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Raskar Computational Camera Fall 2009 Lecture 01

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    Notes on slide 1

    http://scalarmotion.wordpress.com/2009/03/15/propeller-image-aliasing/

    See http://www1.cs.columbia.edu/CAVE/projects/separation/occluders_gallery.php

    Since we are adapting LCD technology we can fit a BiDi screen into laptops and mobile devices.

    http://www.youtube.com/watch?v=2CWpZKuy-NE

    About the Camera Culture group (http://cameraculture.info)

    My own background

    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 blocks : light, optics, sensors, processing, (display: light sensitive display)

    4 blocks : light, optics, sensors, processing, (display: light sensitive display)

    Panasonic’s Nano Care beauty appliance . Model numbers EH-SA42 and EH-SA41 http://www.slipperybrick.com/2008/12/webcam-equipped-headphones-put-eyes-over-your-ears/

    Build Your Own 3D Scanner 2/18/2009 Image sources: http://meshlab.sourceforge.net/images/screenshots/SnapMeshLab.align1.png

    Build Your Own 3D Scanner 2/18/2009 Image sources: http://community.middlebury.edu/~schar/papers/structlight/p1.html

    Build Your Own 3D Scanner 2/18/2009 Image sources: http://blog.makezine.com/archive/2006/10/how_to_build_your_own_3d.html http://www.make-digital.com/make/vol14/?pg=195 http://www.shapeways.com/blog/uploads/david-starter-kit.jpg http://www.shapeways.com/blog/archives/248-DAVID-3D-Scanner-Starter-Kit-Review.html#extended http://www.david-laserscanner.com/ http://www.youtube.com/watch?v=XSrW-wAWZe4 http://www.chromecow.com/MadScience/3DScanner/3DScan_02.htm Liquid scanner, various laser scanners

    Check Steve Seitz and U of Washington Phototourism Page

    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.

    For all preliminary info watch videos and see slides at http://raskar.info/photo/ 2008 http://web.media.mit.edu/~raskar/photo/2008/VideoPresentation/ 2007 http://portal.acm.org/citation.cfm?doid=1281500.1281502 Given the multi-disciplinary nature of the course, the class will be open and supportive of students with different backgrounds. Hence, we are going to try a two-track approach for homeworks: one software-intensive and the other with software-hardware (electronics/optics) emphasis.

    Slides on ‘How to Come up With Ideas’ http://stellar.mit.edu/S/course/MAS/fa09/MAS.531/index.html

    cameraculture.media.mit.edu/femtotransientimaging

    Why doesnt food look as yummy as it tastes in most photos?

    0:30

    Taken to the extreme objects off the focal plane become so blurry that they effectively disappear at least if they are smaller than the aperture Leonardo noticed 500 years ago a needle placed in front of his eye because it was smaller than his pupil did not occlude his vision

    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.

    Conventional lenses have a limited depth of field. One can increase the depth of field and reduce the blur by stopping down the aperture. However, this leads to noisy images.

    A solution proposed by authors and now commercialized by CDM optics uses a cubic phase plate. The effect of cubic phase plate is equivalent so summing images due to lens position was different planes of focus. Note that the cubic phase plate can be made up of glass of varying thickness OR glass of varying refractive index. The example here shows a total phase difference of just 8 periods.

    Unlike traditional systems, where you see a conical profile of lightfield for a point in focus, for CDM the profile is more like a twisted cylinder of straws. This makes the point spread function somewhat depth independent.

    put two projectors, one virtual projector at the middle, along this line, connecting the virtual light source, always destructive interference, does it make sense and right? http://raskar.scripts.mit.edu/~raskar/lightfields/

    in wave optics, WDF exhibit similar property, compare the two,

    the motivation, to augment lf, model diffraction in light field formulation

    put two projectors, one virtual projector at the middle, along this line, connecting the virtual light source, always destructive interference, does it make sense and right?

    New techniques are trying decrease this distance using a folded optics approach. The origami lens uses multiple total internal reflection to propagate the bundle of rays.

    Consider a conventional lens: An incoming light ray is first refracted when it enters the shaped lens surface because of the abrupt change of the refractive index from air to the homogeneous material. It passes the lens material in a direct way until it emerges through the exit surface of the lens where it is refracted again because of the abrupt index change from the lens material to air (see Fig. 1, right). A well-defined surface shape of the lens causes the rays to be focussed on a spot and to create the image. The high precision required for the fabrication of the surfaces of conventional lenses aggrevates the miniaturization of the lenses and raises the costs of production. GRIN lenses represent an interesting alternative since the lens performance depends on a continuous change of the refractive index within the lens material. Instead of complicated shaped surfaces plane optical surfaces are used. The light rays are continuously bent within the lens until finally they are focussed on a spot. Miniaturized lenses are fabricated down to 0.2 mm in thickness or diameter. The simple geometry allows a very cost-effective production and simplifies the assembly. Varying the lens length implies an enormous flexibility at hand to fit the lens parameters as, e.g., the focal length and working distance. For example, appropriately choosing the lens length causes the image plane to lie directly on the surface plane of the lens so that sources such as optical fibers can be glued directly onto the lens surface.

    Current explosion in information technology has been derived from our ability to control the flow of electrons in a semiconductor in the most intricate ways. Photonic crystals promise to give us similar control over photons - with even greater flexibility because we have far more control over the properties of photonic crystals than we do over the electronic properties of semiconductors.

    Changes in the index of refraction of air are made visible by Schlieren Optics. This special optics technique is extremely sensitive to deviations of any kind that cause the light to travel a different path. Clearest results are obtained from flows which are largely two-dimensional and not volumetric. In schlieren photography, the collimated light is focused with a lens, and a knife-edge is placed at the focal point, positioned to block about half the light. In flow of uniform density this will simply make the photograph half as bright. However in flow with density variations the distorted beam focuses imperfectly, and parts which have focussed in an area covered by the knife-edge are blocked. The result is a set of lighter and darker patches corresponding to positive and negative fluid density gradients in the direction normal to the knife-edge.

    Full-Scale Schlieren Image Reveals The Heat Coming off of a Space Heater, Lamp and Person

    Precursor to Google Streetview Maps

    The liquid lenses that we develop are based on the electrowetting phenomenon described below : a water drop is deposited on a substrate made of metal, covered by a thin insulating layer. The voltage applied to the substrate modifies the contact angle of the liquid drop. The liquid lens uses two isodensity liquids, one is an insulator while the other is a conductor. The variation of voltage leads to a change of curvature of the liquid-liquid interface, which in turn leads to a change of the focal length of the lens.

    The liquid lenses that we develop are based on the electrowetting phenomenon described below : a water drop is deposited on a substrate made of metal, covered by a thin insulating layer. The voltage applied to the substrate modifies the contact angle of the liquid drop. The liquid lens uses two isodensity liquids, one is an insulator while the other is a conductor. The variation of voltage leads to a change of curvature of the liquid-liquid interface, which in turn leads to a change of the focal length of the lens.

