Computer Vision: 3D Shape Reconstruction

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Computer Vision: 3D Shape Reconstruction. Use images to build 3D model of object or site. 3D site model built from laser range scans collected by CMU autonomous helicopter. Computer Vision: Guiding Motion. Visually guided manipulation Hand-eye coordination Visually guided locomotion - PowerPoint PPT Presentation

Text of Computer Vision: 3D Shape Reconstruction

Image Processing and Computer Vision

Computer Vision: 3D Shape ReconstructionUse images to build 3D model of object or site

3D site model built from laser range scans collected by CMU autonomous helicopter1Computer Vision: Guiding MotionVisually guided manipulationHand-eye coordinationVisually guided locomotionrobotic vehicles

CMU NavLab IIComputer Vision: Recognition & Classification

Challenges in Object Recognition245 267 234 142 22 28 38121 156 187 98 73 32 12123 21 21 38 209 237 12199 87 59 197 216 244

Object Recognition ResearchLow Image QualityLarge Quantity of DataIntra-class Object VariationLarge number of Object ClassesAutomated LearningRobust AlgorithmsAdvanced Image Enhancement Segmentation and Hierarchical Analysis LipsFaceTextBuilding Hand Gesture VehicleClockLicense Plate Object Detection Object Detection IssuesQuality/Quantity IssuesIntra-Class Variation

Lighting Variation

Geometric Variation

Simpler Problem: ClassificationFixed size input Fixed object size, orientation, and alignmentObject is present (at fixed size and alignment)Object is NOT present(at fixed size and alignment)Decision

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Detection: Apply Classifier ExhaustivelySearch in positionSearch in scale

View-based Classifiers

FaceClassifier #1FaceClassifier #2FaceClassifier #31) Apply Local Operatorsf1(0, 1) = #3214f1(0, 0) = #5710fk(n, m) = #723

2) Look Up Probabilitiesf1(0, 1) = #3214f1(0, 0) = #5710fk(n, m) = #723

P1( #5710, 0, 0 | obj) = 0.53P1( #5710, 0, 0 | non-obj) = 0.56P1( #3214, 0, 1 | obj) = 0.57P1( #3214, 0, 1 | non-obj) = 0.48Pk( #723, n, m | obj) = 0.83Pk( #723, n, m | non-obj) = 0.193) Make DecisionP1( #5710, 0, 0 | obj) = 0.53P1( #5710, 0, 0 | non-obj) = 0.56P1( #3214, 0, 1 | obj) = 0.57P1( #3214, 0, 1 | non-obj) = 0.48Pk( #723, n, m | obj) = 0.83Pk( #723, n, m | non-obj) = 0.190.53 * 0.57 * . . . * 0.830.56 * 0.48 * . . . * 0.19> lTwo Classifiers Trained for Faces

Eight Classifiers Trained for Cars

Probabilities Estimated Off-Linef1(0, 0) = #567H1(#567, 0, 0) = H1(567, 0, 0) + 1

fk(n, m) = #350Hk(#350, 0, 0) = Hk(#350, 0, 0) + 1P1(#567, 0, 0) =S H1(#i, 0, 0)H1(#567, 0, 0)Pk(#350, 0, 0) =S Hk(#i, 0, 0)Hk(#350, 0, 0)Training ClassifiersCars: 300-500 images per viewpointFaces: 2,000 images per viewpoint~1,000 synthetic variations of each original imagebackground scenery, orientation, position, frequency2000 non-object imagesSamples selected by bootstrappingMinimization of classification error on training setAdaBoost algorithm (Freund & Shapire 97, Shapire & Singer 99) Iterative methodDetermines weights for samples18

Web-based Demo of Face Detectorhttp://www.vasc.ri.cmu.edu/cgi-bin/demos/findface.cgi21