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Nonparametric Density Estimation and Discrimination from Images of Shapes
Author(s) -
Wright David,
Stander Julian,
Nicolaides Kypros
Publication year - 1997
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.1111/1467-9876.00075
Subject(s) - smoothing , nonparametric statistics , density estimation , pixel , artificial intelligence , pattern recognition (psychology) , computer science , kernel density estimation , basis (linear algebra) , binary number , mathematics , statistics , computer vision , estimator , geometry , arithmetic
Nonparametric density estimation is the basis for a new methodology for discrimination using shape data in the form of pixel images. Our work is driven by an application based on screening for neural tube defects from ultrasonography data that comprise binary pixel images of head shapes from human fetuses. We discuss the choice of smoothing parameters used for the density estimates, the variation that is inherent in our method and how our approach could be extended to take into account other discriminatory information. We compare our method based on density estimates with alternative approaches such as those based on Fourier descriptors.