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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
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
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
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
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
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
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 …

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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  • 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.
  • http://raskar.info
    http://cameraculture.media.mit.edu
  • Transcript

    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. Flutter Shutter: Shutter is OPEN and CLOSED Preserves High Spatial Frequencies Sharp Photo Blurred Photo PSF == Broadband Function Fourier Transform
    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. Out of Focus Photo: Coded Aperture
    17. Captured Blurred Photo
    18. Refocused on Person
    19. Larval Trematode WormLarval Trematode Worm
    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. 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. 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. 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. Ramesh Raskar, MIT Media Lab
    25. Ramesh Raskar, MIT Media Lab Example of digital refocusingExample of digital refocusing
    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. Mask in a Camera Mask Aperture Canon EF 100 mm 1:1.28 Lens, Canon SLR Rebel XT camera
    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. Ramesh Raskar, MIT Media Lab • e.g. Reverse Auctione.g. Reverse Auction
    30. Less is MoreLess is More Blocking Light == More InformationBlocking Light == More Information Coding in TimeCoding in Time Coding in SpaceCoding in Space
    31. Larval Trematode WormLarval Trematode Worm
    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. 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. 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. 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. 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. 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. Mitsubishi Electric Research Laboratories Special Effects in the Real World Raskar 2006 Inside of Multi-LED Emitter
    39. Mitsubishi Electric Research Laboratories Special Effects in the Real World Raskar 2006 Tag
    40. Ramesh Raskar, MIT Media Lab • When life gives you lemon, make lemonadeWhen life gives you lemon, make lemonade
    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. Depth Discontinuities Internal and external Shape boundaries, Occluding contour, Silhouettes
    43. Depth Edges
    44. Our MethodCanny
    45. Canny Intensity Edge Detection Our Method Photo Result
    46. Car Manuals
    47. What are the problems with ‘real’ photo in conveying information ? Why do we hire artists to draw what can be photographed ?
    48. Shadows Clutter Many Colors Highlight Shape Edges Mark moving parts Basic colors
    49. Shadows Clutter Many Colors Highlight Edges Mark moving parts Basic colors A New ProblemA New Problem
    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. A Night Time Scene: Objects are Difficult to Understand due to Lack of Context Dark Bldgs Reflections on bldgs Unknown shapes
    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. Background is captured from day-time scene using the same fixed camera Night Image Day Image Result: Enhanced Image
    54. Flash Result Reflection LayerAmbient Flash and Ambient ImagesFlash and Ambient Images [ Agrawal, Raskar, Nayar, Li Siggraph05 ][ Agrawal, Raskar, Nayar, Li Siggraph05 ]
    55. Agrawala et al, Digital Photomontage, Siggraph 2004
    56. Agrawala et al, Digital Photomontage, Siggraph 2004
    57. actual photomontageset of originals perceived
    58. Source images Brush strokes Computed labeling Composite
    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. 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. 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. 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. 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. Ramesh Raskar, MIT Media Lab Xd X++ X X+Y X X neXt Ramesh Raskar, MIT Media Lab
    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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