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Camera Culture

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Better than any one photo (resolution/frame rate, fov, dynamic range etc) Achieve effects via multi-photo fusion. Create a Super-camera. Mimic human eye ... – PowerPoint PPT presentation

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Title: Camera Culture


1
Camera Culture
Ramesh Raskar Associate Prof, Media Lab, MIT
Course WebPage http//raskar.info/course.html
2
Todays Plan
  • Summary, Camera for image search
  • Visual Social Computing Citizen Journalism
  • Next class big question
  • Opportunities in Pervasive Public Recording
  • Big concept
  • (Last week)
  • Understanding Camera Constraints
  • (This week)
  • What matters in photography pixels (Low-level
    cues) or low-dimensional features (Mid-level
    cues)?
  • Decomposing pixels into meaningful values

3
Camera for image search
  • How can we augment the camera to support best
    'image search'?
  • 'Search'segment/identify/recognize/transform/comp
    are/archive
  • Or more precisely, object matching across images.
  • (For example, if we find to find a specific face
    image, we need a procedure to segment and
    identify (detect) the pixels likely to belong to
    a face, then recognize the candidate face by
    transforming into a representation where we can
    match with that specific face image. Currently,
    this is all performed in software using
    traditional cameras. Typically, the algorithms
    try to reduce the image to lower-dimensional
    'features' and do the matching in this
    feature-space. Unlike text search, where the
    search pipeline is simple thanks to easy matching
    process, object-matching-in-images is quite
    difficult. What can we additional data can we
    capture while recording pixels and what new
    algorithms can exploit this augmented photo?)
  • How can we make the scene ingredients machine
    readable so that we can easily perform the
    'search'? Is this the key problem? 3D
    reconstruction (so that it is view independent,
    )? Hardware and software solutions? Crowdsourcing
    (let people do
  • marking/sorting/indexing for others)? Metadata
    tagging (tag highlevel text labels rather than
    pixel-level tagging)?
  • Do we need to capture Material index (where is
    all the wood in this image)? Segmentation
    boundaries (shape versus reflectance edges)?
    Repeatable view and illumination invariance (be
    able to recreate image from a given view so it
    can be compared with another image, or create
    images that look same independent of
    time-of-day)?
  • Some ideas (i) to locate all 'images' with
    faces, record the iris biometric which validates
    if a photo includes a human eye, and then we can
    search all images across an album with that
    face/eye/iris, (ii) embed RFID tag (electronic
    bar-code) in every object and record the binary
    index with an RFID reader.

4
Next Class
  • Homework
  • What are the opportunities in pervasive recording
    of public spaces?
  • Pervasive public recordingsurveillance/GoogleEart
    hLive/Subscription cameras
  • Technology
  • See thru fog, time-lapse processing,
    day-nite/season/multi-modal fusion, how to
    consume these images, how to merge with
    static/dynamic content, merge with static/dynamic
    cameras, support object recognition, refine GPS
    coords, crowdsourcing, metadata (video frame)
    tagging
  • Society
  • Commerce (real-estate, reviews, remote
    maintenance), Environment (earthquake-prediction
    like opportunities, Politics (protests)
  • Volunteer
  • Class notes Lav (today), next ..
  • Select/read/present/paper
  • Visual Social Computing Tom
  • Mobile Photography Eugene
  • Beyond Visible Spectrum Brandon
  • Emerging sensors Matt
  • Developing Countries Lav/ Tilke
  • Sols for Visually Challenged James

5
Today 3pm
  • Less is More Coded Computational Photography
  • Speaker Ramesh Raskar, MIT Media Lab Date
    Wednesday, February 20 2008 Time 300PM to
    400PM Refreshments 245PM Location Star
    Seminar Room (32-D463)

6
Topics
  • Imaging Devices, Modern Optics and Lenses
  • Emerging Sensor Technologies
  • Mobile Photography
  • Visual Social Computing and Citizen Journalism
  • Imaging Beyond Visible Spectrum
  • Computational Imaging in Sciences
  • Trust in Visual Media
  • Solutions for Visually Challenged
  • Cameras in Developing Countries
  • Future Products and Business Models

7
Feedback
  • What are your questions about camera/technology/so
    ciety?
  • Your expectations from the course?

8
Topics
  • Other courses
  • Art and Photography
  • CSAIL Computational Photography
  • MechE Optics
  • Fall2008
  • Intro to Computational Camera and Photography
  • I will teach course in Fall
  • Current course
  • More emphasis on future cameras
  • Faster review of technology and then look at
    impact/applications/opportunities
  • Big ideas/technologies/applications,
  • Understand rules-of-thumb and trade-offs
  • Ideal for thesis/projects/research
    papers/business models
  • Learn fun stuff before the nitty gritty

9
Photography Full of Tradeoffs...
  • Available light vs. exposure time vs. scene
    movement vs. field of view vs. focus depth vs.
    sensitivity vs. noise vs. color rendition vs.
    color gamut vs. contrast vs. visible detail vs. ….

