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Abstract: Appearance-based object recognition systems rely on training from imagery, which allows the recognition of objects without requiring a 3D geometric model. It has been little explored whether such systems can be trained from imagery that is unlabeled, and whether they can be trained from imagery that is not trivially segmentable. (Update)
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BibTeX entry: (Update)
A. Selinger and R. Nelson. Minimally Supervised Acquisition of 3D Recognition Models from Images. TR 724, Computer Science Dept., U. Rochester, 2000. http://citeseer.ist.psu.edu/article/selinger01minimally.html More
@techreport{ selinger00minimally,
author = "Andrea Selinger and Randal C. Nelson",
title = "Minimally Supervised Acquisition of 3{D} Recognition Models from Images",
number = "TR724",
year = "2000",
url = "citeseer.ist.psu.edu/article/selinger01minimally.html" }
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333 Visual learning and recognition of 3-d objects from appearan.. (context) - Murase, Nayar - 1995
166 A network that learns to recognize three-dimensional objects (context) - Poggio, Edelman - 1990
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17 A perceptual grouping hierarchy for appearance-based 3d obje.. - Selinger, Nelson - 1999
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