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
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How to come up with new Ideas Raskar Feb09
1. Ramesh Raskar, MIT Media Lab
After X, what is neXt
Coming up with
New Ideas in Imaging
Ramesh Raskar, MIT Media Lab
2. Ramesh Raskar, MIT Media Lab
Xd
X++
X X+Y
X
X
neXt
Ramesh Raskar, MIT Media Lab
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. 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. Ramesh Raskar, MIT Media Lab
Camera CultureCamera Culture
Course WebPage :
http://cameraculture.info/courses/
6. Ramesh Raskar, MIT Media Lab
After X, what is neXt
Coming up with
New Ideas in Imaging
Ramesh Raskar, MIT Media Lab
7. Ramesh Raskar, MIT Media Lab
Xd
X++
X X+Y
X
X
neXt
Ramesh Raskar, MIT Media Lab
8. Ramesh Raskar, MIT Media Lab
Simple Exercise ..Simple Exercise ..
What is neXt
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. 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. Flutter Shutter CameraFlutter Shutter Camera
Raskar, Agrawal, Tumblin [Siggraph2006]
LCD opacity switched
in coded sequence
12. Figure 2 results
Input Image
Problem: Motion Deblurring
Ramesh Raskar, Camera Culture, MIT
Media Lab
13. Image Deblurred by solving a linear system. No post-processing
Blurred Taxi
Ramesh Raskar, Camera Culture, MIT
Media Lab
14.
15. Flutter Shutter: Shutter is OPEN and CLOSED
Preserves High Spatial
Frequencies
Sharp Photo Blurred Photo
PSF == Broadband Function
Fourier
Transform
16. 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
21. 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
22. 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/
23. 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)
24. 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
26. Ramesh Raskar, MIT Media Lab
Example of digital refocusingExample of digital refocusing
27. 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
28. Mask in a Camera
Mask
Aperture
Canon EF 100 mm 1:1.28 Lens,
Canon SLR Rebel XT camera
29. 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
31. Less is MoreLess is More
Blocking Light == More InformationBlocking Light == More Information
Coding in TimeCoding in Time Coding in SpaceCoding in Space
33. 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
34. 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
35. 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
36. 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
37. 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
38. 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
39. Mitsubishi Electric Research Laboratories Special Effects in the Real World Raskar 2006
Inside of Multi-LED Emitter
41. Ramesh Raskar, MIT Media Lab
• When life gives you lemon, make lemonadeWhen life gives you lemon, make lemonade
42.
43. 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
57. 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
58. A Night Time Scene:
Objects are Difficult to Understand due to Lack of Context
Dark Bldgs
Reflections on
bldgs
Unknown
shapes
59. Enhanced Context :
All features from night scene are preserved, but background in clear
‘Well-lit’ Bldgs
Reflections in
bldgs windows
Tree, Street
shapes
60. Background is captured from day-time
scene using the same fixed camera
Night Image
Day Image
Result: Enhanced Image
61. Flash Result Reflection LayerAmbient
Flash and Ambient ImagesFlash and Ambient Images
[ Agrawal, Raskar, Nayar, Li Siggraph05 ][ Agrawal, Raskar, Nayar, Li Siggraph05 ]
70. 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
71. 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
72. 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
73. 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’
74. 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. )
75. Ramesh Raskar, MIT Media Lab
Xd
X++
X X+Y
X
X
neXt
Ramesh Raskar, MIT Media Lab
76. Raskar, Camera Culture, MIT Media Lab
Camera Culture
Ramesh Raskar
Camera Culture
MIT Media Lab
http://raskar.info
http://cameraculture.info
Notas do Editor
Six ways of coming up with new ideas based on an idea ‘X’.
Ramesh RaskarAssociate Professor
MIT Media Lab
http://raskar.info
http://cameraculture.info
http://raskar.info
http://cameraculture.info
Ramesh RaskarAssociate Professor
MIT Media Lab
http://raskar.info
http://cameraculture.info
License plate example: Blur = 60 pixels
Can you guess what the car make is ? How many think it is the Audi ? Actually it is a Folksvagon.
Coded exposure makes the filter broadband
Reversibly encode all the information in this otherwise blurred photo
The glint out of focus shows the unusual pattern.
Shielded by screening pigment. The visual organ provides no spatial information, but by comparing the signal from 2 organs or by moving the body, the worm can navigate towards brighter or darker places. It can also keep certain body orientation. Despite lack of spatial vision, this is an evolutionary forerunner to real eyes.
Shielded by screening pigment. The visual organ provides no spatial information, but by comparing the signal from 2 organs or by moving the body, the worm can navigate towards brighter or darker places. It can also keep certain body orientation. Despite lack of spatial vision, this is an evolutionary forerunner to real eyes.
Talk about limitations: Colocated artifacts, color coherency, ref can’t be obtain by subtraction
When we take a photograph of a group of people, such as this image on the left, what we get is a frozen moment of time that is often less natural, and less attractive than the scene we remember. This is because the cognitive processes that form our visual memories integrate over a range of time to form a subjective impression. This memory will likely look a lot more like the image on the right, where everyone is smiling naturally.
The goal of our photomontage system is to help us create photographs that better match the image we see in our mind’s eye. To do so, we begin with a stack of images, and combine the best parts of each to form an image that is better than any of the originals.