prikaz prve stranice dokumenta Initial Results of Multilevel Principal Components Analysis of Facial Shape
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Initial Results of Multilevel Principal Components Analysis of Facial Shape
U: Medical Image Understanding and Analysis. MIUA 2017. Communications in Computer and Information Science. Springer, 2017. str. 674-685. urn:nbn:hr:184:289568
Farnell, Damian; Galloway, Jennifer; Zhurov, Alexei; Richmond, Stephen; Perttiniemi, Pertti; Katić, Višnja

Institucijski repozitorij: Repozitorij Medicinskog fakulteta u Rijeci

Citirajte ovaj rad

Farnell, D., Galloway, J., Zhurov, A., Richmond, S., Perttiniemi, P. i Katić, V. (2017). Initial Results of Multilevel Principal Components Analysis of Facial Shape. Medical Image Understanding and Analysis. MIUA 2017. Communications in Computer and Information Science (str. 674-685). Springer. Preuzeto s https://urn.nsk.hr/urn:nbn:hr:184:289568

Farnell, Damian, et al. "Initial Results of Multilevel Principal Components Analysis of Facial Shape." Medical Image Understanding and Analysis. MIUA 2017. Communications in Computer and Information Science, Springer, 2017, str. 674-685. https://urn.nsk.hr/urn:nbn:hr:184:289568

Farnell, Damian, Jennifer Galloway, Alexei Zhurov, Stephen Richmond, Pertti Perttiniemi i Višnja Katić. "Initial Results of Multilevel Principal Components Analysis of Facial Shape." U Medical Image Understanding and Analysis. MIUA 2017. Communications in Computer and Information Science, 674-685. Springer, 2017. Citirano 18.06.2024.. https://urn.nsk.hr/urn:nbn:hr:184:289568

Farnell, D., et al. (2017) 'Initial Results of Multilevel Principal Components Analysis of Facial Shape', Medical Image Understanding and Analysis. MIUA 2017. Communications in Computer and Information Science, Springer, str. 674-685. Preuzeto s: https://urn.nsk.hr/urn:nbn:hr:184:289568 (Datum pristupa: 18.06.2024.)

Farnell D, Galloway J, Zhurov A, Richmond S, Perttiniemi P, Katić V. Initial Results of Multilevel Principal Components Analysis of Facial Shape. Medical Image Understanding and Analysis. MIUA 2017. Communications in Computer and Information Science. Springer; 2017. Str. 674-685. [pristupljeno 18.06.2024.] Dostupno na: https://urn.nsk.hr/urn:nbn:hr:184:289568

D. Farnell, J. Galloway, A. Zhurov, S. Richmond, P. Perttiniemi i V. Katić, "Initial Results of Multilevel Principal Components Analysis of Facial Shape", Medical Image Understanding and Analysis. MIUA 2017. Communications in Computer and Information Science Springer, 2017. [Online] Dostupno na: https://urn.nsk.hr/urn:nbn:hr:184:289568 [Citirano: 18.06.2024.]

Za citiranje koristite ovu mrežnu adresu: https://urn.nsk.hr/urn:nbn:hr:184:289568
Podaci o radu
Naslov (engleski)Initial Results of Multilevel Principal Components Analysis of Facial Shape
AutorDamian Farnell
AutorJennifer Galloway
AutorAlexei Zhurov
AutorStephen Richmond
AutorPertti Perttiniemi
AutorVišnja Katić
Autorova ustanovaSveučilište u Rijeci
Medicinski fakultet
Znanstveno / umjetničko
područje, polje i grana		BIOMEDICINA I ZDRAVSTVO
Dentalna medicina
Znanstveno / umjetničko
područje, polje i grana		PRIRODNE ZNANOSTI
Matematika
Znanstveno / umjetničko
područje, polje i grana		TEHNIČKE ZNANOSTI
Računarstvo
Sažetak (engleski)
Traditionally, active shape models (ASMs) do not make a distinction between groups in the subject population and they rely on methods such as (single-level) principal components analysis (PCA). Multilevel principal components analysis (mPCA) allows one to model between- group effects and within-group effects explicitly. Three dimensional (3D) laser scans were taken from 250 subjects (38 Croatian female, 35 Croatian male, 40 English female, 40 English male, 23 Welsh female, 27 Welsh male, 23 Finnish female, and 24 Finnish male) and 21 landmark points were created subsequently for each scan. After Procrustes transformation, eigenvalues from mPCA and from single-level PCA based on these points were examined. mPCA indicated that the first two eigenvalues of largest magnitude related to within-groups components, but that the next eigenvalue of largest magnitude related to between-groups components. Eigenvalues from single-level PCA always had a larger magnitude than either within-group or between-group eigenvectors at equivalent eigenvalue number. An examination of the first mode of variation indicated possible mixing of between-group and within-group effects in single-level PCA. Component scores for mPCA indicated clustering with country and gender for the between-groups components (as expected), but not for the within-group terms (also as expected). Clustering of component scores for single-level PCA was harder to resolve. In conclusion, mPCA is viable method of forming shape models that offers distinct advantages over single-level PCA when groups occur naturally in the subject population.
Ključne riječi (engleski)
Jezikengleski
Vrsta publikacijeZnanstveni rad - Izvorni znanstveni rad
Status objaveObjavljen
Vrsta recenzijeRecenziran
Verzija publikacijeObjavljena verzija rada (izdavačev PDF)
Naslov zbornikaMedical Image Understanding and Analysis. MIUA 2017. Communications in Computer and Information Science
Brojčani podacistr. 674-685
URN:NBNurn:nbn:hr:184:289568
Datum objave publikacije2017
SkupNaziv: Medical Image Understanding and Analysis
Kratica: MIUA
Lokacija: Edinburgh, Ujedinjena Kraljevina
Organizator: Sinapse
Datum početka: 2017-07-11
Datum završetka: 2017-07-13
Vrsta izlaganja: Postersko izlaganje
URL dokumentahttp://bib.irb.hr/882165
Vrsta resursaTekst
IzdavačSpringer
Prava pristupaZatvoreni pristup
Uvjeti korištenjaIkona licence Zaštićeno autorskim pravom.
Datum i vrijeme pohrane2022-02-23 15:14:27