    The liquid lenses that we develop are based on the electrowetting phenomenon described below : a water drop is deposited on a substrate made of metal, covered by a thin insulating layer. The voltage applied to the substrate modifies the contact angle of the liquid drop. The liquid lens uses two isodensity liquids, one is an insulator while the other is a conductor. The variation of voltage leads to a change of curvature of the liquid-liquid interface, which in turn leads to a change of the focal length of the lens.

    Since we are adapting LCD technology we can fit a BiDi screen into laptops and mobile devices.

    Recall that one of our inspirations was this new class of optical multi-touch device. At the top you can see a prototype that Sharp Microelectronics has published. These devices are basically arrays of naked phototransistors. Like a document scanner, they are able to capture a sharp image of objects in contact with the surface of the screen. But as objects move away from the screen, without any focusing optics, the images captured this device are blurred.

    Our observation is that by moving the sensor plane a small distance from the LCD in an optical multitouch device, we enable mask-based light-field capture. We use the LCD screen to display the desired masks, multiplexing between images displayed for the user and masks displayed to create a virtual camera array. I’ll explain more about the virtual camera array in a moment, but suffice to say that once we have measurements from the array we can extract depth.

    This device would of course support multi-touch on-screen interaction, but because it can measure the distance to objects in the scene a user’s hands can be tracked in a volume in front of the screen, without gloves or other fiducials.

    Thus the ideal BiDi screen consists of a normal LCD panel separated by a small distance from a bare sensor array. This format creates a single device that spatially collocates a display and capture surface.

    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.

    http://raskar.info/prakash

    http://cobweb.ecn.purdue.edu/~malcolm/pct/CTI_Ch03.pdf

    http://www.youtube.com/watch?v=2CWpZKuy-NE

    In a confocal laser scanning microscope, a laser beam passes through a light source aperture and then is focused by an objective lens into a small (ideally diffraction limited ) focal volume within a fluorescent specimen. A mixture of emitted fluorescent light as well as reflected laser light from the illuminated spot is then recollected by the objective lens. A beam splitter separates the light mixture by allowing only the laser light to pass through and reflecting the fluorescent light into the detection apparatus. After passing a pinhole , the fluorescent light is detected by a photodetection device (a photomultiplier tube (PMT) or avalanche photodiode ), transforming the light signal into an electrical one that is recorded by a computer.

    While the single photosensor worm, has evolved into worms with multiple photosensors .. They have been evolutionary forerunners to the human eye. So what about the cameras here .. Maybe we are thinking about these initial types in a limited way and They are forerunners to something very exciting.

    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.

    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?

    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.