Flash
10
Available Light vs Parameter/Specs box
Exposure
Dynamic Range
Focus distance
Resolution/ Frame rate
Focal Length (zoom)
Field of view
Depth of field
Aperture
Limited Parameters
Limited Abilities
11
Dynamic Range
Short Exposure
Goal High Dynamic Range
Long Exposure
12
Phase 1 of Better Photography
  • Epsilon Photography
  • Low-level vision
  • Best pixel and pixel-features
  • Vary focus, exposure, polarization, illumination
  • Vary time, view
  • Better than any one photo (resolution/frame rate,
    fov, dynamic range etc)
  • Achieve effects via multi-photo fusion
  • Create a Super-camera
  • Mimic human eye

13
Phase 1.1 of Better Photography
  • Create a Super-camera
  • Mimic human eye
  • What aspect of human eye are critical/ useless?
  • Eye Feedback wrt brain, After-image/illusions,
  • Camera geometry/stereo pair, multispectral,unifor
    m res, memory,
  • What are other parameters/Design/Features to
    improve?
  • Very small camera/thin camera ..
  • Tight loop with illumination
  • ..

14
The Eyes Lens
15
Varioptic Liquid Lens Electrowetting
Varioptic, Inc., 2007
16
Varioptic Liquid Lens
(Courtesy Varioptic Inc.)
17
Captured Video
(Courtesy Varioptic Inc.)
18
Conventional Compound Lens
19
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
20
Origami Lens
Conventional Lens
Origami Lens
21
Optical Performance
Conventional Origami
Scene
22
Compound Lens of Dragonfly
23
TOMBO Thin Camera (2001)
Thin observation module by bound optics
(TOMBO), J. Tanida, T. Kumagai, K.
Yamada, S. Miyatake Applied Optics, 2001
24
TOMBO Thin Camera
25
Captured Image
TOMBO
Scene
Captured Image (Multiple low-resolution copies
of the scene)
26
Reconstructed Image
27
Phase 1 of Better Photography
  • Epsilon Photography
  • Low-level vision
  • Best pixel and pixel-features
  • Vary focus, exposure, polarization, illumination
  • Vary time, view
  • Better than any one photo (resolution/frame rate,
    fov, dynamic range etc)
  • Achieve effects via multi-photo fusion
  • Create a Super-camera
  • Mimic human eye

28
Phase 1.1 of Better Photography
  • Create a Super-camera
  • Mimic human eye
  • What aspect of human eye are critical/ useless?
  • ..
  • What are other parameters/Design/Features to
    improve?
  • Very small camera/thin camera ..
  • Tight loop with illumination
  • ..

29
Phase 2 of Better Photography
  • Coded Photography
  • Mid-level cues
  • Regions, shapes(depth), edges, motion,
    material-index (…)
  • Cartoons via Multi-flash camera (depth edges),
    Wavelength profile,
  • Visual interface issue (human eye expects pixels)
  • Decompose pixel values (…)
  • Single or few photos
  • Create a functionally super-camera
  • Dont mimic human eye

30
Multiperspective Camera?
  • Jingyi Yu 2004

31
Phase 3 of Better Photography
  • Essence Photography
  • High-level cues
  • Inference, perception, cognition
  • Intent based (like biovision systems)
  • Not a single-solution fits-all
  • ? Single or few photos
  • Beats photography
  • Dont just mimic human eye, or record
    pixels/mid-level cues
  • Create a meaningful representation of visual
    experience
  • New art form, new commerce models

32
Visual Social Computing and Citizen Journalism
  • What is VSC
  • Social Computing is well known, I made up VSC
  • My defn of SC Online computation of the people,
    by the people, for the people (old world govt,
    economy, epidemiology)
  • Subsets
  • Crowdsourcing (CAPTCHA) (by the people, but maybe
    for just one person)
  • Participatory sensing (of the people, but no
    active part by individuals, not for the people)
  • Recommendation systems (by the people and for the
    people)
  • Tagging (Digg) (all three)
  • Blogs, social networks, auctions, wikipedia, tags
  • 90 of all data will be about people
  • Example problem Can we reduce distrust among
    Kenyas groups?
  • Easy to predict certain trends ..
  • Just add dimensions
  • Text, audio/music, images, video, (whats next)
  • LP-gtCassette-VHS player -gt CD player -gt DVD
    Player (ok Blue-ray DVD player) -gt (whats next)
  • Radio-TV- ..
  • Gopher -gt Newsgroups -gtWikipedia _gt (whats next)
  • Take anything text/audio based -gt image/video
  • Take anything image based -gt video (Flickr -gt
    YouTube)

33
Today 3pm
  • Less is More Coded Computational Photography
  • Speaker Ramesh Raskar, MIT Media Lab Date
    Wednesday, February 20 2008 Time 300PM to
    400PM Refreshments 245PM Location Star
    Seminar Room (32-D463)

34
Next Class
  • Homework
  • What are the opportunities in pervasive recording
    of public spaces?
  • Pervasive public recordingsurveillance/GoogleEart
    hLive/Subscription cameras
  • Technology
  • See thru fog, time-lapse processing,
    day-nite/season/multi-modal fusion, how to
    consume these images, how to merge with
    static/dynamic content, merge with static/dynamic
    cameras, support object recognition, refine GPS
    coords, crowdsourcing, metadata (video frame)
    tagging
  • Society
  • Commerce (real-estate, reviews, remote
    maintenance), Environment (earthquake-prediction
    like opportunities, Politics (protests)
  • Volunteer
  • Class notes Lav (today), next ..
  • Select/read/present/paper
  • Visual Social Computing Tom
  • Beyond Visible Spectrum Brandon
  • Mobile Photography
  • Emerging sensors
  • Developing Countries Lav
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