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These are the Lecture Slides of Introduction to Computer Version which includes Machine Learning, Framework, Prediction Function, Feature Representation, Image, Desired Output, Prediction Function, Prediction Error, Predicted Value etc. Key important points are: Recognition, Overview, History, Visual Object Categories, Objects, Animals, Plants, Inanimate, Natural, Camera
Typology: Lecture notes
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Biederman 1987Docsity.com
ANIMALS PLANTS INANIMATE NATURAL MAN-MADE ….. VERTEBRATE MAMMALS BIRDS TAPIR BOAR GROUSE CAMERA Docsity.com
Specific recognition tasks Svetlana LazebnikDocsity.com
Image annotation / tagging / attributes
Object detection
Scene understanding? Svetlana LazebnikDocsity.com
Variability: (^) Camera position Illumination Shape parameters Within-class variations?
Svetlana LazebnikDocsity.com
Variability: Camera position Illumination Alignment Roberts (1965); Lowe (1987); Faugeras & Hebert (1986); Grimson & Lozano-Perez (1986); Huttenlocher & Ullman (1987) Shape: assumed known Svetlana LazebnikDocsity.com
Recognition as an alignment problem: Block world J. Mundy, Object Recognition in the Geometric Era: a Retrospective, 2006 L. G. Roberts, Machine Perception of Three Dimensional Solids, Ph.D. thesis, MIT Department of Electrical Engineering,
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Alignment: Huttenlocher & Ullman (1987) Docsity.com
Primitives (geons) Objects http://en.wikipedia.org/wiki/Recognition_by_Components_Theory Biederman (1987) Svetlana LazebnikDocsity.com
Zisserman et al. (1995) Generalized cylinders Ponce et al. (1989) Forsyth (2000)
Svetlana LazebnikDocsity.com