Título:
Unsupervised thresholds for shape matching
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Autor:
Musé, Pablo
Sur, Frederic
Cao, Frederic
Gousseau, Yann
Sur, Frederic
Cao, Frederic
Gousseau, Yann
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Tipo de documento:
Preprint
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Palabras clave:
Visual databases
Image matching
Computer vision
Image matching
Computer vision
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Año de publicación:
2003
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Resumen:
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.
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Citación:
Musé, P, Sur, Frederic, Cao, Frederic, Gousseau, Yann. Unsupervised thresholds for shape matching [Preprint] Publicado en International Conference on Image Processing, 2003. Proceedings. Doi 10.1109/ICIP.2003.1246763
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Licencia:
Licencia Creative Commons Atribución – No Comercial – Sin Derivadas (CC - By-NC-ND)
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| Ficheros | Descripción | Tamaño | Formato | ||
|---|---|---|---|---|---|
| unsupervised-tresholds.pdf | — | 356.25 KB | Adobe PDF |