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Camera Culture Ramesh  Raskar Camera Culture Associate Professor, MIT Media Lab Photography  Wishlist  http://raskar.info
Film-like  Digital Photography
Fernald,  Science [Sept 2006] Shadow Refractive Reflective
‘ film-like’ Photography Lens Detector Pixels Image Slide by Shree Nayar Reproduce for the eye
Wish List Today ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Computational Photography Computational Illumination Computational Camera Scene :  8D Ray Modulator Display Generalized Sensor Generalized Optics Processing Ray Bender Ray Sampler Ray  Reconstruction Generalized Optics Recreate 4D Lightfield Light Sources Modulators 4D Incident Lighting 4D Light Field
Can you look around a corner ?
 
Computational Photography  [Raskar and Tumblin] ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],captures  a machine-readable representation of our world to hyper-realistically  synthesize  the essence of our visual experience.
Camera Culture Ramesh  Raskar Camera Culture Associate Professor, MIT Media Lab Computational  Photography Wish List http://raskar.info
Synthesis Low Level Mid Level High Level Hyper realism Raw Angle, spectrum aware Non-visual Data, GPS Metadata Priors Comprehensive 8D reflectance field Computational Photography Digital Epsilon Coded Essence Computational Photography aims to make progress on both axis Camera Array HDR,  FoV Focal stack Decomposition problems Depth Spectrum LightFields Human Stereo Vision Transient Imaging Virtual Object Insertion Relighting Augmented Human Experience Material editing from single photo Scene completion from photos Motion Magnification Phototourism Resolution
Camera Culture Ramesh  Raskar Wish #1 Ultimate Post-capture Control
Digital Refocusing using  Light Field Camera 125 μ  square-sided microlenses [Ng et al 2005]
Zooming into the raw photo
Mask based Light Field Camera [Veeraraghavan, Raskar, Agrawal, Tumblin, Mohan, Siggraph 2007 ] Mask Sensor
Captured 2D Photo
x y u v 121 Sub-aperture Views
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Microlens array Sensor Plenoptic Camera Heterodyne Camera Mask Sensor
Figure 2 results Input Image Motion Blur in Low Light
Motion Blur in Low Light
Fluttered Shutter Camera Raskar, Agrawal, Tumblin Siggraph2006 Ferroelectric shutter in front of the lens is turned opaque or transparent in a rapid binary sequence
Image Deblurred by solving a linear system. No post-processing Blurred Taxi
Motion Blur in Low Light
Compact Programmable Lights ?
Image-Based Re-lighting Measure incoming light in Milan, Light the actress in LA Matte the background Matched LA and Milan lighting. Debevec et al., SIGG2001
Camera Culture Ramesh  Raskar ,[object Object],[object Object],[object Object],[object Object]
Camera Culture Ramesh  Raskar Wish #2 Freedom  from  Form
Convert LCD into a big  flat camera ? Beyond Multi-touch: 3D Gestures
Large Virtual Camera for  3D Interactive HCI and  Video Conferencing Matthew Hirsch, Henry Holtzman Doug Lanman, Ramesh Raskar Siggraph Asia 2009 BiDi Screen
Touch + Hover using Thin, Depth Sensing LCD Sensor
Sensing Depth from    Array of Virtual Cameras in LCD
Shallow DoF with Simple Lens Lots of glass; Heavy; Bulky; Expensive
Image Destabilization: Programmable Defocus using Lens and Sensor Motion Ankit Mohan, Douglas Lanman, Shinsaku Hiura, Ramesh Raskar MIT Media Lab MIT Media Lab Camera Culture
Image  De stabilization Lens Sensor Camera Static Scene
Image Destabilization Static Scene Lens Motion Sensor Motion Camera Mohan, Lanman,Hiura, Raskar ICCP 2009
Shifting Pinhole and Sensor A B A’ B’ Pinhole v p Sensor v s d a d b d s Focus Here
Shifting Pinhole and Sensor A B Pinhole A’ B’ v p Sensor v s d a d b d s Focus Here
Shifting Pinhole and Sensor A B Pinhole A’ B’ v p Sensor v s d a d b d s Focus Here
Shifting Pinhole and Sensor A B Pinhole A’ B’ v p Sensor v s d a d b d s Focus Here
“ Time Lens” Ratio of speeds Lens Equation: Virtual Focal Length: Virtual F-Number:
Time Lens:
Small Aperture Photo all-in-focus
Destabilized Small Aperture focused in the front using destabilization
Adjusting the Focus Plane focused in the middle using destabilization
Adjusting the Focus Plane focused in the back using destabilization
Camera Culture Ramesh  Raskar Wish #3 Understand the World
Real  or  Fake ?
Direct Reflection
Fake  Real Internal  Reflection
High Frequency Pattern surface Camera Flash Nayar, Krishnan, Grossberg, Raskar [Siggraph 2006] i
Convert single 2D photo into 3D ? Snavely, Seitz, Szeliski U of Washington/Microsoft: Photosynth
Exploit Community Photo Collections U of Washington/Microsoft: Photosynth
Can you look around a corner ?
Can you ‘see’ around a corner ?
Femto-Photography:  Higher Dimensional Capture FemtoFlash UltraFast Detector Computational Optics Serious Sync
 
