NDLI: Unsupervised thresholds for shape matching

Content Provider	IEEE Xplore Digital Library
Author	Muse, P. Sur, F. Cao, F. Gousseau, Y.
Copyright Year	2003
Description	Author affiliation: ENS de Cachan, France (Muse, P.; Sur, F.)
Abstract	Shape recognition systems usually order a fixed number of best matches to each query, but do not address or answer the two following questions: Is a query shape in a given database? How can we be sure that a match is correct? This communication deals with these two key points. A database being given, with each shape S and each distance /spl delta/, we associate its number of false alarms NFA(S, /spl delta/), namely the expectation of the number of shapes at distance /spl delta/ in the database. Assume that NFA(S, /spl delta/) is very small with respect to 1, and that a shape S' is found at distance /spl delta/ from S in the database. This match could not occur just by chance and is therefore a meaningful detection. Its explanation is usually the common origin of both shapes. Experimental evidence will show that NFA(S, /spl delta/) can be predicted accurately.
Sponsorship	IEEE Signal Process. Soc
File Size	314410
File Format	PDF
ISBN	0780377508
ISSN	15224880
DOI	10.1109/ICIP.2003.1246763
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-09-14
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Shape Image databases Spatial databases Feature extraction Computer vision Voting Image coding Testing Event detection Image edge detection
Content Type	Text
Resource Type	Article

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