NDLI: Quantitative analysis of the viewpoint consistency constraint in model-based vision

Content Provider	IEEE Xplore Digital Library
Author	Du, L. Sullivan, G.D. Baker, K.D.
Copyright Year	1993
Description	Author affiliation: Dept. of Electron. & Electr. Eng., Surrey Univ., Guildford, UK (Du, L.)
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.<>
Starting Page	632
Ending Page	639
File Size	833244
Page Count	8
File Format	PDF
ISBN	0818638702
DOI	10.1109/ICCV.1993.378152
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1993-05-11
Publisher Place	Germany
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image edge detection Feature extraction Image analysis Object detection Detectors Computer science Computer errors Algorithm design and analysis Object recognition Machine vision
Content Type	Text
Resource Type	Article

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