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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)
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Using Image Data in Your Research Kenton McHenry, Ph.D. Research Scientist
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 Images [Hoiem, 2012]
Image Creation Light emitted Fraction of light reflects into camera Lens Sensor [Hoiem, 2012]
Image Creation • Light(s) • Position • Strength • Geometry • Color • Surface(s) • Orientation • Color • Material • Nearby surfaces • Sensor • Lens • Aperture • Exposure • Resolution Light emitted Light reflected to camera Sensor [Hoiem, 2012]
Surfaces: Reflected Light incoming light absorption specular reflection incoming light incoming light diffuse reflection [Hoiem, 2012]
Surfaces: Orientation 1 2 Ix = rxLNx [Hoiem, 2012]
Surfaces transparency light source light source refraction [Hoiem, 2012]
Surfaces light source fluorescence λ1 λ2
Surfaces light source phosphorescence t=1 t>1 [Hoiem, 2012]
Surfaces light source subsurface scattering λ [Hoiem, 2012]
Light Human Luminance Sensitivity Function [Hoiem, 2012]
Light [Hoiem, 2012]
Light • [GIMP Demo]
Sensors • Long (red), Medium (green), and Short (blue) cones, plus intensity rods [Hoiem, 2012]
Sensors [Hoiem, 2012]
Sensors R 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]
Surface Interactions [Hoiem, 2012]
Raster Images image(234, 452) = 0.58 [Hoiem, 2012]
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
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 labs http://www.research.att.com/~yann/ License plate readers http://en.wikipedia.org/wiki/Automatic_number_plate_recognition • Technology to convert scanned documents to ASCII text • If you have a scanner, it probably came with OCR software [Hoiem, 2012]
Biometrics Face recognition systems now beginning to appear more widelyhttp://www.sensiblevision.com/ Fingerprint scanners on many new laptops, other devices [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