    1 Favorite & 1 Group

    Raskar Computational Camera Fall 2009 Lecture 01 - Presentation Transcript

    1. Camera Culture Ramesh Raskar MIT Media Lab http:// CameraCulture . info/ MAS 131/ 531 Computational Camera & Photography:
    2. http://scalarmotion.wordpress.com/2009/03/15/propeller-image-aliasing/
    3. Shower Curtain: Diffuser Direct Global
    4. Shower Curtain: Diffuser Direct Global
    5. A Teaser: Dual Photography Scene Photocell Projector
    6. A Teaser: Dual Photography Scene Photocell Projector
    7. A Teaser: Dual Photography Scene Photocell Projector
    8. A Teaser: Dual Photography Scene Photocell Projector Camera
    9. camera The 4D transport matrix: Contribution of each projector pixel to each camera pixel scene projector
    10. camera The 4D transport matrix: Contribution of each projector pixel to each camera pixel scene projector Sen et al, Siggraph 2005
    11. camera The 4D transport matrix: Which projector pixel contribute to each camera pixel scene projector Sen et al, Siggraph 2005 ?
    12. Dual photography from diffuse reflections: Homework Assignment 2 the camera’s view Sen et al, Siggraph 2005
    13. Digital cameras are boring: Film-like Photography
      • Roughly the same features and controls as film cameras
        • zoom and focus
        • aperture and exposure
        • shutter release and advance
        • one shutter press = one snapshot
    14. Improving FILM-LIKE Camera Performance
      • What would make it ‘perfect’ ?
      • Dynamic Range
      • Vary Focus Point-by-Point
      • Field of view vs. Resolution
      • Exposure time and Frame rate
      • What type of ‘Cameras’ will we study?
      • Not just film-mimicking 2D sensors
        • 0D sensors
          • Motion detector
          • Bar code scanner
          • Time-of-flight range detector
        • 1D sensors
          • Line scan camera (photofinish)
          • Flatbed scanner
          • Fax machine
        • 2D sensors
        • 2-1/2D sensors
        • ‘ 3D’ sensors
        • 4D and 6D tomography machines and displays
    16. Can you look around a corner ?
    17. Convert LCD into a big flat camera? Beyond Multi-touch
    18. Medical Applications of Tomography bone reconstruction segmented vessels Slides by Doug Lanman
    19. Camera Culture Ramesh Raskar Camera Culture
    20. Computational Illumination Curved Planar Non-planar Single Projector Multiple Projectors Pocket-Proj Objects 1998 1998 2002 2002 1999 2002 2003 1998 1997 Computational Photography My Background Projector j User : T ?
    21. Questions
      • What will a camera look like in 10,20 years?
      • How will the next billion cameras change the social culture?
      • How can we augment the camera to support best ‘image search’?
      • What are the opportunities in pervasive recording?
        • e.g. GoogleEarth Live
      • How will ultra-high-speed/resolution imaging change us?
      • How should we change cameras for movie-making, news reporting?
    22. Approach
      • Not just USE but CHANGE camera
        • Optics, illumination, sensor, movement
        • Exploit wavelength, speed, depth, polarization etc
        • Probes, actuators, Network
      • We have exhausted bits in pixels
        • Scene understanding is challenging
        • Build feature-revealing cameras
        • Process photons
    23. Plan
      • What is Computational Camera?
      • Introductions
      • Class format
      • Fast Forward Preview
        • Sample topics
      • First warmup assignment
    24. What is Computational Camera ? - Generate photos that cannot be creates by a single camera at a single instant - Create the ultimate camera that mimics the eye - Create impossible photos that don’t mimic the eye - Learn from scientific imaging (tomography, coded aperture, coherence, phase-contrast)
    25. Fernald, Science [Sept 2006] Shadow Refractive Reflective Tools for Visual Computing
    26. Traditional ‘film-like’ Photography Lens Detector Pixels Image Slide by Shree Nayar
    27. Computational Camera : Optics, Sensors and Computations Generalized Sensor Generalized Optics Computations 4D Ray Bender Upto 4D Ray Sampler Ray Reconstruction Raskar and Tumblin Picture
    28. Novel Cameras Generalized Sensor Generalized Optics Processing
    29. Programmable Lighting Novel Cameras Scene Generalized Sensor Generalized Optics Processing Generalized Optics Light Sources Modulators
    30. Motivation
      • Why Study Cameras Now ?
          • So what .. everyone has in their pocket..
          • Applied Optics has studied every aspect of the lens
          • Sensor electronics has its own field
    31. Motivation
      • Why Computational Cameras Now ?
      • What will be the dominant platform for imaging ?
      • What are the opportunities ?
      • What is different from image processing ?
      • How will it impact social computing ?
          • Supporting computations that are carried out by/for groups of people, blogs, collab-filtering, participatory sensing, soc-nets
    32. Cameras Everywhere
      • 500 Million Camera phones -> 1 Billion
        • Dwarfs most electronic platforms
      • Rapid increase in automated surveillance
      • Next media:
          • Google Earth, YouTube, Flickr ..
          • Text, Speech, Music, Images, Video, 3D, ..
          • Technology and Art will exploit which media next ?
      • Key element for art, research, products, social-computing ..
      • Image processing vs Computational Photo
        • Beyond Post-capture computation
          • What will Photoshop2025 look like ?
          • Do we need to understand the camera ?
            • Aperture, Autofocus, Motion Blur, Bokeh, Sensor parameters, Infrared light
    33. Cameras Everywhere
    34. Where are the ‘cameras’?
    35. Where are the ‘cameras’?
    36. Inflection Point in Camera Usage
    37.  
    38. DIY Green Screen Effects $160 Panasonic Beauty Appliance Minoru Webcam
    39. Fujifilm FinePix 3D (stereo) GigaPan Epic 100
    40. Simply getting depth is challenging ! Godin et al. An Assessment of Laser Range Measurement on Marble Surfaces . Intl. Conf. Optical 3D Measurement Techniques, 2001 M. Levoy. Why is 3D scanning hard? 3DPVT, 2002
      • Must be simultaneously illuminated and imaged (occlusion problems)
      • Non-Lambertian BRDFs (transparency, reflections, subsurface scattering)
      • Acquisition time (dynamic scenes), large (or small) features, etc.
      Lanman and Taubin’09
    41. Contact Non-Contact Active Passive Transmissive Reflective Shape-from-X (stereo/multi-view, silhouettes, focus/defocus, motion, texture, etc.) Active Variants of Passive Methods (stereo/focus/defocus using projected patterns) Time-of-Flight Triangulation (laser striping and structured lighting) Computed Tomography (CT) Transmissive Ultrasound Direct Measurements (rulers, calipers, pantographs, coordinate measuring machines (CMM), AFM) Non-optical Methods (reflective ultrasound, radar, sonar, MRI) Taxonomy of 3D Scanning: Lanman and Taubin’09
    42. DARPA Grand Challenge
    43. Do-It-Yourself (DIY) 3D Scanners Lanman and Taubin’09
    44. What is ‘interesting’ here? Social voting in the real world = ‘popular’
    45. Pantheon
    46. How do we move through a space?
    47. Computational Photography [Raskar and Tumblin]
      • Epsilon Photography
        • Low-level vision: Pixels
        • Multi-photos by perturbing camera parameters
        • HDR, panorama, …
        • ‘ Ultimate camera’
      • Coded Photography
        • Mid-Level Cues:
          • Regions, Edges, Motion, Direct/global
        • Single/few snapshot
          • Reversible encoding of data
        • Additional sensors/optics/illum
