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Ramesh Raskar, MIT Media Lab
After X, what is neXt
Coming up with
New Ideas in Imaging
Ramesh Raskar, MIT Media Lab
Ramesh Raskar, MIT Media Lab
Xd
X++
X X+Y
X
X
neXt
Ramesh Raskar, MIT Media Lab
Raskar, Camera Culture, MIT Media Lab
Camera Culture
Ramesh Raskar
Camera Culture
MIT Media Lab
http://raskar.info
http://...
Create tools to
better capture and share visual information
The goal is to create an entirely
new class of imaging platfor...
Ramesh Raskar, MIT Media Lab
Camera CultureCamera Culture
Course WebPage :
http://cameraculture.info/courses/
Ramesh Raskar, MIT Media Lab
After X, what is neXt
Coming up with
New Ideas in Imaging
Ramesh Raskar, MIT Media Lab
Ramesh Raskar, MIT Media Lab
Xd
X++
X X+Y
X
X
neXt
Ramesh Raskar, MIT Media Lab
Ramesh Raskar, MIT Media Lab
Simple Exercise ..Simple Exercise ..
What is neXt
Ramesh Raskar, MIT Media Lab
Strategy #1: XStrategy #1: Xdd
• Extend it to next dimension (or some other) dimensionExtend ...
Coded-Aperture ImagingCoded-Aperture Imaging
• Lens-free imaging!Lens-free imaging!
• Pinhole-cameraPinhole-camera
sharpne...
Flutter Shutter CameraFlutter Shutter Camera
Raskar, Agrawal, Tumblin [Siggraph2006]
LCD opacity switched
in coded sequence
Figure 2 results
Input Image
Problem: Motion Deblurring
Ramesh Raskar, Camera Culture, MIT
Media Lab
Image Deblurred by solving a linear system. No post-processing
Blurred Taxi
Ramesh Raskar, Camera Culture, MIT
Media Lab
Flutter Shutter: Shutter is OPEN and CLOSED
Preserves High Spatial
Frequencies
Sharp Photo Blurred Photo
PSF == Broadband ...
Coded Aperture CameraCoded Aperture Camera
The aperture of a 100 mm lens is modified
Rest of the camera is unmodified
Inse...
Out of Focus Photo: Coded Aperture
Captured Blurred
Photo
Refocused on
Person
Larval Trematode WormLarval Trematode Worm
Ramesh Raskar, MIT Media Lab
Strategy #2: X+YStrategy #2: X+Y
• Fusion of the dissimilarFusion of the dissimilar
– More di...
Ramesh Raskar, MIT Media Lab
Imaging in Sciences:Imaging in Sciences:
Computer TomographyComputer Tomography
• http://info...
Ramesh Raskar, MIT Media Lab
Borehole tomographyBorehole tomography
• receivers measure end-to-end travel timereceivers me...
Ramesh Raskar, MIT Media Lab
Prototype cameraPrototype camera
40004000 × 4000 pixels ÷ 292 × 292 lenses = 14 × 14× 4000 pi...
Ramesh Raskar, MIT Media Lab
Ramesh Raskar, MIT Media Lab
Example of digital refocusingExample of digital refocusing
Coded-Aperture ImagingCoded-Aperture Imaging
• Lens-free imaging!Lens-free imaging!
• Pinhole-cameraPinhole-camera
sharpne...
Mask in a Camera
Mask
Aperture
Canon EF 100 mm 1:1.28 Lens,
Canon SLR Rebel XT camera
Ramesh Raskar, MIT Media Lab
Strategy #3: XStrategy #3: X
Do exactly the oppositeDo exactly the opposite
• Processing, Mem...
Ramesh Raskar, MIT Media Lab
• e.g. Reverse Auctione.g. Reverse Auction
Less is MoreLess is More
Blocking Light == More InformationBlocking Light == More Information
Coding in TimeCoding in Time...
Larval Trematode WormLarval Trematode Worm
Mitsubishi Electric Research Laboratories Special Effects in the Real World Raskar 2006
Vicon
Motion Capture
High-speed
IR...
Towards ‘on-set’ motion capture
• 500 Hz with Id for each Marker Tag
• Visually imperceptible tags + Natural lighting
• Un...
Mitsubishi Electric Research Laboratories Special Effects in the Real World Raskar 2006
R Raskar, H Nii, B de Decker, Y Ha...
