Unsupervised learning of models for object recognition
A method is presented to learn object class models from unlabeled and unsegmented cluttered scenes for the purpose of visual object recognition. The variability across a class of objects is modeled in a principled way, treating objects as flexible constellations of rigid parts (features). Variabilit...
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| Format: | Others |
| Language: | en |
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2000
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| Online Access: | https://thesis.library.caltech.edu/6095/1/Weber_r_2000.pdf Weber, Markus (2000) Unsupervised learning of models for object recognition. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/ec32-c786. https://resolver.caltech.edu/CaltechTHESIS:10052010-115540388 <https://resolver.caltech.edu/CaltechTHESIS:10052010-115540388> |
Internet
https://thesis.library.caltech.edu/6095/1/Weber_r_2000.pdfWeber, Markus (2000) Unsupervised learning of models for object recognition. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/ec32-c786. https://resolver.caltech.edu/CaltechTHESIS:10052010-115540388 <https://resolver.caltech.edu/CaltechTHESIS:10052010-115540388>