using image data in your research
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Using Image Data in Your Research. Kenton McHenry, Ph.D. Research Scientist. Image and Spatial Data Analysis Group. Image and Spatial Data Analysis Group. Research & Development Cyberinfrastructure : Software development for the sciences (and industry) - PowerPoint PPT PresentationTRANSCRIPT
National Center for Supercomputing ApplicationsUniversity of Illinois at Urbana-Champaign
Using Image Data in Your Research
Kenton McHenry, Ph.D.Research Scientist
Image and Spatial Data Analysis Group
Image and Spatial Data Analysis Group
• Research & Development• Cyberinfrastructure: Software development for the sciences
(and industry)• Computer Vision: Information from images• High Performance Computing: Software that scales with
regards to computation and data
Image and Spatial Data Analysis Group• Content Based Retrieval
• Search in digitized collections• Document segmentation• Authorship• 3D models
• Automatic Image Annotation • Assign keywords as metadata
• Tracking• 3D Reconstruction• Image Stitching
Image and Spatial Data Analysis Group
• Digital Preservation• Access to data content independent of format• Access to software functionality independent of distribution• Information loss evaluation• Document similarity
• Environmental Modeling• Workflows• Heterogeneous data sources
• Data Exploration• Data mining• eScience
Goals for Today
• A high level understanding of what Computer Vision is and how YOU might use it.• A sense of what is currently possible• A sense of how these things break• A sense of what might be possible• A sense of what is pure science fiction!• The looming opportunity in “Big Data”
• A little bit of hands on experience
Computer Vision
• Books: • D. Forsyth, J. Ponce, “Computer Vision: A Modern Approach”,
Pearson, 2011.• R. Szeliski, “Computer Vision: Algorithms and Applications”,
http://szeliski.org/Book, 2010.
• CS 543: Computer Vision (UIUC)• Derek Hoiem, Ph.D.• http://www.cs.illinois.edu/class/sp12/cs543
Computer Vision
[Hoiem, 2012]
Computer Vision
• Make a computer understand images and video
• What kind of scene?• Are there cars?• Where are the cars?• Is it day or night?• What is the ground made of?• How far is the building?
[Hoiem, 2012]
Raster Images0.92 0.93 0.94 0.97 0.62 0.37 0.85 0.97 0.93 0.92 0.990.95 0.89 0.82 0.89 0.56 0.31 0.75 0.92 0.81 0.95 0.910.89 0.72 0.51 0.55 0.51 0.42 0.57 0.41 0.49 0.91 0.920.96 0.95 0.88 0.94 0.56 0.46 0.91 0.87 0.90 0.97 0.950.71 0.81 0.81 0.87 0.57 0.37 0.80 0.88 0.89 0.79 0.850.49 0.62 0.60 0.58 0.50 0.60 0.58 0.50 0.61 0.45 0.330.86 0.84 0.74 0.58 0.51 0.39 0.73 0.92 0.91 0.49 0.740.96 0.67 0.54 0.85 0.48 0.37 0.88 0.90 0.94 0.82 0.930.69 0.49 0.56 0.66 0.43 0.42 0.77 0.73 0.71 0.90 0.990.79 0.73 0.90 0.67 0.33 0.61 0.69 0.79 0.73 0.93 0.970.91 0.94 0.89 0.49 0.41 0.78 0.78 0.77 0.89 0.99 0.93
[Hoiem, 2012]
Image Creation
Light emitted
Sensor
Lens
Fraction of light reflects into camera
[Hoiem, 2012]
Image Creation • Light(s)• Position• Strength• Geometry• Color
• Surface(s)• Orientation• Color• Material• Nearby surfaces
• Sensor• Lens• Aperture• Exposure• Resolution
Light emitted
Sensor
Light reflected to camera
[Hoiem, 2012]
Surfaces: Reflected Light
incoming lightspecular reflection
ΘΘ
incoming lightdiffuse reflection
absorptionincoming light
[Hoiem, 2012]
Surface: Reflected Light
Surfaces: Orientation
1
2Ix = rxLNx
[Hoiem, 2012]
Surfaces
light sourcetransparency
light source
refraction
[Hoiem, 2012]
Surfaces
λ1
light source
λ2
fluorescence
Surfaces
t=1
light source
t>1
phosphorescence
[Hoiem, 2012]
Surfaces
λ
light source
subsurface scattering
[Hoiem, 2012]
Light
Human Luminance Sensitivity Function
[Hoiem, 2012]
Light.
# P
hoto
ns
D. Normal Daylight
Wavelength (nm.)
B. Gallium Phosphide Crystal
400 500 600 700
# P
hoto
ns
Wavelength (nm.)
A. Ruby Laser
400 500 600 700
400 500 600 700
# P
hoto
ns
C. Tungsten Lightbulb
400 500 600 700
# P
hoto
ns
[Hoiem, 2012]
Light
Light
• [GIMP Demo]
Sensors
• Long (red), Medium (green), and Short (blue) cones, plus intensity rods
[Hoiem, 2012]
Sensors
[Hoiem, 2012]
SensorsR
G
B
[Hoiem, 2012]
Sensors: Perspective
• Projecting a 3D world onto a 2D plane• Parallel lines disappear at vanishing points• Sizes appear smaller further away
Surface Interactions!
