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EE 596 Machine Vision Autumn 2013. Professor Linda Shapiro shapiro@cs.washington.edu TA: Alfred Gui alfredg.uw.edu. Introduction. What IS computer vision? Where do images come from?. The analysis of digital images by a computer. You tell me!. Applications: Medical Imaging.
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EE 596Machine VisionAutumn 2013 Professor Linda Shapiro shapiro@cs.washington.edu TA: Alfred Gui alfredg.uw.edu
Introduction • What IS computer vision? • Where do images come from? The analysis of digital images by a computer You tell me!
Applications: Medical Imaging CT image of a patient’s abdomen liver kidney kidney
3D Reconstruction of the Blood Vessel Tree
Mouse Eye Image Retrieval for Genetic Studies
Robotics • 2D Gray-tone or Color Images • 3D Range Images “Mars” rover What am I?
Image Databases: Images from my Ground-Truth collection: http://www.cs.washington.edu/research/imagedatabase/groundtruth • Retrieve images containing trees
Science Calineuria (Cal) Doroneuria (Dor) Yor (Yor) Previous Classification Results: UW and Oregon State University
Soil Mesofauna XenillusA TraychetesA ZyqoribafulaA AchipteriaA BdellozoniumI CatoposurusA EniochthoniusA BelbaA BelbaI PtenothrixV PtiliidA HypochthoniusLA EpilohmanniaT EpilohmanniaD EpilohmanniaA EntomobrgaTM EpidamaeusA QuadroppiaA HypogastruraA MetrioppiaA LiacarusRA IsotomaVI NothrusF IsotomaA onychiurusA PlatynothrusF TomocerusA OppiellaA SiroVI PhthiracarusA PlatynothrusI PeltenuialaA
Surveillance: Event Recognition in Aerial Videos Original Video Frame Color Regions Structure Regions
Face Recognition Glasgow University
2D Object Recognition from “Parts” Oxford University
Graphics: Special Effects Andy Serkis, Gollum, Lord of the Rings
3D Craniofacial Shape Analysis from Meshes of Children’s Heads
Digital Image Terminology: 0 0 0 0 1 0 0 0 0 1 1 1 0 0 0 1 95 96 94 93 92 0 0 92 93 93 92 92 0 0 93 93 94 92 93 0 1 92 93 93 93 93 0 0 94 95 95 96 95 pixel (with value 94) its 3x3 neighborhood region of medium intensity resolution (7x7) • binary image • gray-scale (or gray-tone) image • color image • multi-spectral image • range image • labeled image
The Three Stages of Computer Vision • low-level • mid-level • high-level image image image features features analysis
Low-Level sharpening blurring
Low-Level Canny original image edge image Mid-Level ORT data structure circular arcs and line segments edge image
Mid-level K-means clustering (followed by connected component analysis) regions of homogeneous color original color image data structure
Low- to High-Level low-level edge image mid-level consistent line clusters high-level BuildingRecognition