Mitsubishi Electric Research Laboratories Special Effects in the Real World Raskar 2006
Imperceptible Tags under clothing,...
Mitsubishi Electric Research Laboratories Special Effects in the Real World Raskar 2006
Labeling Space
(Indoor GPS)
Each l...
Mitsubishi Electric Research Laboratories Special Effects in the Real World Raskar 2006
Pattern
MSB
Pattern
MSB
Pattern
MS...
Mitsubishi Electric Research Laboratories Special Effects in the Real World Raskar 2006
Inside of Multi-LED Emitter
Mitsubishi Electric Research Laboratories Special Effects in the Real World Raskar 2006
Tag
Ramesh Raskar, MIT Media Lab
• When life gives you lemon, make lemonadeWhen life gives you lemon, make lemonade
Ramesh Raskar, Karhan Tan, Rogerio Feris,Ramesh Raskar, Karhan Tan, Rogerio Feris,
Jingyi Yu, Matthew TurkJingyi Yu, Matth...
Depth Discontinuities
Internal and external
Shape boundaries, Occluding contour, Silhouettes
Depth
Edges
Our MethodCanny
Canny Intensity
Edge Detection
Our Method
Photo Result
Car Manuals
What are the problems
with ‘real’ photo in
conveying information ?
Why do we hire artists
to draw what can be
photographed...
Shadows
Clutter
Many Colors
Highlight Shape Edges
Mark moving parts
Basic colors
Shadows
Clutter
Many Colors
Highlight Edges
Mark moving parts
Basic colors
A New ProblemA New Problem
Ramesh Raskar, MIT Media Lab
Strategy #4: XStrategy #4: X
• Given a Hammer ..Given a Hammer ..
– Find all the nailsFind al...
A Night Time Scene:
Objects are Difficult to Understand due to Lack of Context
Dark Bldgs
Reflections on
bldgs
Unknown
sha...
Enhanced Context :
All features from night scene are preserved, but background in clear
‘Well-lit’ Bldgs
Reflections in
bl...
Background is captured from day-time
scene using the same fixed camera
Night Image
Day Image
Result: Enhanced Image
Flash Result Reflection LayerAmbient
Flash and Ambient ImagesFlash and Ambient Images
[ Agrawal, Raskar, Nayar, Li Siggrap...
Agrawala et al, Digital Photomontage, Siggraph 2004
Agrawala et al, Digital Photomontage, Siggraph 2004
actual
photomontageset of originals
perceived
Source images Brush strokes Computed labeling
Composite
Ramesh Raskar, MIT Media Lab
Strategy #5: XStrategy #5: X
• Given a problem, find other solutionsGiven a problem, find oth...
Ramesh Raskar, MIT Media Lab
Strategy #6: X++Strategy #6: X++
• Pick your adjective ..Pick your adjective ..
• Making it f...
Ramesh Raskar, MIT Media Lab
X++ : Add your favorite adjectiveX++ : Add your favorite adjective
• Context aware,Context aw...
Ramesh Raskar, MIT Media Lab
PitfallsPitfalls
• These six ways are only a startThese six ways are only a start
• They are ...
Ramesh Raskar, MIT Media Lab
What are Bad ideas to pursueWhat are Bad ideas to pursue
• X then Y (then Z)X then Y (then Z)...
Ramesh Raskar, MIT Media Lab
Xd
X++
X X+Y
X
X
neXt
Ramesh Raskar, MIT Media Lab
Raskar, Camera Culture, MIT Media Lab
Camera Culture
Ramesh Raskar
Camera Culture
MIT Media Lab
http://raskar.info
http://...
How to come up with new Ideas Raskar Feb09
How to come up with new Ideas Raskar Feb09
How to come up with new Ideas Raskar Feb09
How to come up with new Ideas Raskar Feb09
How to come up with new Ideas Raskar Feb09
How to come up with new Ideas Raskar Feb09
How to come up with new Ideas Raskar Feb09
How to come up with new Ideas Raskar Feb09
How to come up with new Ideas Raskar Feb09
How to come up with new Ideas Raskar Feb09
How to come up with new Ideas Raskar Feb09
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How to come up with new Ideas Raskar Feb09

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If you are inspired by an idea 'X', how will you come up with the neXt idea? This presentation shows 6 different ways you can exercise your mind in an attempt to develop the next cool idea.

