Non-line of Sight imaging by SPAD - Revision history

imported>Student2016: /* Imaging example of SPAD */

2016-12-16T20:02:36Z

Imaging example of SPAD

← Older revision		Revision as of 20:02, 16 December 2016
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	One imaging example of the SPAD can be the object behind the blind. Normal imaging detector would have no way to capture this man behind the blind, and the information they obtain would be like the left hump in the figure below. However some photons pass through the blind and then gets reflected off the object. They form the second smaller hump. And the man can be imaged.		One imaging example of the SPAD can be the object behind the blind. Normal imaging detector would have no way to capture this man behind the blind, and the information they obtain would be like the left hump in the figure below. However some photons pass through the blind and then gets reflected off the object. They form the second smaller hump. And the man can be imaged.
	[[File:imaging.png\|500px\|thumb\|left\|figure 2. photon stistics vs. time]]		[[File:imaging.png\|500px\|thumb\|left\|figure 2. photon stistics vs. time]]
	[[File:gif~~.png\|250px\|thumb\|center\|figure 3. men behind the blind(gif)]]~~		[[File:gif.png\|250px\|thumb\|center\|figure 3. men behind the blind(gif)]]
	~~[[File:gif2~~.png\|250px\|thumb\|center\|figure 3. men behind the blind(gif)]]

	== Methods ==		== Methods ==

imported>Student2016: /* Imaging example of SPAD */

2016-12-16T20:01:58Z

Imaging example of SPAD

← Older revision		Revision as of 20:01, 16 December 2016
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	[[File:imaging.png\|500px\|thumb\|left\|figure 2. photon stistics vs. time]]		[[File:imaging.png\|500px\|thumb\|left\|figure 2. photon stistics vs. time]]
	[[File:gif.png\|250px\|thumb\|center\|figure 3. men behind the blind(gif)]]		[[File:gif.png\|250px\|thumb\|center\|figure 3. men behind the blind(gif)]]
			[[File:gif2.png\|250px\|thumb\|center\|figure 3. men behind the blind(gif)]]

	== Methods ==		== Methods ==

imported>Student2016: /* Monte Carlo simulation setup ( higher probability) */

2016-12-16T20:00:24Z

Monte Carlo simulation setup ( higher probability)

← Older revision		Revision as of 20:00, 16 December 2016
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	To begin with, we simplified the scene provided by the paper: let the wall have infinite height, object have no thickness, and detector area to be square. Besides, we assume all the surface is lambertian reflection, where the reflected ray has largest probability at normal. The layout and lambertian illustration is below:		To begin with, we simplified the scene provided by the paper: let the wall have infinite height, object have no thickness, and detector area to be square. Besides, we assume all the surface is lambertian reflection, where the reflected ray has largest probability at normal. The layout and lambertian illustration is below:

	[[File:SimplifiedLayout.png\|~~250px~~\|left\|thumb\|caption\|Fig 7. Simplified layout with rotated object]]		[[File:SimplifiedLayout.png\|290px\|left\|thumb\|caption\|Fig 7. Simplified layout with rotated object]]
	[[File:lambertian.png\|300px\|center\|thumb\|caption\|Fig 8. lambertian reflection illustration]]		[[File:lambertian.png\|300px\|center\|thumb\|caption\|Fig 8. lambertian reflection illustration]]

imported>Student2016: /* Reconstruction algorithm */

2016-12-16T20:00:08Z

Reconstruction algorithm

← Older revision		Revision as of 20:00, 16 December 2016
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	The reconstruction of object location is solely dependent on timing response captured by each SPAD. Fig 5(a) shows measured timing response from both background and from photons reflected from hidden object. By ignoring background signal (which could be done by doing calibration without hidden object behind the wall), we could select target signal associated with time <math><t>_i</math> (Fig 6(b)) and plot spatial distribution for SPAD image sensor pixels who has response at time <math><t>_i</math> in Fig 6(a). Among those pixels at point i, we could tell how far photons travel from laser to object and to point i. Then the trace of an ellipse could be drawn to show all possible positions of an object. We could then repeat this process by drawing another ellipse using pixels who has response in time <math><t>_i</math>. The object position could be determined by the most overlapped regions of different ellipses.		The reconstruction of object location is solely dependent on timing response captured by each SPAD. Fig 5(a) shows measured timing response from both background and from photons reflected from hidden object. By ignoring background signal (which could be done by doing calibration without hidden object behind the wall), we could select target signal associated with time <math><t>_i</math> (Fig 6(b)) and plot spatial distribution for SPAD image sensor pixels who has response at time <math><t>_i</math> in Fig 6(a). Among those pixels at point i, we could tell how far photons travel from laser to object and to point i. Then the trace of an ellipse could be drawn to show all possible positions of an object. We could then repeat this process by drawing another ellipse using pixels who has response in time <math><t>_i</math>. The object position could be determined by the most overlapped regions of different ellipses.

