stanford.edu/teach/index.php/TienDaiIFHDR A Versatile Image Fusion Method]<br>		Link to our group's other project: [http://white.stanford.edu/teach/index.php/TienDaiIFHDR A Versatile Image Fusion Method]<br>

imported>Psych2012: /* Appendix II */

2012-03-23T23:35:48Z

Appendix II

← Older revision		Revision as of 23:35, 23 March 2012
Line 118:		Line 118:
	= Appendix II =		= Appendix II =
	A majority of the project was carried out by Steve Dai, with contributions from Qiyuan Tian on the understanding of the algorithm, testing methodology, and related discussions.		A majority of the project was carried out by Steve Dai, with contributions from Qiyuan Tian on the understanding of the algorithm, testing methodology, and related discussions.

	Link to my group's other project: [http://white.stanford.edu/teach/index.php/TienDaiIFHDR A Versatile Image Fusion Method]<br>		Link to my group's other project: [http://white.stanford.edu/teach/index.php/TienDaiIFHDR A Versatile Image Fusion Method]<br>

imported>Psych2012: /* Appendix II */

2012-03-23T23:35:20Z

Appendix II

← Older revision		Revision as of 23:35, 23 March 2012
Line 118:		Line 118:
	= Appendix II =		= Appendix II =
	A majority of the project was carried out by Steve Dai, with contributions from Qiyuan Tian on the understanding of the algorithm, testing methodology, and related discussions.		A majority of the project was carried out by Steve Dai, with contributions from Qiyuan Tian on the understanding of the algorithm, testing methodology, and related discussions.
			Link to my group's other project: [http://white.stanford.edu/teach/index.php/TienDaiIFHDR A Versatile Image Fusion Method]<br>

imported>Psych2012: /* Conclusions */

2012-03-22T07:16:08Z

Conclusions

← Older revision		Revision as of 07:16, 22 March 2012
Line 99:		Line 99:
	What was most surprising is the misalignment between groups of scan lines, and on top of that, the lack of misalignment as a function across the field of view of the camera. If this phenomenon is in fact true and verified, it suggests that a possible solution of registration would involve a combination of both feature and area-based methods. One intuitive approach would be use mutual information as a feature matching algorithm in cases where feature points cannot be readily extracted. Mutual information would identify corresponding patches of the two images to be registered, and using the coordinates of these patches we can compute either an affine or projective transformation matrix that maps in between the two images.		What was most surprising is the misalignment between groups of scan lines, and on top of that, the lack of misalignment as a function across the field of view of the camera. If this phenomenon is in fact true and verified, it suggests that a possible solution of registration would involve a combination of both feature and area-based methods. One intuitive approach would be use mutual information as a feature matching algorithm in cases where feature points cannot be readily extracted. Mutual information would identify corresponding patches of the two images to be registered, and using the coordinates of these patches we can compute either an affine or projective transformation matrix that maps in between the two images.

	As I continue this project, the next step would be to add a stochastic gradient optimization to the registration algorithm to efficiently search for the optimal misregistration offset. With this optimization on-hand, we can then explore ways to implement combined feature and area-based methods. Of course, we would also re-run the experiments previously discussed on this page with the newly extracted data to verify the integrity of our ~~conclusion~~.		As I continue this project, the next step would be to add a stochastic gradient optimization to the registration algorithm to efficiently search for the optimal misregistration offset. With this optimization on-hand, we can then explore ways to implement combined feature and area-based methods. Of course, we would also re-run the experiments previously discussed on this page with the newly extracted data to verify the integrity of our results.

	= References =		= References =

imported>Psych2012: /* Conclusions */

2012-03-22T07:15:56Z

Conclusions

← Older revision		Revision as of 07:15, 22 March 2012
Line 99:		Line 99:
	What was most surprising is the misalignment between groups of scan lines, and on top of that, the lack of misalignment as a function across the field of view of the camera. If this phenomenon is in fact true and verified, it suggests that a possible solution of registration would involve a combination of both feature and area-based methods. One intuitive approach would be use mutual information as a feature matching algorithm in cases where feature points cannot be readily extracted. Mutual information would identify corresponding patches of the two images to be registered, and using the coordinates of these patches we can compute either an affine or projective transformation matrix that maps in between the two images.		What was most surprising is the misalignment between groups of scan lines, and on top of that, the lack of misalignment as a function across the field of view of the camera. If this phenomenon is in fact true and verified, it suggests that a possible solution of registration would involve a combination of both feature and area-based methods. One intuitive approach would be use mutual information as a feature matching algorithm in cases where feature points cannot be readily extracted. Mutual information would identify corresponding patches of the two images to be registered, and using the coordinates of these patches we can compute either an affine or projective transformation matrix that maps in between the two images.

	As I continue this project, the next step would be to add a stochastic gradient optimization to the registration algorithm to efficiently search for the optimal misregistration offset. With this optimization on-hand, we can then explore ways to implement combined feature and area-based methods.		As I continue this project, the next step would be to add a stochastic gradient optimization to the registration algorithm to efficiently search for the optimal misregistration offset. With this optimization on-hand, we can then explore ways to implement combined feature and area-based methods. Of course, we would also re-run the experiments previously discussed on this page with the newly extracted data to verify the integrity of our conclusion.

	= References =		= References =

imported>Psych2012: /* Conclusions */

2012-03-22T07:13:31Z

Conclusions

← Older revision		Revision as of 07:13, 22 March 2012
Line 96:		Line 96:

	In using mutual information for waveband registration, we found that the spectral bands align well within each camera, and most misregistrations across the two cameras are on the scale of only a few pixels. As an extension of this project, we can take advantage of the difference in spatial resolution between the VNIR and SWIR cameras to detect any sub-pixel misregistration.		In using mutual information for waveband registration, we found that the spectral bands align well within each camera, and most misregistrations across the two cameras are on the scale of only a few pixels. As an extension of this project, we can take advantage of the difference in spatial resolution between the VNIR and SWIR cameras to detect any sub-pixel misregistration.

