P. Musé, F. Sur, F. Cao, Y. Gousseau. Unsupervised thresholds for shape matching. In IEEE Int. Conf. on Image Processing, ICIP 2003, Barcelona, Spain, September 2003.
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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 d we associate its number of false alarms NFA(S, d ), namely the expectation of the number of shapes at distance d in the database. Assume that NFA(S,d) is very small with respect to 1, and that a shape S' is found at distance d 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, d ) can be predicted accurately
@InProceedings{Muse03a,
Author = {Musé, P. and Sur, F. and Cao, F. and Gousseau, Y.},
Title = {Unsupervised thresholds for shape matching},
BookTitle = {IEEE Int. Conf. on Image Processing, ICIP 2003},
Address = {Barcelona, Spain},
Month = {September},
Year = {2003}
}
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