http://raskar.info
http://cameraculture.info

Published in: Self Improvement, Technology

How to come up with new Ideas Raskar Feb09

  1. 1. Ramesh Raskar, MIT Media Lab After X, what is neXt Coming up with New Ideas in Imaging Ramesh Raskar, MIT Media Lab
  2. 2. Ramesh Raskar, MIT Media Lab Xd X++ X X+Y X X neXt Ramesh Raskar, MIT Media Lab
  3. 3. Raskar, Camera Culture, MIT Media Lab Camera Culture Ramesh Raskar Camera Culture MIT Media Lab http://raskar.info http://cameraculture.info Ramesh Raskar Associate Professor
  4. 4. Create tools to better capture and share visual information The goal is to create an entirely new class of imaging platforms that have an understanding of the world that far exceeds human ability and produce meaningful abstractions that are well within human comprehensibility
  5. 5. Ramesh Raskar, MIT Media Lab Camera CultureCamera Culture Course WebPage : http://cameraculture.info/courses/
  6. 6. Ramesh Raskar, MIT Media Lab After X, what is neXt Coming up with New Ideas in Imaging Ramesh Raskar, MIT Media Lab
  7. 7. Ramesh Raskar, MIT Media Lab Xd X++ X X+Y X X neXt Ramesh Raskar, MIT Media Lab
  8. 8. Ramesh Raskar, MIT Media Lab Simple Exercise ..Simple Exercise .. What is neXt
  9. 9. Ramesh Raskar, MIT Media Lab Strategy #1: XStrategy #1: Xdd • Extend it to next dimension (or some other) dimensionExtend it to next dimension (or some other) dimension • Context aware resizingContext aware resizing – VideoVideo – Instead of square resizing-> CD cover (with a hole in center) resizingInstead of square resizing-> CD cover (with a hole in center) resizing • Text, Audio (Speech), Image, Video .. Whats next ?Text, Audio (Speech), Image, Video .. Whats next ? • Video, 3D meshes, 4D lightfieldsVideo, 3D meshes, 4D lightfields • Images to infrared, sound, ultrasoundImages to infrared, sound, ultrasound • Macro scale to microscale (Levoy, Lightfield to Microscope)Macro scale to microscale (Levoy, Lightfield to Microscope) • Time to space to angle to idTime to space to angle to id • (coded exposure <- coded aperture)(coded exposure <- coded aperture)
  10. 10. Coded-Aperture ImagingCoded-Aperture Imaging • Lens-free imaging!Lens-free imaging! • Pinhole-cameraPinhole-camera sharpness,sharpness, without massive lightwithout massive light loss.loss. • No ray bending (OK forNo ray bending (OK for X-ray, gamma ray, etc.)X-ray, gamma ray, etc.) • Two elementsTwo elements – Code Mask: binaryCode Mask: binary (opaque/transparent)(opaque/transparent) – Sensor gridSensor grid • Mask autocorrelation isMask autocorrelation is delta function (impulse)delta function (impulse) • Similar to MotionSensorSimilar to MotionSensor
  11. 11. Flutter Shutter CameraFlutter Shutter Camera Raskar, Agrawal, Tumblin [Siggraph2006] LCD opacity switched in coded sequence
  12. 12. Figure 2 results Input Image Problem: Motion Deblurring Ramesh Raskar, Camera Culture, MIT Media Lab
  13. 13. Image Deblurred by solving a linear system. No post-processing Blurred Taxi Ramesh Raskar, Camera Culture, MIT Media Lab
  14. 14. Flutter Shutter: Shutter is OPEN and CLOSED Preserves High Spatial Frequencies Sharp Photo Blurred Photo PSF == Broadband Function Fourier Transform
  15. 15. Coded Aperture CameraCoded Aperture Camera The aperture of a 100 mm lens is modified Rest of the camera is unmodified Insert a coded mask with chosen binary pattern
  16. 16. Out of Focus Photo: Coded Aperture