			What was most surprising is the misalignment between groups of scan lines, and on top of that, the lack of misalignment as a function across the field of view of the camera. If this phenomenon is in fact true and verified, it suggests that a possible solution of registration would involve a combination of both feature and area-based methods. One intuitive approach would be use mutual information as a feature matching algorithm in cases where feature points cannot be readily extracted. Mutual information would identify corresponding patches of the two images to be registered, and using the coordinates of these patches we can compute either an affine or projective transformation matrix that maps in between the two images.

			As I continue this project, the next step would be to add a stochastic gradient optimization to the registration algorithm to efficiently search for the optimal misregistration offset. With this optimization on-hand, we can then explore ways to implement combined feature and area-based methods.

	= References =		= References =

imported>Psych2012: /* Appendix II */

2012-03-22T06:58:19Z

Appendix II

← Older revision		Revision as of 06:58, 22 March 2012
Line 113:		Line 113:

	= Appendix II =		= Appendix II =
	~~- Work partition (if a group~~ project)		A majority of the project was carried out by Steve Dai, with contributions from Qiyuan Tian on the understanding of the algorithm, testing methodology, and related discussions.

imported>Psych2012: /* Conclusions */

2012-03-22T06:54:57Z

Conclusions

← Older revision		Revision as of 06:54, 22 March 2012
Line 93:		Line 93:

	= Conclusions =		= Conclusions =
	- ~~What it means~~		Mutual information is a powerful measure for measuring the similarity between two images or two spectral bands in the context of waveband registration. It is based on the dependency and redundancy of pixel intensities for the two images. It is robust to noise and works even if only a small portion of the two images are similar. Note that the images must be large enough to avoid a sparsely populated histogram in the computation of mutual information.

			In using mutual information for waveband registration, we found that the spectral bands align well within each camera, and most misregistrations across the two cameras are on the scale of only a few pixels. As an extension of this project, we can take advantage of the difference in spatial resolution between the VNIR and SWIR cameras to detect any sub-pixel misregistration.

	= References =		= References =
	# [http://www.hyspex.no/products/hyspex/allspecs.php HySpex Main Specifications]. Norsk Elektro Optikk A/S (NEO).		# [http://www.hyspex.no/products/hyspex/allspecs.php HySpex Main Specifications]. Norsk Elektro Optikk A/S (NEO).

imported>Psych2012: /* Scan Line Variations */

2012-03-22T06:44:43Z

Scan Line Variations

← Older revision		Revision as of 06:44, 22 March 2012
Line 87:		Line 87:
	It is also interesting to look at the registration between bands within different groups of scan lines and examine if there is a systematic variation from one end of the the field of view to the other. This amounts to scanning from the top of our image to the bottom and looking at the euclidean offset as a function of scan line. Scan line variations can be analyzed in both the unimodal and bimodal cases. Here we consider groups of 100 scan lines.		It is also interesting to look at the registration between bands within different groups of scan lines and examine if there is a systematic variation from one end of the the field of view to the other. This amounts to scanning from the top of our image to the bottom and looking at the euclidean offset as a function of scan line. Scan line variations can be analyzed in both the unimodal and bimodal cases. Here we consider groups of 100 scan lines.

	For the unimodal case, we scan and compare between the first and last VNIR band. The former has the lowest SNR in VNIR, and the latter has the highest. We also scan and compare between the 58th and last SWIR band. The former has the highest SNR in SWIR, and the latter has the lowest. The results shown below were a little surprising. They tell us that even though the entire image registers well as a whole, corresponding groups of scan lines do not. Moreover, it is difficult to spot any patterns on the plot, and there is no evidence of bias in terms of spatial location toward any scan line groups. Looking at the euclidean offset, it seems that group misregistration is more sever in SWIR than in VNIR. Otherwise, everything else seems sudo-random. The results shown here deserve further investigation.		For the unimodal case, we scan and compare between the first and last VNIR band. The former has the lowest SNR in VNIR, and the latter has the highest. We also scan and compare between the 58th and last SWIR band. The former has the highest SNR in SWIR, and the latter has the lowest. The results shown below were a little surprising. They tell us that even though the entire image registers well as a whole, corresponding groups of scan lines do not. Moreover, it is difficult to spot any patterns on the plot, and there is no evidence of bias in terms of spatial location toward any scan line groups. Looking at the euclidean offset, it seems that group misregistration is more sever in SWIR than in VNIR. Otherwise, everything else seems sudo-random. The results shown here deserve further investigation. With the availability of the newly extracted data, we are hopeful that we will get some results that are more inline with the physical specifications of the cameras.

	<center>[[File:VNIRScanLines.png \|900px]]</center>		<center>[[File:VNIRScanLines.png \|900px]]</center>

imported>Psych2012: /* Bimodal Waveband Registration */

2012-03-22T06:41:27Z

Bimodal Waveband Registration

← Older revision		Revision as of 06:41, 22 March 2012
Line 81:		Line 81:

	<center>[[File:BimodalRegistration.png \|900px]]</center>		<center>[[File:BimodalRegistration.png \|900px]]</center>

			We noted that there is a spike in misregistration around spectral of index of 38 and attempted to analyze how these bands are special compared to others. So far we have not been able to explain this phenomenon. Because there is also uncertainty in the inherent misregistration between the two cameras, data obtained in these plots have been called into question. That's why we have collected data in a more accurate manner from the raw hyperspectral data file for re-running this experiment.

	== Scan Line Variations ==		== Scan Line Variations ==