	[[File:elipse.png\|~~250px~~\|frame\|center\|figure 6. Spherical wave, ellipses, and estimated object location]]		[[File:elipse.png\|220px\|frame\|center\|figure 6. Spherical wave, ellipses, and estimated object location]]

	== Results ==		== Results ==

imported>Student2016: /* Reconstruction algorithm */

2016-12-16T20:00:00Z

Reconstruction algorithm

← Older revision		Revision as of 20:00, 16 December 2016
Line 42:		Line 42:
	The reconstruction of object location is solely dependent on timing response captured by each SPAD. Fig 5(a) shows measured timing response from both background and from photons reflected from hidden object. By ignoring background signal (which could be done by doing calibration without hidden object behind the wall), we could select target signal associated with time <math><t>_i</math> (Fig 6(b)) and plot spatial distribution for SPAD image sensor pixels who has response at time <math><t>_i</math> in Fig 6(a). Among those pixels at point i, we could tell how far photons travel from laser to object and to point i. Then the trace of an ellipse could be drawn to show all possible positions of an object. We could then repeat this process by drawing another ellipse using pixels who has response in time <math><t>_i</math>. The object position could be determined by the most overlapped regions of different ellipses.		The reconstruction of object location is solely dependent on timing response captured by each SPAD. Fig 5(a) shows measured timing response from both background and from photons reflected from hidden object. By ignoring background signal (which could be done by doing calibration without hidden object behind the wall), we could select target signal associated with time <math><t>_i</math> (Fig 6(b)) and plot spatial distribution for SPAD image sensor pixels who has response at time <math><t>_i</math> in Fig 6(a). Among those pixels at point i, we could tell how far photons travel from laser to object and to point i. Then the trace of an ellipse could be drawn to show all possible positions of an object. We could then repeat this process by drawing another ellipse using pixels who has response in time <math><t>_i</math>. The object position could be determined by the most overlapped regions of different ellipses.

	[[File:elipse.png\|~~320px~~\|frame\|center\|figure 6. Spherical wave, ellipses, and estimated object location]]		[[File:elipse.png\|250px\|frame\|center\|figure 6. Spherical wave, ellipses, and estimated object location]]

	== Results ==		== Results ==

imported>Student2016: /* Reconstruction algorithm */

2016-12-16T19:59:48Z

Reconstruction algorithm

← Older revision		Revision as of 19:59, 16 December 2016
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	===Reconstruction algorithm===		===Reconstruction algorithm===

	[[File:background_noise.png\|~~500px~~\|thumb\|center\|figure 5.background noise area vs. valid object signal region]]		[[File:background_noise.png\|400px\|thumb\|center\|figure 5.background noise area vs. valid object signal region]]

	The reconstruction of object location is solely dependent on timing response captured by each SPAD. Fig 5(a) shows measured timing response from both background and from photons reflected from hidden object. By ignoring background signal (which could be done by doing calibration without hidden object behind the wall), we could select target signal associated with time <math><t>_i</math> (Fig 6(b)) and plot spatial distribution for SPAD image sensor pixels who has response at time <math><t>_i</math> in Fig 6(a). Among those pixels at point i, we could tell how far photons travel from laser to object and to point i. Then the trace of an ellipse could be drawn to show all possible positions of an object. We could then repeat this process by drawing another ellipse using pixels who has response in time <math><t>_i</math>. The object position could be determined by the most overlapped regions of different ellipses.		The reconstruction of object location is solely dependent on timing response captured by each SPAD. Fig 5(a) shows measured timing response from both background and from photons reflected from hidden object. By ignoring background signal (which could be done by doing calibration without hidden object behind the wall), we could select target signal associated with time <math><t>_i</math> (Fig 6(b)) and plot spatial distribution for SPAD image sensor pixels who has response at time <math><t>_i</math> in Fig 6(a). Among those pixels at point i, we could tell how far photons travel from laser to object and to point i. Then the trace of an ellipse could be drawn to show all possible positions of an object. We could then repeat this process by drawing another ellipse using pixels who has response in time <math><t>_i</math>. The object position could be determined by the most overlapped regions of different ellipses.

imported>Student2016: /* Imaging example of SPAD */

2016-12-16T19:59:28Z

Imaging example of SPAD

← Older revision		Revision as of 19:59, 16 December 2016
Line 24:		Line 24:
	One imaging example of the SPAD can be the object behind the blind. Normal imaging detector would have no way to capture this man behind the blind, and the information they obtain would be like the left hump in the figure below. However some photons pass through the blind and then gets reflected off the object. They form the second smaller hump. And the man can be imaged.		One imaging example of the SPAD can be the object behind the blind. Normal imaging detector would have no way to capture this man behind the blind, and the information they obtain would be like the left hump in the figure below. However some photons pass through the blind and then gets reflected off the object. They form the second smaller hump. And the man can be imaged.
	[[File:imaging.png\|500px\|thumb\|left\|figure 2. photon stistics vs. time]]		[[File:imaging.png\|500px\|thumb\|left\|figure 2. photon stistics vs. time]]
	[[File:gif.png\|250px\|thumb\|center\|figure 3. men behind the blind]]		[[File:gif.png\|250px\|thumb\|center\|figure 3. men behind the blind(gif)]]