  17. 17. Captured Blurred Photo
  18. 18. Refocused on Person
  19. 19. Larval Trematode WormLarval Trematode Worm
  20. 20. Ramesh Raskar, MIT Media Lab Strategy #2: X+YStrategy #2: X+Y • Fusion of the dissimilarFusion of the dissimilar – More dissimilar, more spectacular the outputMore dissimilar, more spectacular the output • ExampleExample – Scientific imaging + PhotographyScientific imaging + Photography • Coded apertureCoded aperture • TomographyTomography • Lightfields + User interfacesLightfields + User interfaces • Projector = cameraProjector = camera – Spatial Augmented RealitySpatial Augmented Reality
  21. 21. Ramesh Raskar, MIT Media Lab Imaging in Sciences:Imaging in Sciences: Computer TomographyComputer Tomography • http://info.med.yale.edu/intmed/cardio/imaging/techniques/ct_imhttp://info.med.yale.edu/intmed/cardio/imaging/techniques/ct_im aging/aging/
  22. 22. Ramesh Raskar, MIT Media Lab Borehole tomographyBorehole tomography • receivers measure end-to-end travel timereceivers measure end-to-end travel time • reconstruct to find velocities in intervening cellsreconstruct to find velocities in intervening cells • must use limited-angle reconstruction method (likemust use limited-angle reconstruction method (like ART)ART) (from Reynolds)
  23. 23. Ramesh Raskar, MIT Media Lab Prototype cameraPrototype camera 40004000 × 4000 pixels ÷ 292 × 292 lenses = 14 × 14× 4000 pixels ÷ 292 × 292 lenses = 14 × 14 Contax medium format camera Kodak 16-megapixel sensor Adaptive Optics microlens array 125μ square-sided microlenses
  24. 24. Ramesh Raskar, MIT Media Lab
  25. 25. Ramesh Raskar, MIT Media Lab Example of digital refocusingExample of digital refocusing
  26. 26. Coded-Aperture ImagingCoded-Aperture Imaging • Lens-free imaging!Lens-free imaging! • Pinhole-cameraPinhole-camera sharpness,sharpness, without massive lightwithout massive light loss.loss. • No ray bending (OK forNo ray bending (OK for X-ray, gamma ray, etc.)X-ray, gamma ray, etc.) • Two elementsTwo elements – Code Mask: binaryCode Mask: binary (opaque/transparent)(opaque/transparent) – Sensor gridSensor grid • Mask autocorrelation isMask autocorrelation is delta function (impulse)delta function (impulse) • Similar to MotionSensorSimilar to MotionSensor
  27. 27. Mask in a Camera Mask Aperture Canon EF 100 mm 1:1.28 Lens, Canon SLR Rebel XT camera
  28. 28. Ramesh Raskar, MIT Media Lab Strategy #3: XStrategy #3: X Do exactly the oppositeDo exactly the opposite • Processing, Memory, BandwidthProcessing, Memory, Bandwidth – In Computing world, in any era, one of this is a bottleneckIn Computing world, in any era, one of this is a bottleneck – But overtime, they change. You can often take an older idea and doBut overtime, they change. You can often take an older idea and do exactly the opposite.exactly the opposite. – E.g. bandwidth is now considered virtually limitlessE.g. bandwidth is now considered virtually limitless • In imaging:In imaging: – Larger sensors?Larger sensors? • Everyone is thinking about building cheaper, smaller pixel sensors and THENEveryone is thinking about building cheaper, smaller pixel sensors and THEN improving SNR .. Maybe just build larger sensors?improving SNR .. Maybe just build larger sensors? – SLR: Faster mirror flip or no mirror flipSLR: Faster mirror flip or no mirror flip • Companies spent years improving mirror flip speedCompanies spent years improving mirror flip speed • Why not just remove it?Why not just remove it? • More computationMore computation • Less lightLess light