        • ‘ Scene analysis’
      • Essence Photography
        • High-level understanding
          • Not mimic human eye
          • Beyond single view/illum
        • ‘ New artform’
      captures a machine-readable representation of our world to hyper-realistically synthesize the essence of our visual experience.
    48. Goal and Experience Low Level Mid Level High Level Hyper realism Raw Angle, spectrum aware Non-visual Data, GPS Metadata Priors Comprehensive 8D reflectance field Digital Epsilon Coded Essence CP 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
    49.  
      • Ramesh Raskar and Jack Tumblin
      • Book Publishers: A K Peters
      • ComputationalPhotography.org
    51. “ Visual Social Computing”
      • Social Computing (SoCo)
        • Computing
        • by the people,
        • for the people,
        • of the people
      • Visual SoCo
        • Participatory, Collaborative
        • Visual semantics
        • http://raskar.scripts.mit.edu / nextbillioncameras
      ? Raskar 2008
    52. Goals
      • Change the rules of the game
        • Emerging optics, illumination, novel sensors
        • Exploit priors and online collections
      • Applications
        • Better scene understanding/analysis
        • Capture visual essence
        • Superior Metadata tagging for effective sharing
        • Fuse non-visual data
          • Sensors for disabled, new art forms, crowdsourcing, bridging cultures
    53. Vein Viewer (Luminetx)
      • Locate subcutaneous veins
    54. Vein Viewer (Luminetx)
      • Near-IR camera locates subcutaneous veins and project their location onto the surface of the skin.
      Coaxial IR camera + Projector
    55.  
    56. Beyond Visible Spectrum Cedip RedShift
      • Format
        • 4 (3) Assignments
          • Hands on with optics, illumination, sensors, masks
          • Rolling schedule for overlap
          • We have cameras, lenses, electronics, projectors etc
          • Vote on best project
        • Mid term exam
          • Test concepts
        • 1 Final project
          • Should be a Novel and Cool
          • Conference quality paper
          • Award for best project
        • Take 1 class notes
        • Lectures (and guest talks)
        • In-class + online discussion
      • If you are a listener
        • Participate in online discussion, dig new recent work
        • Present one short 15 minute idea or new work
      • Credit
          • Assignments: 40%
          • Project: 30%
          • Mid-term: 20%
          • Class participation: 10%
      • Pre-reqs
          • Helpful: Linear algebra, image processing, think in 3D
          • We will try to keep math to essentials, but complex concepts
    58. What is the emphasis?
      • Learn fundamental techniques in imaging
        • In class and in homeworks
        • Signal processing, Applied optics, Computer graphics and vision, Electronics, Art, and Online photo collections
        • This is not a discussion class
      • Three Applications areas
        • Photography
          • Think in higher dimensions 3D, 4D, 6D, 8D, thermal IR, range cam, lightfields, applied optics
        • Active Computer Vision (real-time)
          • HCI, Robotics, Tracking/Segmentation etc
        • Scientific Imaging
          • Compressive sensing, wavefront coding, tomography, deconvolution, psf
        • But the 3 areas are merging and use similar principles
    59. Pre-reqs
      • Two tracks:
        • Supporting students with varying backgrounds
        • A. software-intensive (Photoshop/HDRshop maybe ok)
          • But you will actually take longer to do assignments
        • B. software-hardware (electronics/optics) emphasis.
      • Helpful:
        • Watch all videos on http://raskar.info/photo/
        • Linear algebra, image processing, think in 3D
        • Signal processing, Applied optics, Computer graphics and vision, Electronics, Art, and Online photo collections
      • We will try to keep math to essentials, but introduce complex concepts at rapid pace
      • Assignments versus Class material
        • Class material will present material with varying degree of complexity
        • Each assignments has sub-elements with increasing sophistication
        • You can pick your level
      • Assignments:
      • You are encouraged to program in Matlab for image analysis
      • You may need to use C++/OpenGL/Visual programming for some hardware assignments
      • Each student is expected to prepare notes for one lecture
      • These notes should be prepared and emailed to the instructor no later than the following Monday night (midnight EST). Revisions and corrections will be exchanged by email and after changes the notes will be posted to the website before class the following week.
      • 5 points
      • Course mailing list : Please make sure that your emailid is on the course mailing list
      • Send email to raskar (at) media.mit.edu
      • Please fill in the email/credit/dept sheet
      •  
      • Office hours :
      • Email is the best way to get in touch
      • Ramesh:. raskar (at) media.mit.edu
      • Ankit: ankit (at) media.mit.edu
      • Other mentors: Prof Mukaigawa, Dr Ashok Veeraraghavan
      • After class:
      • Muddy Charles Pub
      • (Walker Memorial next to tennis courts, only with valid id)
    61. 2 Sept 18th Modern Optics and Lenses, Ray-matrix operations 3 Sept 25th Virtual Optical Bench, Lightfield Photography, Fourier Optics, Wavefront Coding 4 Oct 2nd Digital Illumination , Hadamard Coded and Multispectral Illumination 5 Oct 9th Emerging Sensors : High speed imaging, 3D  range sensors, Femto-second concepts, Front/back illumination, Diffraction issues 6 Oct 16th Beyond Visible Spectrum: Multispectral imaging and Thermal sensors, Fluorescent imaging, 'Audio camera' 7 Oct 23rd Image Reconstruction Techniques, Deconvolution, Motion and Defocus Deblurring, Tomography, Heterodyned Photography, Compressive Sensing 8 Oct 30th Cameras for Human Computer Interaction (HCI): 0-D and 1-D sensors, Spatio-temporal coding, Frustrated TIR, Camera-display fusion 9 Nov 6th Useful techniques in Scientific and Medical Imaging: CT-scans, Strobing, Endoscopes, Astronomy and Long range imaging 10 Nov 13th Mid-term  Exam, Mobile Photography, Video Blogging, Life logs and Online Photo collections 11 Nov 20th Optics and Sensing in Animal Eyes. What can we learn from successful biological vision systems? 12 Nov 27th Thanksgiving Holiday (No Class) 13 Dec 4th Final Projects
    62. Topics not covered
      • Only a bit of topics below
      • Art and Aesthetics
          • 4.343 Photography and Related Media
      • Software Image Manipulation
        • Traditional computer vision,
        • Camera fundamentals, Image processing, Learning,
          • 6.815/6.865  Digital and Computational Photography
      • Optics
          • 2.71/2.710 Optics
      • Photoshop
        • Tricks, tools
      • Camera Operation
        • Whatever is in the instruction manual
    63. Courses related to CompCamera
      • Spring 2010:
        • Camera Culture Seminar [Raskar, Media Lab]
          • Graduate seminar
          • Guest lectures + in class discussion
          • Homework question each week
          • Final survey paper (or project)
          • CompCamera class: hands on projects, technical details
        • Digital and Computational Photography [Durand, CSAIL]
          • Emphasis on software methods, Graphics and image processing
          • CompCamera class: hardware projects, devices, beyond visible spectrum/next gen cameras
        • Optics [George Barbastathis, MechE]
          • Fourier optics, coherent imaging
          • CompCamera class: Photography, time-domain, sensors, illumination