	== Methods ==		== Methods ==

imported>Student2016: /* Scene introduction */

2016-12-16T19:54:37Z

Scene introduction

← Older revision		Revision as of 19:54, 16 December 2016
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	There have been multiple papers published on this topic. The one we studied is “Detection and tracking of moving objects hidden from view”. It was published last year on nature Photonics. The set-up of scene is described in Fig 4. SPAD and laser are spaced 15cm apart and they both are used to locate and track an object (10cm wide and 30cm high) behind the wall.		There have been multiple papers published on this topic. The one we studied is “Detection and tracking of moving objects hidden from view”. It was published last year on nature Photonics. The set-up of scene is described in Fig 4. SPAD and laser are spaced 15cm apart and they both are used to locate and track an object (10cm wide and 30cm high) behind the wall.

	[[File:scene.png\|~~500px~~\|thumb\|center\|figure 4.scene; 4(a): top view; 4(b): side view; 4(c) top view with laser point source emitting spherical wave; 4(d) top view with hidden object emitting spherical wave]]		[[File:scene.png\|700px\|thumb\|center\|figure 4.scene; 4(a): top view; 4(b): side view; 4(c) top view with laser point source emitting spherical wave; 4(d) top view with hidden object emitting spherical wave]]

	The working principle starts by laser focus onto the floor where photons are scattered to different directions for first reflection. Some of lucky photons hit on hidden object (as in Fig 4(d) for second reflection. Among second reflection, some photons scatter to the filed of view of SPAD camera and register its time of flight for third reflection. Photons captured in SPAD field of view also has a spacial distribution that is ellipse with focal points one at first reflection location and the other one at hidden object, as illustrated in Fig 6(b).		The working principle starts by laser focus onto the floor where photons are scattered to different directions for first reflection. Some of lucky photons hit on hidden object (as in Fig 4(d) for second reflection. Among second reflection, some photons scatter to the filed of view of SPAD camera and register its time of flight for third reflection. Photons captured in SPAD field of view also has a spacial distribution that is ellipse with focal points one at first reflection location and the other one at hidden object, as illustrated in Fig 6(b).

imported>Student2016: /* Attenuation of reflected signals versus distance */

2016-12-16T19:49:32Z

Attenuation of reflected signals versus distance

← Older revision		Revision as of 19:49, 16 December 2016
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	[[File:attenuation_1.png\|320px\|left\|thumb\|caption\|Fig 18. with object-without object]]		[[File:attenuation_1.png\|320px\|left\|thumb\|caption\|Fig 18. with object-without object]]
	[[File:attenuation_2.png\|~~320px~~\|center\|thumb\|caption\|Fig 19. with object-without object]]		[[File:attenuation_2.png\|300px\|center\|thumb\|caption\|Fig 19. with object-without object]]

	===Spatial distribution of reflected photons validation===		===Spatial distribution of reflected photons validation===

imported>Student2016: /* Results */

2016-12-16T19:49:06Z

Results

← Older revision		Revision as of 19:49, 16 December 2016
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	With the above two assumptions we get the simulation result, here we only displaying only the rays that passed the SPAD detector area:		With the above two assumptions we get the simulation result, here we only displaying only the rays that passed the SPAD detector area:
	[[File:onlyspad_object.png \|380px\|left\|thumb\|caption\|Fig 9. Simulation result with object]]		[[File:onlyspad_object.png \|380px\|left\|thumb\|caption\|Fig 9. Simulation result with object]]
	[[File:onlyspad_woobject.png \|~~350px~~\|center\|thumb\|caption\|Fig 10. Simulation result without object]]		[[File:onlyspad_woobject.png \|380px\|center\|thumb\|caption\|Fig 10. Simulation result without object]]

Non-line of Sight imaging by SPAD​ - Revision history

imported>Student2016: /* Imaging example of SPAD */

imported>Student2016: /* Imaging example of SPAD */

imported>Student2016: /* Monte Carlo simulation setup ( higher probability) */

imported>Student2016: /* Reconstruction algorithm */

imported>Student2016: /* Reconstruction algorithm */

imported>Student2016: /* Reconstruction algorithm */

imported>Student2016: /* Imaging example of SPAD */

imported>Student2016: /* Scene introduction */

imported>Student2016: /* Attenuation of reflected signals versus distance */

imported>Student2016: /* Results */

Non-line of Sight imaging by SPAD - Revision history