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Titolo:
NONPARAMETRIC DENSITY-ESTIMATION AND DISCRIMINATION FROM IMAGES OF SHAPES
Autore:
WRIGHT D; STANDER J; NICOLAIDES K;
Indirizzi:
UNIV PLYMOUTH,SCH MATH & STAT,DRAKE CIRCUS PLYMOUTH PL4 8AA DEVON ENGLAND UNIV PLYMOUTH,SCH MATH & STAT PLYMOUTH PL4 8AA DEVON ENGLAND UNIV LONDON KINGS COLL HOSP LONDON ENGLAND
Titolo Testata:
Applied Statistics
fascicolo: 3, volume: 46, anno: 1997,
pagine: 365 - 380
SICI:
0035-9254(1997)46:3<365:NDADFI>2.0.ZU;2-A
Fonte:
ISI
Lingua:
ENG
Keywords:
DENSITY ESTIMATES ON PIXEL IMAGES OF SHAPE; FOURIER DESCRIPTORS; MEDICAL STATISTICS; PRENATAL SCREENING; RECEIVER-OPERATOR CHARACTERISTIC CURVES; ULTRASONOGRAPHY;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
CompuMath Citation Index
Science Citation Index Expanded
Citazioni:
22
Recensione:
Indirizzi per estratti:
Citazione:
D. Wright et al., "NONPARAMETRIC DENSITY-ESTIMATION AND DISCRIMINATION FROM IMAGES OF SHAPES", Applied Statistics, 46(3), 1997, pp. 365-380

Abstract

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 inour 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.

ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 26/10/20 alle ore 09:01:10