        • Computational Imaging (Horn, Spring 2006)
          • Coding, Nuclear/Astronomical imaging, emphasis on theory
    64. Questions ..
      • Brief Introductions
      • Are you a photographer ?
      • Do you use camera for vision/image processing? Real-time processing?
      • Do you have background in optics/sensors?
        • Name, Dept, Year, Why you are here
    66. Goals
      • Play with cameras
        • Thermal, hyperspectral, high speed, 3D ranging, polarization, line-scan, camera-array
      • Write conference quality paper
        • First International Conf on Comp Photo, April 2010
          • (Papers due Nov 2nd)
        • CVPR
          • Nov end
        • Siggraph
          • January
      • Think beyond post-capture computation
      • Build your own Camera toys
    67. 2 nd International Conference on Computational Photography Papers due November 2, 2009 http://cameraculture.media.mit.edu/iccp10
    68. Writing a Conference Quality Paper
      • How to come up with new ideas
        • See slideshow on Stellar site
      • Developing your idea
        • Deciding if it is worth persuing
        • http://en.wikipedia.org/wiki/George_H._Heilmeier#Heilmeier.27s_Catechism
        • What are you trying to do? How is it done today, and what are the limits of current practice? What's new in your approach and why do you think it will be successful? Who cares? If you're successful, what difference will it make? What are the risks and the payoffs? How much will it cost? How long will it take?
      • Last year outcome
        • 3 Siggraph/ICCV submissions, SRC award, 2 major research themes
    69. How to quickly get started writing a paper
      • Abstract
      •   1. Introduction 
      •     Motivation 
      •     Contributions** (For the first time, we have shown that xyz) 
      •     Related Work 
      •     Limitations and Benefits
      •   2. Method 
      •     (For every section as well as paragraph, first sentence should be the 'conlusion' of what that section or paragraph is going to show)
      •   3. More Second Order details (Section title will change)
      •   4. Implementation
      •   5. Results 
      •     Performance Evaluation 
      •     Demonstration
      •   6. Discussion and Issues 
      •     Future Directions
      •   7. Conclusion
    70. Casio EX F1
      • What can it do?
        • Mostly high speed imaging
        • 1200 fps
        • Burst mode
      • Déjà vu (Media Lab 1998) and Moment Camera (Michael Cohen 2005)
      • HDR
      • Movie
    71.  
    72. Cameras and Photography Art, Magic, Miracle
    73. Topics
      • Smart Lighting
        • Light stages, Domes, Light waving, Towards 8D
      • Computational Imaging outside Photography
        • Tomography, Coded Aperture Imaging
      • Smart Optics
        • Handheld Light field camera, Programmable imaging/aperture
      • Smart Sensors
        • HDR Cameras, Gradient Sensing, Line-scan Cameras, Demodulators
      • Speculations
    74. Debevec et al. 2002: ‘Light Stage 3’
    75. Image-Based Actual Re-lighting Film the background in Milan, Measure incoming light, Light the actress in Los Angeles Matte the background Matched LA and Milan lighting. Debevec et al., SIGG2001
    76. Can you look around a corner ?
    77. Can you look around a corner ? Kirmani, Hutchinson, Davis, Raskar 2009 Accepted for ICCV’2009, Oct 2009 in Kyoto Impulse Response of a Scene cameraculture.media.mit.edu/femtotransientimaging
    78. Femtosecond Laser as Light Source Pico-second detector array as Camera
    79. Are BOTH a ‘photograph’? Rollout Photographs © Justin Kerr: Slide idea: Steve Seitz http://research.famsi.org/kerrmaya.html
    80. Part 2: Fast Forward Preview
    81. Synthetic Lighting Paul Haeberli, Jan 1992
    82. Homework
      • Take multiple photos by changing lighting
      • Mix and match color channels to relight
      • Due Sept 19th
    83. Debevec et al. 2002: ‘Light Stage 3’
    84. Image-Based Actual Re-lighting Film the background in Milan, Measure incoming light, Light the actress in Los Angeles Matte the background Matched LA and Milan lighting. Debevec et al., SIGG2001
    85. Depth Edge Camera
    86. 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
    87.  
    88.  
    89.  
    90.  
    91. Depth Discontinuities Internal and external Shape boundaries, Occluding contour, Silhouettes
    92. Depth Edges
    93. Our Method Canny
    94. A Night Time Scene: Objects are Difficult to Understand due to Lack of Context Dark Bldgs Reflections on bldgs Unknown shapes
    95. Enhanced Context : All features from night scene are preserved, but background in clear ‘ Well-lit’ Bldgs Reflections in bldgs windows Tree, Street shapes
    96. Background is captured from day-time scene using the same fixed camera Night Image Day Image Result: Enhanced Image
    97. Participatory Urban Sensing
      • Deborah Estrin talk yesterday
      • Static/semi-dynamic/dynamic data
      • A. City Maintenance
        • Side Walks
      • B. Pollution
      • -Sensor network
      • C. Diet, Offenders
        • Graffiti
        • Bicycle on sidewalk
      • Future ..
      • Citizen Surveillance Health Monitoring
      http://research.cens.ucla.edu/areas/2007/Urban_Sensing/ (Erin Brockovich) n
    98. Crowdsourcing http://www.wired.com/wired/archive/14.06/crowds.html Object Recognition Fakes Template matching Amazon Mechanical Turk: Steve Fossett search ReCAPTCHA=OCR
    99.  
    100. Community Photo Collections U of Washington/Microsoft: Photosynth
    101. GigaPixel Images Microsoft HDView http://www.xrez.com/owens_giga.html http://www.gigapxl.org/
    102. Optics
      • It is all about rays not pixels
      • Study using lightfields
    103. Assignment 2
      • Andrew Adam’s Virtual Optical Bench
    104. Light Field Inside a Camera
    105. Lenslet-based Light Field camera [Adelson and Wang, 1992, Ng et al. 2005 ] Light Field Inside a Camera
    106. Stanford Plenoptic Camera [Ng et al 2005]
      • 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
    107. Digital Refocusing [Ng et al 2005] Can we achieve this with a Mask alone?
    108. Mask based Light Field Camera [Veeraraghavan, Raskar, Agrawal, Tumblin, Mohan, Siggraph 2007 ] Mask Sensor
    109. How to Capture 4D Light Field with 2D Sensor ? What should be the pattern of the mask ?
    110. Radio Frequency Heterodyning Receiver: Demodulation High Freq Carrier 100 MHz Reference Carrier Incoming Signal 99 MHz Baseband Audio Signal
    111. Optical Heterodyning Photographic Signal (Light Field) Carrier Incident Modulated Signal Reference Carrier Main Lens Object Mask Sensor Recovered Light Field Software Demodulation Baseband Audio Signal Receiver: Demodulation High Freq Carrier 100 MHz Reference Carrier Incoming Signal 99 MHz
    112. Captured 2D Photo Encoding due to Mask
    113. 1/f 0 Mask Tile Cosine Mask Used
    114. f θ Modulated Light Field f x f θ 0 f x0 Modulation Function Sensor Slice captures entire Light Field
    115. 2D FFT Traditional Camera Photo Heterodyne Camera Photo Magnitude of 2D FFT 2D FFT Magnitude of 2D FFT
    116. Computing 4D Light Field 2D Sensor Photo, 1800*1800 2D Fourier Transform, 1800*1800 2D FFT Rearrange 2D tiles into 4D planes 200*200*9*9 4D IFFT 4D Light Field 9*9=81 spectral copies 200*200*9*9
    117. Agile Spectrum Imaging With Ankit Mohan, Jack Tumblin [Eurographics 2008]
    118. Lens Glare Reduction [Raskar, Agrawal, Wilson, Veeraraghavan SIGGRAPH 2008]
      • Glare/Flare due to camera lenses reduces contrast
    119. Glare Reduction/Enhancement using 4D Ray Sampling Captured Glare Reduced Glare Enhanced
      • Glare = low frequency noise in 2D