Bokode
 
Defocus blur of Bokode
Coding in Angle Mohan, Woo, Smithwick, Hiura, Raskar [Siggraph 2009]
[object Object],camera Bokode (angle) Quote suggested by Kurt Akeley Encoding in  Angle , not space, time or wavelength sensor
[object Object],Street-view  Tagging
capturing Bokodes cell-phone camera close to the Bokode (10,000+ bytes of data)
Camera Culture Ramesh  Raskar ,[object Object],[object Object],[object Object],[object Object],[object Object]
Camera Culture Ramesh  Raskar Wish #4 Sharing Visual Experience
Camera Culture Ramesh  Raskar ,[object Object],[object Object],[object Object],[object Object],[object Object]
6D Photo Frames One Pixel of a 6D Display = 4D Display Single  Pixel of  6D Frame 1 2 1 1 2D 2D 2D
6D Photo Frames:  Respond to Viewpoint + Ambient Light
Camera Culture Ramesh  Raskar ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Camera Culture Ramesh  Raskar Wish #5 Capturing Essence
Essence Photography ,[object Object],[object Object],Yu, McMillan
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
Depth Edges with MultiFlash Raskar, Tan, Feris, Jingyi Yu, Turk –  ACM SIGGRAPH 2004
 
 
 
 
Depth Discontinuities Internal and external Shape boundaries, Occluding contour, Silhouettes
Depth  Edges
Our Method Canny
Canny Intensity  Edge Detection Our Method Photo Result
 
 
 
Blind Camera Sascha Pohflepp,  U of the Art, Berlin, 2006
Scene Completion Using Millions of Photographs Hays and Efros, Siggraph 2007
Camera Culture Ramesh  Raskar Wish #5 …………………
Photos of tomorrow:  computed not recorded http://scalarmotion.wordpress.com/2009/03/15/propeller-image-aliasing/
Questions ,[object Object],[object Object],[object Object],[object Object],[object Object]
2 nd  International Conference on  Computational Photography Papers due  November 2, 2009 http://cameraculture.media.mit.edu/iccp10
[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Camera Culture Group, MIT Media Lab  Ramesh  Raskar  http://raskar.info Computational Photography Wish List Sensor

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Raskar 6Sight Keynote Talk Nov09

