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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. ...

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 Raskar Associate Professor MIT Media Lab http://raskar.info http://cameraculture.info http://raskar.info http://cameraculture.info
  • Ramesh Raskar Associate 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

How to come up with new Ideas Raskar Feb09 Presentation Transcript

  • 1. After X , what is ne X t Coming up with New Ideas in Imaging Ramesh Raskar, MIT Media Lab
  • 2. X d X++ X X+Y X X ne X t Ramesh Raskar, MIT Media Lab
  • 3. 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. Camera Culture Course WebPage : http:// cameraculture.info /courses/
  • 6. After X , what is ne X t Coming up with New Ideas in Imaging Ramesh Raskar, MIT Media Lab
  • 7. X d X++ X X+Y X X ne X t Ramesh Raskar, MIT Media Lab
  • 8. Simple Exercise .. What is ne X t
  • 9. Strategy #1: X d
    • Extend it to next dimension (or some other) dimension
    • Context aware resizing
      • Video
      • Instead of square resizing-> CD cover (with a hole in center) resizing
    • Text, Audio (Speech), Image, Video .. Whats next ?
    • Video, 3D meshes, 4D lightfields
    • Images to infrared, sound, ultrasound
    • Macro scale to microscale (Levoy, Lightfield to Microscope)
    • Time to space to angle to id
    • (coded exposure <- coded aperture)
  • 10. Coded-Aperture Imaging
    • Lens-free imaging!
    • Pinhole-camera sharpness, without massive light loss.
    • No ray bending (OK for X-ray, gamma ray, etc.)
    • Two elements
      • Code Mask: binary (opaque/transparent)
      • Sensor grid
    • Mask autocorrelation is delta function (impulse)
    • Similar to MotionSensor ?
  • 11. Flutter 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 Sharp Photo Blurred Photo PSF == Broadband Function Fourier Transform Preserves High Spatial Frequencies
  • 16. Coded 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
  • 17. Out of Focus Photo: Coded Aperture
  • 18. Captured Blurred Photo
  • 19. Refocused on Person
  • 20. Larval Trematode Worm
  • 21. Strategy #2: X+Y
    • Fusion of the dissimilar
      • More dissimilar, more spectacular the output
    • Example
      • Scientific imaging + Photography
        • Coded aperture
        • Tomography
    • Lightfields + User interfaces
    • Projector = camera
      • Spatial Augmented Reality
  • 22. Imaging in Sciences: Computer Tomography
    • http://info.med.yale.edu/intmed/cardio/imaging/techniques/ct_imaging/
  • 23. Borehole tomography
    • receivers measure end-to-end travel time
    • reconstruct to find velocities in intervening cells
    • must use limited-angle reconstruction method (like ART)
    (from Reynolds)
  • 24. Prototype camera
    • 4000 × 4000 pixels ÷ 292 × 292 lenses = 14 × 14 pixels per lens
    Contax medium format camera Kodak 16-megapixel sensor Adaptive Optics microlens array 125 μ square-sided microlenses
  • 25.  
  • 26. Example of digital refocusing
  • 27. Coded-Aperture Imaging
    • Lens-free imaging!
    • Pinhole-camera sharpness, without massive light loss.
    • No ray bending (OK for X-ray, gamma ray, etc.)
    • Two elements
      • Code Mask: binary (opaque/transparent)
      • Sensor grid
    • Mask autocorrelation is delta function (impulse)
    • Similar to MotionSensor ?
  • 28. Mask in a Camera Mask Aperture Canon EF 100 mm 1:1.28 Lens, Canon SLR Rebel XT camera
  • 29. Strategy #3: X Do exactly the opposite
    • Processing, Memory, Bandwidth
      • In Computing world, in any era, one of this is a bottleneck
      • But overtime, they change. You can often take an older idea and do exactly the opposite.
      • E.g. bandwidth is now considered virtually limitless
    • In imaging:
      • Larger sensors?
        • Everyone is thinking about building cheaper, smaller pixel sensors and THEN improving SNR .. Maybe just build larger sensors?
      • SLR: Faster mirror flip or no mirror flip
        • Companies spent years improving mirror flip speed
        • Why not just remove it?
    • More computation
    • Less light
  • 30.
    • e.g. Reverse Auction
  • 31. Less is More Blocking Light == More Information Coding in Time Coding in Space
  • 32. Larval Trematode Worm
  • 33. 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. 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. Imperceptible Tags under clothing, tracked under ambient light Hidden Marker Tags Outdoors Unique Id
  • 37. Labeling Space (Indoor GPS) Each location receives a unique temporal code But 60Hz video projector is too slow Projector Tags Pos=0 Pos=25 5 Time