        • But is high frequency noise in 4D
        • Remove via simple outlier rejection
      i j x Sensor u
    121. Long-range synthetic aperture photography Levoy et al., SIGG2005
    122. Synthetic aperture videography
    123. Focus Adjustment: Sum of Bundles
    124. Synthetic aperture photography Smaller aperture   less blur, smaller circle of confusion
    125. Synthetic aperture photography Merge MANY cameras to act as ONE BIG LENS Small items are so blurry they seem to disappear..
    126. Coded Aperture http://users.ameritech.net/paulcarlisle/codedaperture.html Encoding MURA pattern Decoding pattern Multiple Pinholes
    127. Light field photography using a handheld plenoptic camera Ren Ng, Marc Levoy, Mathieu Brédif, Gene Duval, Mark Horowitz and Pat Hanrahan
    128. 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
    129.  
    130. Example of digital refocusing
    131. Extending the depth of field conventional photograph, main lens at f / 22 conventional photograph, main lens at f / 4 light field, main lens at f / 4, after all-focus algorithm [Agarwala 2004]
    132. Imaging in Sciences: Computer Tomography
      • http://info.med.yale.edu/intmed/cardio/imaging/techniques/ct_imaging/
    133. 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)
    134. Deconvolution microscopy
      • competitive with confocal imaging, and much faster
      • assumes emission or attenuation, but not scattering
      • therefore cannot be applied to opaque objects
      • begins with less information than a light field (3D vrs 4D)
      ordinary microscope image deconvolved from focus stack
    135. 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 ?
    136. Mask in a Camera Mask Aperture Canon EF 100 mm 1:1.28 Lens, Canon SLR Rebel XT camera
    137. Digital Refocusing Captured Blurred Image
    138. Digital Refocusing Refocused Image on Person
    139. Digital Refocusing
    140. Larval Trematode Worm
    141. Mask? Sensor 4D Light Field from 2D Photo: Heterodyne Light Field Camera Full Resolution Digital Refocusing: Coded Aperture Camera Mask? Sensor Mask Sensor Mask? Sensor Mask Sensor
    142. Coding and Modulation in Camera Using Masks Coded Aperture for Full Resolution Digital Refocusing Heterodyne Light Field Camera Mask? Sensor Mask Sensor Mask Sensor
    143. Digital Refocusing Only cone in focus Captured Photo
    144. Slides by Todor Georgiev
    145. Slides by Todor Georgiev
    146. Slides by Todor Georgiev
    147. Conventional Lens: Limited Depth of Field Open Aperture Slides by Shree Nayar Smaller Aperture
    148. Wavefront Coding using Cubic Phase Plate " Wavefront Coding: jointly optimized optical and digital imaging systems “, E. Dowski, R. H. Cormack and S. D. Sarama , Aerosense Conference, April 25, 2000 Slides by Shree Nayar
    149. Depth Invariant Blur Conventional System Wavefront Coded System Slides by Shree Nayar
    150. Typical PSF changes slowly Designed PSF changes fast Decoding depth via defocus blur • Design PSF that changes quickly through focus so that defocus can be easily estimated • Implementation using phase diffractive mask (Sig 2008, Levin et al used amplitude mask) Phase mask R. Piestun, Y. Schechner, J. Shamir, “Propagation-Invariant Wave Fields with Finite Energy,” JOSA A 17, 294-303 (2000) R. Piestun, J. Shamir, “Generalized propagation invariant wave-fields,” JOSA A 15, 3039 (1998)
    151. Rotational PSF R. Piestun, Y. Schechner, J. Shamir, “Propagation-Invariant Wave Fields with Finite Energy,” JOSA A 17, 294-303 (2000) R. Piestun, J. Shamir, “Generalized propagation invariant wave-fields,” JOSA A 15, 3039 (1998)
    152. Can we deal with particle-wave duality of light with modern Lightfield theory ? Young’s Double Slit Expt first null (OPD = λ/2) Diffraction and Interferences modeled using Ray representation
    153. Light Fields
      • Radiance per ray
      • Ray parameterization:
        • Position : x
        • Direction : θ
      Goal: Representing propagation, interaction and image formation of light using purely position and angle parameters Reference plane position direction
    154. Light Fields for Wave Optics Effects Wigner Distribution Function Light Field LF < WDF Lacks phase properties Ignores diffraction, phase masks Radiance = Positive ALF ~ WDF Supports coherent/incoherent Radiance = Positive/Negative Virtual light sources Light Field Augmented Light Field WDF
    155. Limitations of Traditional Lightfields Wigner Distribution Function Traditional Light Field ray optics based simple and powerful rigorous but cumbersome wave optics based limited in diffraction & interference holograms beam shaping rotational PSF
    156. Example: New Representations Augmented Lightfields Wigner Distribution Function Traditional Light Field WDF Traditional Light Field Augmented LF Interference & Diffraction Interaction w/ optical elements ray optics based simple and powerful limited in diffraction & interference rigorous but cumbersome wave optics based Non-paraxial propagation http://raskar.scripts.mit.edu/~raskar/lightfields/
    157. (ii) Augmented Light Field with LF Transformer WDF Light Field Augmented LF Interaction at the optical elements Augmenting Light Field to Model Wave Optics Effects , [Oh, Barbastathis, Raskar] LF propagation (diffractive) optical element LF LF LF LF LF propagation light field transformer negative radiance
    158. Virtual light projector with real valued (possibly negative radiance) along a ray real projector real projector first null (OPD = λ/2) virtual light projector Augmenting Light Field to Model Wave Optics Effects , [Oh, Barbastathis, Raskar]
    159. (ii) ALF with LF Transformer
    160. “ Origami Lens”: Thin Folded Optics (2007) “ Ultrathin Cameras Using Annular Folded Optics, “ E. J. Tremblay , R. A. Stack, R. L. Morrison, J. E. Ford Applied Optics , 2007 - OSA Slides by Shree Nayar
    161. Gradient Index (GRIN) Optics Conventional Convex Lens Gradient Index ‘Lens’ Continuous change of the refractive index within the optical material Constant refractive index but carefully designed geometric shape Refractive Index along width n x Change in RI is very small, 0.1 or 0.2
    162. Photonic Crystals
      • ‘ Routers’ for photons instead of electrons
      • Photonic Crystal
        • Nanostructure material with ordered array of holes
        • A lattice of high-RI material embedded within a lower RI
        • High index contrast
        • 2D or 3D periodic structure
      • Photonic band gap
        • Highly periodic structures that blocks certain wavelengths
        • (creates a ‘gap’ or notch in wavelength)
      • Applications
        • ‘ Semiconductors for light’: mimics silicon band gap for electrons
        • Highly selective/rejecting narrow wavelength filters (Bayer Mosaic?)
        • Light efficient LEDs
        • Optical fibers with extreme bandwidth (wavelength multiplexing)
        • Hype: future terahertz CPUs via optical communication on chip
    163. Schlieren Photography
      • Image of small index of refraction gradients in a gas
      • Invisible to human eye (subtle mirage effect)
      Knife edge blocks half the light unless distorted beam focuses imperfectly Collimated Light Camera
    164. http://www.mne.psu.edu/psgdl/FSSPhotoalbum/index1.htm
    165. Varying Polarization Yoav Y. Schechner, Nir Karpel 2005 Best polarization state Worst polarization state Best polarization state Recovered image [Left] The raw images taken through a polarizer. [Right] White-balanced results: The recovered image is much clearer, especially at distant objects, than the raw image
    166. Varying Polarization
      • Schechner, Narasimhan, Nayar
