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• W2135563376 abstract "The authors present a quantitative analysis of the viewpoint consistency constraint (VCC), which is the fundamental principle behind model-based methods for recognizing 3-D objects from 2-D data. It defines a measure of viewpoint consistency error (VCE), based on a formal model of image feature errors. Existing methods for establishing feature correspondences using the VCC are discussed. The poor performance of incremental methods is demonstrated and attributed to the failure to ensure that global consistency improves during search. A more reliable method, viewpoint consistency ascent, which uses the VCE explicitly as a heuristic for a state-space search, is presented. The two algorithms are compared in an experimental study. The approach to quantitative analysis of alternative algorithms is illustrated, which may be applied to model based object recognition more generally. >" @default.
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• W2135563376 date "2002-12-30" @default.
• W2135563376 modified "2023-09-25" @default.
• W2135563376 title "Quantitative analysis of the viewpoint consistency constraint in model-based vision" @default.
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• W2135563376 doi "https://doi.org/10.1109/iccv.1993.378152" @default.
• W2135563376 hasPublicationYear "2002" @default.
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