  • 38. Pattern MSB Pattern MSB-1 Pattern LSB
    • For each tag
    • From light sequence, decode x and y coordinate
    • Transmit back to RF reader ( Id , x, y )
    0 1 1 0 0 X=12
  • 39. Inside of Multi-LED Emitter
  • 40. Tag
  • 41.
    • When life gives you lemon, make lemonade
  • 42. Depth Edge Camera
  • 43.  
  • 44. Ramesh Raskar, Karhan Tan, Rogerio Feris, Jingyi Yu, Matthew Turk Mitsubishi Electric Research Labs (MERL), Cambridge, MA U of California at Santa Barbara U of North Carolina at Chapel Hill Non-photorealistic Camera: Depth Edge Detection and Stylized Rendering using Multi-Flash Imaging
  • 45.  
  • 46.  
  • 47.  
  • 48.  
  • 49. Depth Discontinuities Internal and external Shape boundaries, Occluding contour, Silhouettes
  • 50. Depth Edges
  • 51. Our Method Canny
  • 52. Canny Intensity Edge Detection Our Method Photo Result
  • 53.  
  • 54.  
  • 55. Car Manuals
  • 56. What are the problems with ‘real’ photo in conveying information ? Why do we hire artists to draw what can be photographed ?
  • 57. Shadows Clutter Many Colors Highlight Shape Edges Mark moving parts Basic colors
  • 58. Shadows Clutter Many Colors Highlight Edges Mark moving parts Basic colors A New Problem
  • 59. Canny Intensity Edge Detection Our Method
  • 60.  
  • 61.  
  • 62. Strategy #4: X
    • Given a Hammer ..
      • Find all the nails
      • Sometimes even screws and bolts
    • Given a cool solution/technique,
      • find other problems
    • Good recent examples
      • Gradient domain techniques
        • Introduced in Graphics for High dynamic range tone mapping [Fattal Lischinski 2002]
        • Now a major hammer
          • Image editing, compositing, fusion, alpha matting, reflection layer recovery
  • 63. A Night Time Scene: Objects are Difficult to Understand due to Lack of Context Dark Bldgs Reflections on bldgs Unknown shapes
  • 64. Enhanced Context : All features from night scene are preserved, but background in clear ‘ Well-lit’ Bldgs Reflections in bldgs windows Tree, Street shapes
  • 65. Background is captured from day-time scene using the same fixed camera Night Image Day Image Result: Enhanced Image
  • 66. Flash Result Reflection Layer Ambient Flash and Ambient Images [ Agrawal, Raskar, Nayar, Li Siggraph05 ]
  • 67. Agrawala et al, Digital Photomontage, Siggraph 2004
  • 68.  
  • 69.  
  • 70.  
  • 71.  
  • 72. Agrawala et al, Digital Photomontage, Siggraph 2004
  • 73. Agrawala et al, Digital Photomontage, Siggraph 2004
  • 74. actual photomontage set of originals perceived
  • 75. Source images Brush strokes Computed labeling Composite
  • 76. Strategy #5: X
    • Given a problem, find other solutions
      • Given a nail, find all hammers
      • Sometimes even screwdrivers and pliers may work
    • High Dynamic Range Tone Mapping
      • Started with Jack Tumblin’s LCIS
      • Gradient domain
      • Bilateral filter
      • Filter banks etc ..
      • About 6 years of heavy machinery
      • Btw, the topic is done to death but continues to enthuse
  • 77. Strategy #6: X++
    • Pick your adjective ..
    • Making it faster, better, cheaper
    • neXt = adjective + X
  • 78. X++ : Add your favorite adjective
    • Context aware,
    • Adaptive
    • (temporally) Coherent,
    • Hierarchical,
    • Progressive
    • Efficient
    • Parallelized
    • Distributed
    • Good example: Image or video compression schemes
    • But X++ is a bad sign
      • The field is dying in terms of research but booming in business impact
  • 79. Pitfalls
    • These six ways are only a start
    • They are a good mental exercise and will allow you to train as a researcher
    • Great for class projects
    • But
      • Maynot produce radically new ideas
      • Sometimes a danger of being labeled incremental
      • Could be into ‘public domain ideas’
  • 80. What are Bad ideas to pursue
    • X then Y (then Z)
      • X+Y is great with true fusion, fusion of dissimilar is best
      • But avoid a ‘pipeline’ systems paper, where the output of one is THEN channeled into the input of the next stage, and non of the components are novel
      • E.g. I want to build a
    • Follow the hype (too much competition)
    • Do because it can be done
      • (Why do we climb? because it is there!
      • But only the first one gets a credit.
      • May make you strong, and give you a sense of achievement but not a research project. )
  • 81. X d X++ X X+Y X X ne X t Ramesh Raskar, MIT Media Lab
  • 82. Camera Culture Ramesh Raskar Camera Culture MIT Media Lab http://raskar.info http://cameraculture.info