      • Instant dehazing of images using polarization
    167. Photon-x: Polarization Bayer Mosaic for Surface normals
    168. Foveon: Thick Sensor
    169. Novel Sensors
      • Gradient sensing
      • HDR Camera, Log sensing
      • Line-scan Camera
      • Demodulating
      • Motion Capture
      • 3D
      • Camera =
        • 0D sensors
          • Motion detector
          • Bar code scanner
          • Time-of-flight range detector (Darpa Grand Challenge)
        • 1D sensors
          • Line scan camera (photofinish)
          • Flatbed scanner
          • Fax machine
        • 2D sensors
        • 2-1/2D sensors
        • ‘ 3D’ sensors
    171. Single Pixel Camera
    172. Compressed Imaging Scene X Aggregate Brightness Y “ A New Compressive Imaging Camera Architecture” D. Takhar et al. , Proc. SPIE Symp. on Electronic Imaging, Vol. 6065, 2006. Sparsity of Image: sparse basis coefficients measurement basis Measurements:
    173. Single Pixel Camera Image on the DMD
    174. Example 4096 Pixels 1600 Measurements (40%) 65536 Pixels 6600 Measurements (10%) Original Compressed Imaging
    175. Example 4096 Pixels 800 Measurements (20%) 4096 Pixels 1600 Measurements (40%) Original Compressed Imaging
    176. Line Scan Camera: PhotoFinish 2000 Hz
    177.  
    178. The CityBlock Project Precursor to Google Streetview Maps
    179. Figure 2 results Input Image Problem: Motion Deblurring
    180. Image Deblurred by solving a linear system. No post-processing Blurred Taxi
    181. Application: Aerial Imaging Long Exposure : The moving camera creates smear Time Shutter Open Shutter Closed Short Explosure : Avoids blur. But the image is dark Time Time Shutter Open Shutter Closed Goal: Capture sharp image with sufficient brightness using a camera on a fast moving aircraft Sharpness versus Image Pixel Brightness Time Shutter Open Shutter Closed Solution: Flutter Shutter Time = 0 Time = T
    182. Application: Electronic Toll Booths Time Goal: Automatic number plate recognition from sharp image Monitoring Camera for detecting license plates Time Shutter Open Shutter Closed Solution: Sufficiently long exposure duration with fluttered shutter Ideal exposure duration depends on car speed which is difficult to determine a-priory. Longer exposure duration blurs the license plate image making character recognition difficult
    183. Fluttered Shutter Camera Raskar, Agrawal, Tumblin Siggraph2006 Ferroelectric shutter in front of the lens is turned opaque or transparent in a rapid binary sequence
    184. Short Exposure Traditional MURA Coded Coded Exposure Photography: Assisting Motion Deblurring using Fluttered Shutter Raskar, Agrawal, Tumblin (Siggraph2006) Deblurred Results Captured Photos Shutter Result has Banding Artifacts and some spatial frequencies are lost Decoded image is as good as image of a static scene Image is dark and noisy
    185.  
    186. Compound Lens of Dragonfly
    187. TOMBO: Thin Camera (2001) “ Thin observation module by bound optics (TOMBO),” J. Tanida, T. Kumagai, K. Yamada, S. Miyatake Applied Optics, 2001
    188. TOMBO: Thin Camera
    189. ZCam (3Dvsystems), Shuttered Light Pulse Resolution : 1cm for 2-7 meters
    190. Graphics can inserted behind and between characters
    191. Cameras for HCI
      • Camera looking at people
        • Face detection/recognition
        • People tracking, segmentation
      • Camera looking at fingers
        • FTIR
        • Optical mouse
        • Anoto Pen
      • Multi-flash
      • Complexity of Optical Transmission
      • Motion capture
        • 2D, 1D, 0D
        • Motion detector
        • Wii
    192. Boring Camera HCI Projects
      • Locate/Track/Segment
        • But fails under lighting, skin-color , clothing, background change
      • Track LED from one/two webcams
        • Triangulate
      • Segment finger or face
        • Background segmentation
        • Put on a glove/hat for better segmentation
      • Artistic interactive display
        • Human figure segmentation
        • Proximity/blob size
        • Motion (hand waving etc)
      • You can impress
        • some of the people all of the time, and all of the people some of the time, but not all of the people all of the time
      • Solution:
        • Use smarter sensors, processing to create magic
    193. Cameras for HCI
      • Frustrated total internal reflection
      Han, J. Y. 2005. Low-Cost Multi-Touch Sensing through Frustrated Total Internal Reflection. In Proceedings of the 18th Annual ACM Symposium on User Interface Software and Technology
    194. Converting LCD Screen = large Camera for 3D Interactive HCI and Video Conferencing Matthew Hirsch, Henry Holtzman Doug Lanman, Ramesh Raskar Siggraph Asia 2009 Class Project in CompCam 2008 SRC Winner BiDi Screen *
    195. Beyond Multi-touch: Mobile Laptops Mobile
    196. Light Sensing Pixels in LCD Display with embedded optical sensors Sharp Microelectronics Optical Multi-touch Prototype
    197. Design Overview Display with embedded optical sensors LCD , displaying mask Optical sensor array ~2.5 cm ~50 cm
    198. Beyond Multi-touch: Hover Interaction
      • Seamless transition of multitouch to gesture
      • Thin package, LCD
    199. Design Vision Object Collocated Capture and Display Bare Sensor Spatial Light Modulator
    200. Touch + Hover using Depth Sensing LCD Sensor
    201. Overview: Sensing Depth from Array of Virtual Cameras in LCD
      • 36 bit code at 0.3mm resolution
      • 100 fps camera at 8800 nm
      http://www.acreo.se/upload/Publications/Proceedings/OE00/00-KAURANEN.pdf
      • Smart Barcode size : 3mm x 3mm
      • Ordinary Camera: Distance 3 meter
      Computational Probes: Long Distance Bar-codes Mohan, Woo,Smithwick, Hiura, Raskar Accepted as Siggraph 2009 paper
    204. Bokode
    205. Barcodes markers that assist machines in understanding the real world
    206. Bokode: ankit mohan, grace woo, shinsaku hiura, quinn smithwick, ramesh raskar camera culture group, MIT media lab imperceptible visual tags for camera based interaction from a distance
    207. Defocus blur of Bokode
    208. Image greatly magnified. Simplified Ray Diagram
    209. Our Prototypes
    210. street-view tagging
    211. Vicon Motion Capture High-speed IR Camera Medical Rehabilitation Athlete Analysis Performance Capture Biomechanical Analysis
    212. 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
    213. Imperceptible Tags under clothing, tracked under ambient light Hidden Marker Tags Outdoors Unique Id http://raskar.info/prakash
    214. Camera-based HCI
      • Many projects here
        • Robotics, Speechome, Spinner, Sixth Sense
      • Sony EyeToy
      • Wii
      • Xbox/Natal
      • Microsoft Surface
        • Shahram Izadi (Microsoft Surface/SecondLight)
        • Talk at Media Lab, Tuesday Sept 22 nd , 3pm
    215. Computational Imaging in the Sciences
      • Driving Factors:
      • new instruments lead to new discoveries
      • (e.g., Leeuwenhoek + microscopy  microbiology)
      • Q: most important instrument in last century?
      • A: the digital computer
      • What is Computational Imagining?
      • according to B.K. Horn:
      • “ … imaging methods in which computation is inherent in image formation. ”
      • digital processing has led to a revolution in medical and scientific data collection
      • (e.g., CT, MRI, PET, remote sensing, etc.)
      Slides by Doug Lanman
    216. Computational Imaging in the Sciences
      • Medical Imaging:
      • transmission tomography (CT)
      • reflection tomography (ultrasound)
      • Geophysics:
      • borehole tomography
      • seismic reflection surveying
      • Applied Physics:
      • diffuse optical tomography
