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Synthetic aperture confocal imaging






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Synthetic aperture confocal imaging. Marc Levoy Billy Chen Vaibhav Vaish. Mark Horowitz Ian McDowall Mark Bolas. technologies large camera arrays large projector arrays camera–projector arrays. optical effects synthetic aperture photography synthetic aperture illumination
Synthetic aperture confocal imaging

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Slide1 l.jpgSlide 1

Synthetic aperture confocal imaging

Marc Levoy

Billy Chen

Vaibhav Vaish

Mark Horowitz

Ian McDowall

Mark Bolas

Outline l.jpgSlide 2

technologies

large camera arrays

large projector arrays

camera–projector arrays

optical effects

synthetic aperture photography

synthetic aperture illumination

synthetic confocal imaging

applications

  • partially occluding environments

  • weakly scattering media

examples

foliage

murky water

Outline

Stanford multi camera array wilburn 2002 l.jpgSlide 3

Stanford Multi-Camera Array[Wilburn 2002]

  • 640 × 480 pixels ×30fps × 128 cameras

  • synchronized timing

  • continuous video streaming

  • flexible physical arrangement

Ways to use large camera arrays l.jpgSlide 4

Ways to use large camera arrays

  • widely spaced light field capture

  • tightly packed high-performance imaging

  • intermediate spacing synthetic aperture photography

Synthetic aperture photography l.jpgSlide 5

Synthetic aperture photography

Synthetic aperture photography6 l.jpgSlide 6

Synthetic aperture photography

Synthetic aperture photography7 l.jpgSlide 7

Synthetic aperture photography

Synthetic aperture photography8 l.jpgSlide 8

Synthetic aperture photography

Synthetic aperture photography9 l.jpgSlide 9

Synthetic aperture photography

Synthetic aperture photography10 l.jpgSlide 10

Synthetic aperture photography

Related work l.jpgSlide 11

Related work

  • not like synthetic aperture radar (SAR)

  • more like X-ray tomosynthesis

  • [Levoy and Hanrahan, 1996]

  • [Isaksen, McMillan, Gortler, 2000]

Example using 45 cameras l.jpgSlide 12

Example using 45 cameras

Synthetic pull focus l.jpgSlide 13

Synthetic pull-focus

Crowd scene l.jpgSlide 14

Crowd scene

Crowd scene15 l.jpgSlide 15

Crowd scene

Synthetic aperture photography using an array of mirrors l.jpgSlide 16

Synthetic aperture photographyusing an array of mirrors

  • 11-megapixel camera

  • 22 planar mirrors

?

Synthetic aperture illumation l.jpgSlide 19

Synthetic aperture illumation

Synthetic aperture illumation20 l.jpgSlide 20

Synthetic aperture illumation

  • technologies

    • array of projectors

    • array of microprojectors

    • single projector + array of mirrors

  • applications

    • bright display

    • autostereoscopic display [Matusik 2004]

    • confocal imaging [this paper]

Confocal scanning microscopy l.jpgSlide 21

light source

pinhole

Confocal scanning microscopy

Confocal scanning microscopy22 l.jpgSlide 22

r

light source

pinhole

pinhole

photocell

Confocal scanning microscopy

Confocal scanning microscopy23 l.jpgSlide 23

Confocal scanning microscopy

light source

pinhole

pinhole

photocell

Confocal scanning microscopy24 l.jpgSlide 24

Confocal scanning microscopy

light source

pinhole

pinhole

photocell

Slide25 l.jpgSlide 25

[UMIC SUNY/Stonybrook]

Synthetic confocal scanning l.jpgSlide 26

→ 5 beams

→ 0 or 1 beam

Synthetic confocal scanning

light source

Synthetic confocal scanning27 l.jpgSlide 27

→ 5 beams

→ 0 or 1 beam

Synthetic confocal scanning

light source

Synthetic confocal scanning28 l.jpgSlide 28

d.o.f.

→ 5 beams

→ 0 or 1 beam

Synthetic confocal scanning

  • works with any number of projectors ≥ 2

  • discrimination degrades if point to left of

  • no discrimination for points to left of

  • slow!

  • poor light efficiency

Synthetic coded aperture confocal imaging l.jpgSlide 29

Synthetic coded-apertureconfocal imaging

  • different from coded aperture imaging in astronomy

  • [Wilson, Confocal Microscopy by Aperture Correlation, 1996]

Synthetic coded aperture confocal imaging30 l.jpgSlide 30

Synthetic coded-apertureconfocal imaging

Synthetic coded aperture confocal imaging31 l.jpgSlide 31

Synthetic coded-apertureconfocal imaging

Synthetic coded aperture confocal imaging32 l.jpgSlide 32

Synthetic coded-apertureconfocal imaging

Synthetic coded aperture confocal imaging33 l.jpgSlide 33

Synthetic coded-apertureconfocal imaging

100 trials

→ 2 beams × ~50/100 trials ≈ 1

→ ~1 beam × ~50/100 trials ≈ 0.5

Synthetic coded aperture confocal imaging34 l.jpgSlide 34

Synthetic coded-apertureconfocal imaging

100 trials

→ 2 beams × ~50/100 trials ≈ 1

→ ~1 beam × ~50/100 trials ≈ 0.5

floodlit

→ 2 beams

→ 2 beams

trials – ¼ × floodlit

→ 1 – ¼ ( 2 ) ≈ 0.5

→ 0.5 – ¼ ( 2 ) ≈ 0

Synthetic coded aperture confocal imaging35 l.jpgSlide 35

Synthetic coded-apertureconfocal imaging

100 trials

→ 2 beams × ~50/100 trials ≈ 1

→ ~1 beam × ~50/100 trials ≈ 0.5

floodlit

→ 2 beams

→ 2 beams

trials – ¼ × floodlit

→ 1 – ¼ ( 2 ) ≈ 0.5

→ 0.5 – ¼ ( 2 ) ≈ 0

  • 50% light efficiency

  • any number of projectors ≥ 2

  • no discrimination to left of

  • works with relatively few trials (~16)