  29. 29. Ramesh Raskar, MIT Media Lab • e.g. Reverse Auctione.g. Reverse Auction
  30. 30. Less is MoreLess is More Blocking Light == More InformationBlocking Light == More Information Coding in TimeCoding in Time Coding in SpaceCoding in Space
  31. 31. Larval Trematode WormLarval Trematode Worm
  32. 32. Mitsubishi Electric Research Laboratories Special Effects in the Real World Raskar 2006 Vicon Motion Capture High-speed IR Camera Medical Rehabilitation Athlete Analysis Performance Capture Biomechanical Analysis
  33. 33. Towards ‘on-set’ motion capture • 500 Hz with Id for each Marker Tag • Visually imperceptible tags + Natural lighting • Unlimited Number of Tags • Base station and tags only a few 10’s $ Traditional: High-speed IR Camera + Body markers Second Skin: High-speed LED emitters+ Photosensing Body markers
  34. 34. Mitsubishi Electric Research Laboratories Special Effects in the Real World Raskar 2006 R Raskar, H Nii, B de Decker, Y Hashimoto, J Summet, D Moore, Y Zhao, J Westhues, P Dietz, M Inami, S Nayar, J Barnwell, M Noland, P Bekaert, V Branzoi, E Bruns Siggraph 2007 Prakash: Lighting-Aware Motion Capture Using Photosensing Markers and Multiplexed Illuminators
  35. 35. Mitsubishi Electric Research Laboratories Special Effects in the Real World Raskar 2006 Imperceptible Tags under clothing, tracked under ambient light Hidden Marker Tags Outdoors Unique Id
  36. 36. Mitsubishi Electric Research Laboratories Special Effects in the Real World Raskar 2006 Labeling Space (Indoor GPS) Each location receives a unique temporal code But 60Hz video projector is too slow Projector Tags Pos=0 Pos=255 Time
  37. 37. Mitsubishi Electric Research Laboratories Special Effects in the Real World Raskar 2006 Pattern MSB Pattern MSB Pattern MSB-1 Pattern MSB-1 Pattern LSB Pattern LSB For each tag a. From light sequence, decode x and y coordinate b. Transmit back to RF reader (Id, x, y) For each tag a. From light sequence, decode x and y coordinate b. Transmit back to RF reader (Id, x, y) 00 11 11 00 00 X=1 2 X=1 2
  38. 38. Mitsubishi Electric Research Laboratories Special Effects in the Real World Raskar 2006 Inside of Multi-LED Emitter
  39. 39. Mitsubishi Electric Research Laboratories Special Effects in the Real World Raskar 2006 Tag
  40. 40. Ramesh Raskar, MIT Media Lab • When life gives you lemon, make lemonadeWhen life gives you lemon, make lemonade
  41. 41. Ramesh Raskar, Karhan Tan, Rogerio Feris,Ramesh Raskar, Karhan Tan, Rogerio Feris, Jingyi Yu, Matthew TurkJingyi Yu, Matthew Turk Mitsubishi Electric Research Labs (MERL), Cambridge, MAMitsubishi Electric Research Labs (MERL), Cambridge, MA U of California at Santa BarbaraU of California at Santa Barbara U of North Carolina at Chapel HillU of North Carolina at Chapel Hill Non-photorealistic Camera:Non-photorealistic Camera: Depth Edge DetectionDepth Edge Detection andand Stylized RenderingStylized Rendering usingusing Multi-Flash ImagingMulti-Flash Imaging
  42. 42. Depth Discontinuities Internal and external Shape boundaries, Occluding contour, Silhouettes
  43. 43. Depth Edges
  44. 44. Our MethodCanny
  45. 45. Canny Intensity Edge Detection Our Method Photo Result
  46. 46. Car Manuals
  47. 47. What are the problems with ‘real’ photo in conveying information ? Why do we hire artists to draw what can be photographed ?