      • diffraction tomography
      • scattering and inverse scattering
      • Biology:
      • confocal microscopy
      • deconvolution microscopy
      • Astronomy:
      • coded-aperture imaging
      • interferometric imaging
      • Remote Sensing:
      • multi-perspective panoramas
      • synthetic aperture radar
      • Optics:
      • wavefront coding
      • light field photography
      • holography
      Slides by Doug Lanman
    217. What is Tomography?
      • Definition:
      • imaging by sectioning (from Greek tomos: “a section” or “cutting”)
      • creates a cross-sectional image of an object by transmission or reflection data collected by illuminating from many directions
      Parallel-beam Tomography Fan-beam Tomography Slides by Doug Lanman
    218. Reconstruction: Filtered Backprojection
      • Fourier Projection-Slice Theorem:
      • F -1 {G  ( ω )} = P  (t)
      • add slices G  ( ω ) into {u,v} at all angles  and inverse transform to yield g(x,y)
      • add 2D backprojections P  (t) into {x,y} at all angles 
      x y f y f x P  (t) P  (t,s) G  (  ) g(x,y) Slides by Doug Lanman
    219. Medical Applications of Tomography bone reconstruction segmented vessels Slides by Doug Lanman
    220. Biology: Confocal Microscopy pinhole light source photocell pinhole Slides by Doug Lanman
    221. Confocal Microscopy Examples Slides by Doug Lanman
    222. Forerunners .. Mask Sensor Mask Sensor
    223. Fernald, Science [Sept 2006] Shadow Refractive Reflective Tools for Visual Computing
    224. Project Assignments
      • Relighting
      • Dual Photography
      • Virtual Optical Bench
      • Lightfield capture
        • Mask or LCD with programmable aperture
      • One of
        • High speed imaging
        • Thermal imaging
        • 3D range sensing
      • Final Project
    225. Synthetic Lighting Paul Haeberli, Jan 1992
    226. Image-Based Actual Re-lighting Film the background in Milan, Measure incoming light, Light the actress in Los Angeles Matte the background Matched LA and Milan lighting. Debevec et al., SIGG2001
    227. Dual photography from diffuse reflections: Homework Assignment 2 the camera’s view Sen et al, Siggraph 2005
    228. Shower Curtain: Diffuser Direct Global
      • Andrew Adam’s Virtual Optical Bench
    230. Beyond Visible Spectrum Cedip RedShift
    231. Goals
      • Change the rules of the game
        • Emerging optics, illumination, novel sensors
        • Exploit priors and online collections
      • Applications
        • Better scene understanding/analysis
        • Capture visual essence
        • Superior Metadata tagging for effective sharing
        • Fuse non-visual data
          • Sensors for disabled, new art forms, crowdsourcing, bridging cultures
    232. First Assignment: Synthetic Lighting Paul Haeberli, Jan 1992
      • Format
        • 4 (3) Assignments
          • Hands on with optics, illumination, sensors, masks
          • Rolling schedule for overlap
          • We have cameras, lenses, electronics, projectors etc
          • Vote on best project
        • Mid term exam
          • Test concepts
        • 1 Final project
          • Should be a Novel and Cool
          • Conference quality paper
          • Award for best project
        • Take 1 class notes
        • Lectures (and guest talks)
        • In-class + online discussion
      • If you are a listener
        • Participate in online discussion, dig new recent work
        • Present one short 15 minute idea or new work
      • Credit
          • Assignments: 40%
          • Project: 30%
          • Mid-term: 20%
          • Class participation: 10%
      • Pre-reqs
          • Helpful: Linear algebra, image processing, think in 3D
          • We will try to keep math to essentials, but complex concepts
      • Assignments:
      • You are encouraged to program in Matlab for image analysis
      • You may need to use C++/OpenGL/Visual programming for some hardware assignments
      • Each student is expected to prepare notes for one lecture
      • These notes should be prepared and emailed to the instructor no later than the following Monday night (midnight EST). Revisions and corrections will be exchanged by email and after changes the notes will be posted to the website before class the following week.
      • 5 points
      • Course mailing list : Please make sure that your emailid is on the course mailing list
      • Send email to raskar (at) media.mit.edu
      • Please fill in the email/credit/dept sheet
      •  
      • Office hours :
      • Email is the best way to get in touch
      • Ramesh:. raskar (at) media.mit.edu
      • Ankit: ankit (at) media.mit.edu
      • After class:
      • Muddy Charles Pub
      • (Walker Memorial next to tennis courts)
    235. 2 Sept 18th Modern Optics and Lenses, Ray-matrix operations 3 Sept 25th Virtual Optical Bench, Lightfield Photography, Fourier Optics, Wavefront Coding 4 Oct 2nd Digital Illumination , Hadamard Coded and Multispectral Illumination 5 Oct 9th Emerging Sensors : High speed imaging, 3D  range sensors, Femto-second concepts, Front/back illumination, Diffraction issues 6 Oct 16th Beyond Visible Spectrum: Multispectral imaging and Thermal sensors, Fluorescent imaging, 'Audio camera' 7 Oct 23rd Image Reconstruction Techniques, Deconvolution, Motion and Defocus Deblurring, Tomography, Heterodyned Photography, Compressive Sensing 8 Oct 30th Cameras for Human Computer Interaction (HCI): 0-D and 1-D sensors, Spatio-temporal coding, Frustrated TIR, Camera-display fusion 9 Nov 6th Useful techniques in Scientific and Medical Imaging: CT-scans, Strobing, Endoscopes, Astronomy and Long range imaging 10 Nov 13th Mid-term  Exam, Mobile Photography, Video Blogging, Life logs and Online Photo collections 11 Nov 20th Optics and Sensing in Animal Eyes. What can we learn from successful biological vision systems? 12 Nov 27th Thanksgiving Holiday (No Class) 13 Dec 4th Final Projects
    236. What is the emphasis?
      • Learn fundamental techniques in imaging
        • In class and in homeworks
        • Signal processing, Applied optics, Computer graphics and vision, Electronics, Art, and Online photo collections
        • This is not a discussion class
      • Three Applications areas
        • Photography
          • Think in higher dimensions 4D, 6D, 8D, thermal, range cam, lightfields, applied optics
        • Active Computer Vision (real-time)
          • HCI, Robotics, Tracking/Segmentation etc
        • Scientific Imaging
          • Compressive sensing, wavefront coding, tomography, deconvolution, psf
        • But the 3 areas are merging and use similar principles
    237. First Homework Assignment
      • Take multiple photos by changing lighting
      • Mix and match color channels to relight
      • Due Sept 25 th
      • Need Volunteer: taking notes for next class
        • Sept 18: Sam Perli
        • Sept 25: ?
    238. Goal and Experience Low Level Mid Level High Level Hyper realism Raw Angle, spectrum aware Non-visual Data, GPS Metadata Priors Comprehensive 8D reflectance field Digital Epsilon Coded Essence CP 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
      • Capture
      • Overcome Limitations of Cameras
      • Capture Richer Data
      • Multispectral
      • New Classes of Visual Signals
      • Lightfields, Depth, Direct/Global, Fg/Bg separation
      • Hyperrealistic Synthesis
      • Post-capture Control
      • Impossible Photos
      • Exploit Scientific Imaging
      Computational Photography http://raskar.info/photo/
    240.  
    241. Blind Camera Sascha Pohflepp, U of the Art, Berlin, 2006
    242. END
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