Synthetic coded aperture confocal imaging36 l.jpgSlide 36

Synthetic coded-apertureconfocal imaging

100 trials

→ 2 beams × ~50/100 trials ≈ 1

→ ~1 beam × ~50/100 trials ≈ 0.5

floodlit

→ 2 beams

→ 2 beams

trials – ¼ × floodlit

→ 1 – ¼ ( 2 ) ≈ 0.5

→ 0.5 – ¼ ( 2 ) ≈ 0

  • 50% light efficiency

  • any number of projectors ≥ 2

  • no discrimination to left of

  • works with relatively few trials (~16)

  • needs patterns in which illumination of tiles are uncorrelated

Example pattern l.jpgSlide 37

Example pattern

Patterns with less aliasing l.jpgSlide 38

Patterns with less aliasing

Implementation using an array of mirrors l.jpgSlide 39

Implementation using an array of mirrors

Confocal imaging in scattering media l.jpgSlide 41

Confocal imaging in scattering media

  • small tank

    • too short for attenuation

    • lit by internal reflections

Experiments in a large water tank l.jpgSlide 42

Experiments in a large water tank

50-foot flume at Wood’s Hole Oceanographic Institution (WHOI)

Experiments in a large water tank43 l.jpgSlide 43

Experiments in a large water tank

  • 4-foot viewing distance to target

  • surfaces blackened to kill reflections

  • titanium dioxide in filtered water

  • transmissometer to measure turbidity

Experiments in a large water tank44 l.jpgSlide 44

Experiments in a large water tank

  • stray light limits performance

  • one projector suffices if no occluders

Seeing through turbid water l.jpgSlide 45

Seeing through turbid water

floodlit

scanned tile

Application to underwater exploration l.jpgSlide 46

[Ballard/IFE 2004]

Application tounderwater exploration

[Ballard/IFE 2004]

Research challenges in sap and sai l.jpgSlide 47

Research challenges in SAP and SAI

  • theory

    • aperture shapes and sampling patterns

    • illumination patterns for confocal imaging

  • optical design

    • How to arrange cameras, projectors,lenses, mirrors, and other optical elements?

    • How to compare the performance of different arrangements (in foliage, underwater,...)?

Challenges continued l.jpgSlide 48

Challenges (continued)

  • systems design

    • multi-camera or multi-projector chips

    • communication in camera-projector networks

    • calibration in long-range or mobile settings

  • algorithms

    • tracking and stabilization of moving objects

    • compression of dense multi-view imagery

    • shape from light fields

Challenges continued49 l.jpgSlide 49

Challenges (continued)

  • applications of confocal imaging

    • remote sensing and surveillance

    • shape measurement

    • scientific imaging

  • applications of shaped illumination

    • shaped searchlights for surveillance

    • shaped headlamps for driving in bad weather

    • selective lighting of characters for stage and screen

Computational imaging in other fields l.jpgSlide 50

Computational imagingin other fields

  • medical imaging

    • rebinning

    • tomography

  • airborne sensing

    • multi-perspective panoramas

    • synthetic aperture radar

  • astronomy

    • coded-aperture imaging

    • multi-telescope imaging

Computational imaging in other fields51 l.jpgSlide 51

Computational imagingin other fields

  • geophysics

    • accoustic array imaging

    • borehole tomography

  • biology

    • confocal microscopy

    • deconvolution microscopy

  • physics

    • optical tomography

    • inverse scattering

The team l.jpgSlide 52

staff

Mark Horowitz

Marc Levoy

Bennett Wilburn

students

Billy Chen

Vaibhav Vaish

Katherine Chou

Monica Goyal

Neel Joshi

Hsiao-Heng Kelin Lee

Georg Petschnigg

Guillaume Poncin

Michael Smulski

Augusto Roman

collaborators

Mark Bolas

Ian McDowall

Guillermo Sapiro

funding

Intel

Sony

Interval Research

NSF

DARPA

The team

Related papers l.jpgSlide 53

Related papers

  • The Light Field Video Camera

    Bennett Wilburn, Michael Smulski, Hsiao-Heng Kelin Lee, and Mark Horowitz

    Proc. Media Processors 2002, SPIE Electronic Imaging 2002

  • Using Plane + Parallax for Calibrating Dense Camera Arrays

    Vaibhav Vaish, Bennett Wilburn, Neel Joshi,, Marc Levoy

    Proc. CVPR 2004

  • High Speed Video Using a Dense Camera Array

    Bennett Wilburn, Neel Joshi, Vaibhav Vaish, Marc Levoy, Mark Horowitz

    Proc. CVPR 2004

  • Spatiotemporal Sampling and Interpolation for Dense Camera Arrays

    Bennett Wilburn, Neel Joshi, Katherine Chou, Marc Levoy, Mark Horowitz

    ACM Transactions on Graphics (conditionally accepted)

Slide54 l.jpgSlide 54

http://graphics.stanford.edu/papers/confocal


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