Editor's Notes

  1. http://raskar.info http://cameraculture.info Beyond making it faster and cheaper, one can argue that digital photography has barely changed how we capture and share visual experiences.
  2. Kodak DCS400 in Nikon F3 body in early 90’s Commendable first 1.3MP digital but film cartridge still there! (First one in 1991 but even in 1995 the space for cartridge) Quote from Jack Tumblin Digital photography is like a caged lion that is uncaged in a jungle after years .. The lion stays in place rather than rushing out to explore Billion cameras but they all look like human eye KODAK Professional Digital Camera DCS-100: a camera back and camera winder fitted to an unmodified Nikon F3 camera
  3. CPUs and computers don’t mimic the human brain. And robots don’t mimic human activities. Should the hardware for visual computing which is cameras and capture devices, mimic the human eye? Even if we decide to use a successful biological vision system as basis, we have a range of choices. For single chambered to compounds eyes, shadow-based to refractive to reflective optics. So the goal of my group at Media Lab is to explore new designs and develop software algorithms that exploit these designs.
  4. currently we solve the human visual perception problem by simply reproducing what the eye would see. (even for 3D, we show stereo pair) But this makes it difficult to understand or manipulate for computers. (machine readable rep)
  5. Wishlist by consumers and companies today .. i.e. what is NOT available today but they wish it was So, I am not including Wifi, GPS, face detection etc in the list here. But let us dream beyond this list.
  6. 4 blocks : light, optics, sensors, processing, (display: light sensitive display) + 5 th element: Network
  7. Lets dream big .. Can we look around at something beyond the line of sight?
  8. Can photos become emotive abstract renderings ?
  9. Inference and perception are important. Intent and goal of the photo is important. Computational Photography pioneered by Nayar, Levoy, Debevec, Microsoft Research et al in late-90’s. The same way camera put photorealistic art out of business, maybe this new artform will put the traditional camera out of business. Because we wont really care about a photo, merely a recording of light but a form that captures meaningful subset of the visual experience. Multiperspective photos. Photosynth is an example.
  10. My own wishlist .. By defn these problems are not solved .. But I will try to give you a flavor of things my group and others are doing in that direction
  11. See computationalphotography.org
  12. Simplify capture time decisions Fix everything in post if necessary
  13. Stanford Plenoptic Camera
  14. But you lose a lot of resolution, 16M reduced to 300x300 pixels
  15. We can now create lightfield cameras by mechanism that don’t introduce new lenses. http://raskar.info/Mask/
  16. Photo with Mask
  17. Sub aperture views From these 121 views we can create stereo movies, compute depth, achieve digital refocusing etc.
  18. The mask based lightfield camera is more suitable for post-capture control in many ways.
  19. Wish: low light photography to deal with motion blur
  20. License plate example: Blur = 60 pixels Can you guess what the car make is ?
  21. Rudy Burger, ‘don’t use flash and destroy the image’ Can we use flash not just for improving scene brightness but for enhancing the mood? Like in studio lights? Main difference between professionals and consumers is lighting.
  22. Debevec et al have shown for special effects terrific relighting strategies. Wish: do the same with a compact light source and exploit natural lighting.
  23. Mechanical size and restrictions. Can we create a flat camera? But still capture enough light ? Current strategy is to shrink the camera in all dimensions: making it flat also means a tiny lens. Origami lens (Montage program, Joe Ford UCSD, ) Bidi Screen Image Destabilization New exciting projects at Stanford (Marc Levoy’s Open Source Camera) Shree Nayar’s BigShot ‘lego-camera’
  24. How to exploit Sharp’s photosensing LCD originally designed for touch sensing and convert into a large area flat camera Since we are adapting LCD technology we can fit a BiDi screen into laptops and mobile devices.
  25. So here is a preview of our quantitative results. I’ll explain this in more detail later on, but you can see we’re able to accurately distinguish the depth of a set of resolution targets. We show above a portion of portion of views form our virtual cameras, a synthetically refocused image, and the depth map derived from it.
  26. Many are working on extending depth of field. But consumers pay quite a bit to actually reduce the depth of field. Can we support both options?
  27. = Material index and compute bounces (real vs fake) = Automatic 3D, phototourism, and 3D awareness (look around a corner) = Find relationship (network) between all photos = Understand the world (recognize, categorize, make world smarter bokode)
  28. Nayar, Krishnan, Grossberg, Raskar [Siggraph 2006]
  29. Nayar, Krishnan, Grossberg, Raskar [Siggraph 2006]
  30. Bokode.com
  31. = Material index and compute bounces (real vs fake) = Automatic 3D, phototourism, and 3D awareness (look around a corner) = Find relationship (network) between all photos = Understand the world (recognize, categorize, make world smarter bokode)
  32. Life Sharing = Automatic lifelogs and summaries (capture and render from other viewpoints, possibly retinal implants) = Privacy in public (smart probes, good recognition) and authentication = Ultimate photoframes, Print 3D and relightable photos (6D), print any material
  33. Life Sharing = Automatic lifelogs and summaries (capture and render from other viewpoints, possibly retinal implants) = Privacy in public (smart probes, good recognition) and authentication = Ultimate photoframes, Print 3D and relightable photos (6D), print any material
  34. Martin Fuchs, Ramesh Raskar, Hans-Peter Seidel, Hendrik P. A. Lensch Siggraph 2008
  35. This video is only for 4D display that responds to light Bonny’s lenticular prints outside
  36. Life Sharing = Automatic lifelogs and summaries (capture and render from other viewpoints, possibly retinal implants) = Privacy in public (smart probes, good recognition) and authentication = Ultimate photoframes, Print 3D and relightable photos (6D), print any material
  37. A great artifact in musuem which you can hold and observe Or that ride on a roller coaster Synthesize a new experience = Photoediting using learned models of earlier stroke activities, image filling etc = Artistic effects and NPR (like MS Word, but artistic ability to express) = Blind camera
  38. Inference and perception are important. Intent and goal of the photo is important. The same way camera put photorealistic art out of business, maybe this new artform will put the traditional camera out of business. Because we wont really care about a photo, merely a recording of light but a form that captures meaningful subset of the visual experience. Multiperspective photos. Photosynth is an example.
  39. Alexis Gerard threatens us that he has this camera which is so advanced, you don’t even need it .. Maybe all the consumer photographer wants is a black box with big red button. No optics, sensors or flash. If I am standing the middle of times square and I need to take a photo. Do I really need a fancy camera?
  40. The camera can trawl on flickr and retrieve a photo that is roughly taken at the same position, at the same time of day. Maybe all the consumer wants is a blind camera.
  41. Your wish here .. New Digital Imaging Workflow? Emerging opportunities with smarter cameras? Share over lunch, beer and cocktails
  42. photos will be computed rather than recorded Comp photo will be there It will change the workflow, just with digital many pipeline have turned upside down and we will even more At the same time with cameras that understand our world better, there will be a lot of new opportunities http://scalarmotion.wordpress.com/2009/03/15/propeller-image-aliasing/
  43. http:// ComputationalPhotography.org
  44. http://cameraculture.info http://raskar.info