  48. 48. Shadows Clutter Many Colors Highlight Shape Edges Mark moving parts Basic colors
  49. 49. Shadows Clutter Many Colors Highlight Edges Mark moving parts Basic colors A New ProblemA New Problem
  50. 50. Ramesh Raskar, MIT Media Lab Strategy #4: XStrategy #4: X • Given a Hammer ..Given a Hammer .. – Find all the nailsFind all the nails – Sometimes even screws and boltsSometimes even screws and bolts • Given a cool solution/technique,Given a cool solution/technique, – find other problemsfind other problems • Good recent examplesGood recent examples – Gradient domain techniquesGradient domain techniques • Introduced in Graphics for High dynamic range toneIntroduced in Graphics for High dynamic range tone mapping [Fattal Lischinski 2002]mapping [Fattal Lischinski 2002] • Now a major hammerNow a major hammer – Image editing, compositing, fusion, alpha matting, reflection layer recoveryImage editing, compositing, fusion, alpha matting, reflection layer recovery
  51. 51. A Night Time Scene: Objects are Difficult to Understand due to Lack of Context Dark Bldgs Reflections on bldgs Unknown shapes
  52. 52. Enhanced Context : All features from night scene are preserved, but background in clear ‘Well-lit’ Bldgs Reflections in bldgs windows Tree, Street shapes
  53. 53. Background is captured from day-time scene using the same fixed camera Night Image Day Image Result: Enhanced Image
  54. 54. Flash Result Reflection LayerAmbient Flash and Ambient ImagesFlash and Ambient Images [ Agrawal, Raskar, Nayar, Li Siggraph05 ][ Agrawal, Raskar, Nayar, Li Siggraph05 ]
  55. 55. Agrawala et al, Digital Photomontage, Siggraph 2004
  56. 56. Agrawala et al, Digital Photomontage, Siggraph 2004
  57. 57. actual photomontageset of originals perceived
  58. 58. Source images Brush strokes Computed labeling Composite
  59. 59. Ramesh Raskar, MIT Media Lab Strategy #5: XStrategy #5: X • Given a problem, find other solutionsGiven a problem, find other solutions – Given a nail, find all hammersGiven a nail, find all hammers – Sometimes even screwdrivers and pliers may workSometimes even screwdrivers and pliers may work • High Dynamic Range Tone MappingHigh Dynamic Range Tone Mapping – Started with Jack Tumblin’s LCISStarted with Jack Tumblin’s LCIS – Gradient domainGradient domain – Bilateral filterBilateral filter – Filter banks etc ..Filter banks etc .. – About 6 years of heavy machineryAbout 6 years of heavy machinery – Btw, the topic is done to death but continues to enthuseBtw, the topic is done to death but continues to enthuse
  60. 60. Ramesh Raskar, MIT Media Lab Strategy #6: X++Strategy #6: X++ • Pick your adjective ..Pick your adjective .. • Making it faster, better, cheaperMaking it faster, better, cheaper neXt = adjective + XneXt = adjective + X
  61. 61. Ramesh Raskar, MIT Media Lab X++ : Add your favorite adjectiveX++ : Add your favorite adjective • Context aware,Context aware, • AdaptiveAdaptive • (temporally) Coherent,(temporally) Coherent, • Hierarchical,Hierarchical, • ProgressiveProgressive • EfficientEfficient • ParallelizedParallelized • DistributedDistributed • Good example: Image or video compression schemesGood example: Image or video compression schemes • But X++ is a bad signBut X++ is a bad sign – The field is dying in terms of research but booming in business impactThe field is dying in terms of research but booming in business impact
  62. 62. Ramesh Raskar, MIT Media Lab PitfallsPitfalls • These six ways are only a startThese six ways are only a start • They are a good mental exercise and willThey are a good mental exercise and will allow you to train as a researcherallow you to train as a researcher • Great for class projectsGreat for class projects • ButBut – Maynot produce radically new ideasMaynot produce radically new ideas – Sometimes a danger of being labeled incrementalSometimes a danger of being labeled incremental – Could be into ‘public domain ideas’Could be into ‘public domain ideas’
  63. 63. Ramesh Raskar, MIT Media Lab What are Bad ideas to pursueWhat are Bad ideas to pursue • X then Y (then Z)X then Y (then Z) – X+Y is great with true fusion, fusion of dissimilar is bestX+Y is great with true fusion, fusion of dissimilar is best – But avoid a ‘pipeline’ systems paper, where the output ofBut avoid a ‘pipeline’ systems paper, where the output of one is THEN channeled into the input of the next stage,one is THEN channeled into the input of the next stage, and non of the components are noveland non of the components are novel – E.g. I want to build aE.g. I want to build a • Follow the hype (too much competition)Follow the hype (too much competition) • Do because it can be doneDo because it can be done – (Why do we climb? because it is there!(Why do we climb? because it is there! – But only the first one gets a credit.But only the first one gets a credit. – May make you strong, and give you a sense ofMay make you strong, and give you a sense of achievement but not a research project. )achievement but not a research project. )
  64. 64. Ramesh Raskar, MIT Media Lab Xd X++ X X+Y X X neXt Ramesh Raskar, MIT Media Lab
  65. 65. Raskar, Camera Culture, MIT Media Lab Camera Culture Ramesh Raskar Camera Culture MIT Media Lab http://raskar.info http://cameraculture.info

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