[Hoiem, 2012]
Surface Interactions
[Hoiem, 2012]
Surface Interactions
[Hoiem, 2012]
Surfaces: Interactions
Surface Interactions
[Hoiem, 2012]
Raster Images0.92 0.93 0.94 0.97 0.62 0.37 0.85 0.97 0.93 0.92 0.990.95 0.89 0.82 0.89 0.56 0.31 0.75 0.92 0.81 0.95 0.910.89 0.72 0.51 0.55 0.51 0.42 0.57 0.41 0.49 0.91 0.920.96 0.95 0.88 0.94 0.56 0.46 0.91 0.87 0.90 0.97 0.950.71 0.81 0.81 0.87 0.57 0.37 0.80 0.88 0.89 0.79 0.850.49 0.62 0.60 0.58 0.50 0.60 0.58 0.50 0.61 0.45 0.330.86 0.84 0.74 0.58 0.51 0.39 0.73 0.92 0.91 0.49 0.740.96 0.67 0.54 0.85 0.48 0.37 0.88 0.90 0.94 0.82 0.930.69 0.49 0.56 0.66 0.43 0.42 0.77 0.73 0.71 0.90 0.990.79 0.73 0.90 0.67 0.33 0.61 0.69 0.79 0.73 0.93 0.970.91 0.94 0.89 0.49 0.41 0.78 0.78 0.77 0.89 0.99 0.93
[Hoiem, 2012]
image(234, 452) = 0.58
Individual Pixels
[Hoiem, 2012]
Neighborhoods of Pixels
• For nearby surface points most factors do not change much
• Local differences in brightness
[Hoiem, 2012]
Neighborhoods of Pixels
[Hoiem, 2012]
Neighborhoods of Pixels
[Hoiem, 2012]
Neighborhoods of Pixels
[Hoiem, 2012]
Changes in Intensity
• Changes in albedo• Changes in surface normal• Changes in distance
[Hoiem, 2012]
Computer Vision
• Make a computer understand images and video• Lots of variables are involved in the creation of an image/frame• Variables are not independent and interact• The problem is underconstraned
• i.e. multiple scenes can result in the same image
Optical Illusions
Optical Illusions
Optical Illusions
Vision is Really Hard!
• Vision is an amazing feat of natural intelligence• More human brain devoted to vision than anything else
[Hoiem, 2012]
State of the Art
• From 1960’s to present…
Barcodes
• Optical machine readable representation of data• 1950’s
http://en.wikipedia.org/wiki/Barcode
Optical Character Recognition (OCR)
Digit recognition, AT&T labshttp://www.research.att.com/~yann/
• Technology to convert scanned documents to ASCII text• If you have a scanner, it probably came with OCR software
License plate readershttp://en.wikipedia.org/wiki/Automatic_number_plate_recognition
[Hoiem, 2012]
Biometrics
Fingerprint scanners on many new laptops, other devices
Face recognition systems now beginning to appear more widelyhttp://www.sensiblevision.com/
[Hoiem, 2012]
Face detection
• Many new digital cameras now detect faces• Canon, Sony, Fuji, …
[Hoiem, 2012]
Medical imaging
3D imaging, MRI, CT
[Hoiem, 2012], http://en.wikipedia.org/wiki/3D_ultrasound
The Matrix movies, ESC Entertainment, XYZRGB, NRC
3D Reconstruction
Pirates of the Carribean, Industrial Light and Magic
Motion capture
[Hoiem, 2012]
Image Stitching
NASA'S Mars Exploration Rover Spirit captured this westward view from atop a low plateau where Spirit spent the closing months of 2007.
[Hoiem, 2012]
Industry
Vision-guided robots position nut runners on wheels
[Hoiem, 2012]
Sports
http://www.sportvision.com/video.html
[Hoiem, 2012]
Object Recognition
Point & Find, Nokia, Google Goggles
[Hoiem, 2012]
LaneHawk by EvolutionRobotics
Human Computer Interaction
• Object Recognition: http://www.youtube.com/watch?feature=iv&v=fQ59dXOo63o• 3D Reconstruction: http://www.youtube.com/watch?v=7QrnwoO1-8A• Robot: http://www.youtube.com/watch?v=w8BmgtMKFbY
[Hoiem, 2012]
Driving
• Oct 9, 2010. "Google Cars Drive Themselves, in Traffic". The New York Times. John Markoff• June 24, 2011. "Nevada state law paves the way for driverless cars". Financial Post. Christine Dobby• Aug 9, 2011, "Human error blamed after Google's driverless car sparks five-vehicle crash". The
Star (Toronto)
[Hoiem, 2012]
State of the Art
• Remember vision is hard!• Most vision applications are “quirky”.
Image and Spatial Data Analysis Grouphttp://isda.ncsa.illinois.edu
Questions?