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Learning Three-Dimensional Shape Models for Sketch Recognition

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dc.creator Kaelbling, Leslie P.
dc.creator Lozano-Pérez, Tomás
dc.date 2004-12-13T06:56:19Z
dc.date 2004-12-13T06:56:19Z
dc.date 2005-01
dc.date.accessioned 2013-10-09T02:49:28Z
dc.date.available 2013-10-09T02:49:28Z
dc.date.issued 2013-10-09
dc.identifier http://hdl.handle.net/1721.1/7424
dc.identifier.uri http://koha.mediu.edu.my:8181/xmlui/handle/1721
dc.description Artifacts made by humans, such as items of furniture and houses, exhibit an enormous amount of variability in shape. In this paper, we concentrate on models of the shapes of objects that are made up of fixed collections of sub-parts whose dimensions and spatial arrangement exhibit variation. Our goals are: to learn these models from data and to use them for recognition. Our emphasis is on learning and recognition from three-dimensional data, to test the basic shape-modeling methodology. In this paper we also demonstrate how to use models learned in three dimensions for recognition of two-dimensional sketches of objects.
dc.description Singapore-MIT Alliance (SMA)
dc.format 408469 bytes
dc.format application/pdf
dc.language en
dc.relation Computer Science (CS);
dc.subject sketch recognition
dc.subject object recognition
dc.subject computer vision
dc.title Learning Three-Dimensional Shape Models for Sketch Recognition
dc.type Article

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